#  Performance Analysis of Machine Learning Centered Workload Prediction
Models for Cloud

Deepika Saxena, Jitendra Kumar, Ashutosh Kumar Singh, , and Stefan Schmid D.
Saxena is with Department of Computer Science, Goethe University Frankfurt,
Germany. E-mail<EMAIL_ADDRESS><EMAIL_ADDRESS>
J.Kumar is with the Department of Computer Applications, NIT Tiruchirappalli,
Tamilnadu, India. E-mail<EMAIL_ADDRESS>
A. K. Singh are with the Department of Computer Applications, NIT Kurukshetra.
E-mail<EMAIL_ADDRESS>
S. Schmid is with TU Berlin, Germany, University of Vienna, Austria, and
Fraunhofer SIT, Germany. E-mail<EMAIL_ADDRESS>
###### Abstract

The precise estimation of resource usage is a complex and challenging issue
due to the high variability and dimensionality of heterogeneous service types
and dynamic workloads. Over the last few years, the prediction of resource
usage and traffic has received ample attention from the research community.
Many machine learning-based workload forecasting models have been developed by
exploiting their computational power and learning capabilities. This paper
presents the first systematic survey cum performance analysis-based
comparative study of diversified machine learning-driven cloud workload
prediction models. The discussion initiates with the significance of
predictive resource management followed by a schematic description,
operational design, motivation, and challenges concerning these workload
prediction models. Classification and taxonomy of different prediction
approaches into five distinct categories are presented focusing on the
theoretical concepts and mathematical functioning of the existing state-of-
the-art workload prediction methods. The most prominent prediction approaches
belonging to a distinct class of machine learning models are thoroughly
surveyed and compared. All five classified machine learning-based workload
prediction models are implemented on a common platform for systematic
investigation and comparison using three distinct benchmark cloud workload
traces via experimental analysis. The essential key performance indicators of
state-of-the-art approaches are evaluated for comparison and the paper is
concluded by discussing the trade-offs and notable remarks.

###### Index Terms:

Cloud Computing, Deep Learning, Quantum Neural Network, Ensemble Learning,
Hybrid Learning, Evolutionary Neural Network, Forecasting.

##  1 Introduction

The Cloud Computing (CC) paradigm empowered with rapid elasticity, resource
pooling, outsourced service management, broad network access, and pay-as-per-
use model, facilitates scalable computing avenues with minimum upfront capital
investment to enterprises, academia, research and all the stakeholders [1, 2].
CC is acting as a catalyst in driving business progress amidst growing
uncertainty across the geographical boundaries by sustaining momentum and
addressing the inconsistencies in global IT infrastructures [3]. According to
a recent survey report [4], it is anticipated that the global cloud computing
market will reach USD 1,554.94 billion by 2030, registering a Compound Annual
Growth Rate (CAGR) of 15.7%. Moreover, all the emerging technologies including
Internet of Things (IoT), fog and edge computing, cyber-physical systems etc.
emphatically depend on CC, because of their insufficiency of storage and
computing capabilities [5].

###  1.1 Motivation

Cloud Service Providers (CSP) employ virtualization [6, 7, 8, 9, 10] of
physical resources at datacentres to maximize their revenue while serving the
demand of computing instances with privilege of rapid scalability [11, 12,
13]. Therefore, the comphrehensive management of CC infrastructure entirely
depends on the fine-grained provisioning of resources including storage,
processing and networking etc. [14, 15, 16, 17, 18]. The resource demands
exhibit high variation over the time expediting over/under-utilization of
physical machines, and Service Level Agreement (SLA) violation issues [19].
During peak load arrival, the aggregate demand of VM resources exceeds the
available resource capacity of the servers leading to overloaded servers and
performance degradation, for example, some VMs may crash, longer
unavailability of resources and increased response time, etc. Whereas
inadequate resource demands lead to the wastage of computational resources. In
order to manage the dynamic and random requirement of resource capacities or
handle over-/under-load, the migration of VMs in real time from an
over-/under-loaded server to another server having sufficient resource
capacity, leads to delayed execution. In this context, effective handling of
incoming workloads via prior estimation is a prime requirement. An accurate
prediction of load triggers reduction of resource wastage, minimum power
consumption, and number of active servers by allowing only the required number
of physical machines in active state. The precise information of workload
imparts prior reservation of resources to execute and manage the forthcoming
workload effectively, reduce response time, SLA violations, over-provisioning,
and under-provisioning problems, and improve resource utilization,
reliability, service availability. [20, 21, 22, 23].

###  1.2 Workload Prediction Perspective

The perspective and utility of workload prediction for physical resource
management is illustrated in Fig. 1 via interactive information flow between
the ‘cloud infrastructure’ and ‘workload prediction’ blocks. The cluster of
servers {$ServerP_{1}$, $ServerP_{2}$, …, $ServerP_{n}$} ingrained with
virtualization technology enable numerous virtual instances in the form of
VMs, to cater services demands of cloud users. The virtualization allows
sharing of physical machines among various applications (such as AWS, Docker,
MS Azure, and GCP etc.) with the help of dispatcher and hypervisor.

Figure 1: Schematic representation and application of Workload Prediction

The resource usage information is monitored and recorded in a workload
repository within workload prediction and management unit. The raw information
including number and type of requests; number, type, and cost of VMs; resource
(viz., CPU, memory, bandwidth) usage, is retrieved from the repository and
transferred to workload prediction unit. The significant attributes from raw
data samples are extracted, aggregated, and normalized during data pre-
processing. A workload prediction model is employed which generates and
evolves over series of stages such as training, validation, and testing for
the real-time workload prediction. The final prediction model analyzes and
estimates information regarding resource usage, number and type of requests,
etc. for rendering effective resource management decisions. The predicted
resource information assists in assorting the needed physical resources
proactively avoiding the run-time resource provisioning delay while satisfying
Quality of Services (QoS) constraints.

###  1.3 Research Challenges

Assuredly, the cloud workload prediction plays an essential role in proactive
auto-scaling and dynamic management of resources resulting into increased
scalability and throughput of the systems, sustainability, fault-tolerance via
proactive prediction of system failures. However, there exists some major
challenges addressing the cloud workload prediction which are discussed as
follows:

  * •

Heterogeneous Workload: Cloud users submit different type of application
requests, requiring heterogeneous resource capacities with varying priorities
and pricing policies associated with their respective SLAs.

  * •

Uncertain Resource Demands: The resource demand changes over time in an hour,
day, week, month and years with respect to the type of workload and deadline
of execution submitted by the user. Sometimes the traffic becomes bursty [24]
which makes it difficult to estimate the upcoming resource demands and decide
resource distribution.

  * •

Dynamic Adaptation: Since the cloud environment is highly variable and
dynamic, it suffers from unexpected fluctuations, which put forth a crucial
challenge of adaptability for workload prediction i.e., to adapt or re-
generate in order to sustain and perform efficiently with the changing
workloads.

  * •

Data granularity and Prediction window-size: To decide the appropriate size of
data sample or granule and length of prediction window i.e., for shorter or
longer interval, is another critical challenge which directly effects learning
of relevant patterns and developing correlations among extracted patterns.

###  1.4 Paper Outline and Contributions

This paper presents a comprehensive study of machine learning based cloud
workload prediction models. The study begins in Section 1 with a discussion of
the CC and the vital role and research motivation for the workload prediction
within CC environment. It is followed by a schematic representation with an
illustrative description of the application of the load prediction and
management in CDCs. Thenafter, research challenges depicting a commendable
points of the need and efficacious impact of an accurate workload prediction
for resource management and intervening critical issues are discussed. The
operational flow outlining the essential steps of workload prediction are
rendered in Section 2. The intended research methodology is discussed in
Section 3. This study aims to provide an extensive review of the most
prominent and seminal machine learning based models proposed for the
prediction of extensive range of cloud workloads. Accordingly, Section 4
discusses Evolutionary Neural Network based prediction models, Section 5 and
Section 6 entail review of Deep Learning and Hybrid learning based prediction
models, respectively while Section 7 and Section 8 pertain to discuss Ensemble
learning and Quantum learning based prediction models, respectively.
Furthermore, the prediction models underlining the considered five categories
are implemented on the common platform for evaluation and comparison of their
performance in terms of various key performance indicators (KPI)s in Section
9. Finally, Section 10 concludes the study with a discussion of trade-offs
among the prediction models of different classes are remarked with emerging
research challenges addressing cloud workload forecasting along with their
probable solution avenues are discussed. To the best of the authors’
knowledge, this is the first paper which aims to carry out a comprehensive
experimental study on the machine learning based workload prediction models in
the context of resource management in CC. The key contributions of this paper
are:

  * •

The commendable and recent cloud workload prediction models based on the
machine-learning algorithms are designated with respect to their conceptual
and operational characteristics into a classification and taxonomical
organization (Fig. 3).

  * •

To illustrate the generalized conceptual and operational design corresponding
to the workload prediction approach belonging to each category, this paper
figures out five specific machine learning model architectures and their
working strategies.

  * •

A critical discussion and comparison cosidering all the essential detail of
state-of-the-art works are provided and their features are analyzed to
determine the future research scope addressing the limitation of the
respective class based prediction model.

  * •

An implementation of the approaches associated to each of the five classes
based prediction models on the same platorm is conducted for in-depth
experimental analysis and comparison in terms of essential KPIs to measure
their performance followed by discussion of trade-offs and notable remarks.

Table I gives the explanatory terms for the symbols, notations, abbreviations
used throughout the manuscript.

TABLE I: Notations with their Explanatory Terms

Notation | Definition | Notation | Definition
---|---|---|---
$\mathcal{W}^{Ac}$ | actual workload | $\mathcal{X}$ | cell information
$\mathcal{W}^{Pr}$ | predicted workload | $\mathcal{D}$ | Input data
$\mathcal{G}_{1}$ | first layer of LSTM | $\mathcal{B}$ | bias
$\mathcal{CF}_{RU}$ | previous resource usage information | $\mathcal{WT}$ | weight matrix
$\mathcal{G}_{2}$ | sigmoid layer of LSTM | ${w}$ | neural weight
n | number of input layer nodes | $\Theta$ | qubit
p | number of nodes in hidden layer | $\mathcal{\uplus}$ | activation function
z | number of base predictor (BP) | $\textit{y}^{In}$ | qubit input vector
$\mathcal{MSE}$ | mean squared error | $\mathcal{MAE}$ | mean absolute error
m | number of data samples | ENN | evolutionary neural network
CSP | cloud service provider | CC | cloud computing
LSTM | long short term memory | CDC | cloud data centre
RNN | recursive neural network | DBN | deep belief network
Bi-LSTM | bi-directional LSTM | DNN | deep neural network
SGD | stochastic gradient descent | GRU | gated recurrent unit
PLR | piecewise linear representation | LR | logistic regression
TSA | top-sparse autoencoder | CP | canonical polyadic
OED | orthogonal experimental design | S-G Filter | savitzky-golay
PSO | particle swarm optimization | BP | back propagation
ENN | evolutionary neural network | QoS | quality of service
RMSE | root mean squared error | MAE | mean absolute error
MAPE | mean absolute percentage error | MSE | mean squared error
RMSSE | root mean segment squared error | VM | virtual machine
MRPE | mean relative predict error | DL | deep learning
CDF | cumulative distribution frequency | RMSLE | logarithmic RMSE
SaDE | Self-adaptive differential evolution | AEF | absolute error frequency
BaDE | bi-phase adaptive differential evolution | EQNN | evolutionary QNN
TaDE | Tri-adaptive differential evolution | C-NOT | controlled NOT gate
ARIMA | auto regressive integrated moving average | HL | hybrid learning
QNN | quantum neural network | EL | ensemble learning

##  2  Workload Prediction Operational Flow

The essential steps intended for the workload prediction are outlined via
illustration of an operational design in Fig. 2. Consider input data
{$\mathcal{D}_{1}$, $\mathcal{D}_{2}$, …, $\mathcal{D}_{n}$} $\in\mathcal{D}$
is extracted from the raw data stored in the workload repository.

Figure 2: Load prediction operational flow

The data extraction filters relevant attributes from raw data to improve the
pattern learning and developing more intuitive correlations among extracted
patterns. The data aggregation operation is performed in which the extracted
data is assembled as per the chosen prediction window-size (for example, five
minutes) such as {$\mathcal{D}_{1}$, $\mathcal{D}_{2}$, $\mathcal{D}_{3}$}
combines to produce an aggregated data sample $\mathcal{D}^{\ast}_{1}$.
Similarly, {$\mathcal{D}_{a}$, $\mathcal{D}_{a+1}$, $\mathcal{D}_{a+2}$} and
{$\mathcal{D}_{n-2}$, $\mathcal{D}_{n-1}$, $\mathcal{D}_{n}$} aggregate to
generate data samples $\mathcal{D}^{\ast}_{a}$ and $\mathcal{D}^{\ast}_{n}$,
respectively. The aggregated data samples are scaled in a specific range
[$x_{a}$, $x_{b}$] and transformed into a normalized data samples
{$\mathcal{D}^{N}_{1}$, $\mathcal{D}^{N}_{2}$, …, $\mathcal{D}^{N}_{n}$} using
Eq. (1), where $\varpi^{In}_{min}$ and $\varpi^{In}_{max}$ are the minimum and
maximum values of the input data set, respectively and the normalized data
vector is denoted as $\hat{\varpi^{In}}$, which is a set of all normalized
input data values. The values of $x_{a}$ and $x_{b}$ were set equals to 0.0001
and 0.999, respectively for the experiments. These normalized values are
organized into two dimensional input and output matrices denoted as
$\varpi^{In}$ and $\varpi^{Out}$, respectively as stated in Eq. (10):

$\displaystyle\hat{\varpi^{In}}=x_{a}+\frac{d_{i}-\varpi^{In}_{min}}{\varpi^{In}_{max}-\varpi^{In}_{min}}\times(x_{b})$
(1)

$\varpi^{In}=\left[{\begin{array}[]{cccc}\varpi_{1}&\varpi_{2}&....&\varpi_{z}\\\
\varpi_{2}&\varpi_{3}&....&\varpi_{z+1}\\\ .&.&....&.\\\
\varpi_{m}&\varpi_{m+1}&....&\varpi_{z+m-1}\\\
\end{array}}\right]\varpi^{Out}=\left[{\begin{array}[]{c}\varpi_{z+1}\\\
\varpi_{z+2}\\\ .\\\ \varpi_{z+m}\end{array}}\right]$

(10)

Accordingly, prediction sliding window is prepared as shown in the block of
sliding window in Fig. 2. These normalized data samples are divided into three
categories namely, training data, validation data, and testing data. A Machine
Learning Algorithm is appointed as a prediction model which receives training
data to allow specific pattern learning during iterative learning or
optimization process. Thenafter, this prediction model is evaluated using any
error evaluation function such as RMSE or MSE, which is tested for accuracy.
If the desired accuracy is achieved, a Trained Prediction Model is obtained
and validation data is passed into it again to check for prediction accuracy.
If the optimal accuracy is achieved, a Validated Prediction Model is
established. Finally, the test/unseen data is passed to the validated
prediction model and accuracy evaluation is performed. If consecutively, the
desired accuracy is achieved, a Final Prediction Model is deployed, otherwise,
the respective stage of the prediction models reversed back to the iterative
learning process. The performance is measured as predicted output is achieved
for cloud resource management.

##  3 Research Methodology

This section elaborates the review methodology in detail. The review procedure
includes the gathering of major cloud workload prediction papers wherein the
proposed approaches are driven from machine learning algorithms and concepts.
The pioneering quality prediction models, published in top-notch journals and
conference databases such as IEEE, ACM Digital Library, Elsevier, Springer,
Wiley are searched, studied and analysed for comparative study. Furthermore,
the collected papers are refined by the identification of primary studies
based on underlying proposed approaches, then application of a specific
inclusion criteria for grouping the paper based on similar approaches or
having overlapping features into a common class/category. To avoid any
biasness during research, the review process in the remaining sections is
developed by one of the authors, and finalized by the other co-author via
discussions, and iterative review methods. The existing prediction models are
thoroughly explored by distinct authors to ensure the completeness of the
proposed study and inter-performance comparison. While selecting related work
corresponding to each category, the average and below average research work
are filtered and avoided so as to present a clear and concise review of the
best of the existing workload prediction approaches. Accordingly, a
classification and taxonomical representation is presented in Fig. 3 which
designates the existing prediction models into specific classes and sub-
classes of the supervised machine learning approaches based on their
conceptual and operational characteristics.

Cloud Workload Prediction Models Machine Learning ApproachesEvolutionary
LearningDeep LearningHybrid LearningEnsemble LearningQuantum LearningANN+SADE
[25]ANN-BADE [26]ANN-BHO [27]SDWF [27]FLGAPSONN [28]LSTMAutoencoderDeep
BeliefNetworkDeep NeuralNetworkLSTM [29]2D LSTM [30]Bi-LSTM [31] CrystalILP
[32]FEMT-LSTM [33]Encoder+LSTM [34]CP Autoen-coder [35]LPAW Autoe-ncoder
[36]GRUED[37]DBN+RBN [38]DBN+OED [39]DP-CUPA [40]es-DNN[41]DNN+MVM [42]DNN-PPE
[43]SG-LSTM [44]ADRL [45]Bi-Hyp-rec [46]BG-LSTM [47]HPF-DNN
[48]FAHP[28]ACPS[49]LSRU [50]KSE+WMC[51]FAST [52]SGW-S [19]ClIn [53]AMS
[54]E-ELM[55]SF-Cluster[48]EQNN[56]

Figure 3: Classification and Taxonomy of Machine learning based Workload
Prediction Models

Correspondingly, the five exclusive workload prediction classes are designated
as Evolutionary Learning, Deep Learning, Hybrid Learning, Ensemble Learning,
and Quantum Learning. In Evolutionary learning class, the candidate
approaches: ANN+SADE [25], ANN-BADE [26], ANN-BHO [27], SDWF [27], and
FLGAPSONN [28] have applied evolutionary optimization alogorithms for the
learning process or weight update process of neural network layers. The ample
of works subject to Deep learning are further differentiated into four sub-
classes including Long Short-Term Memory (LSTM) cell, Autoencoder, Deep Belief
Network, and Deep Neural Network. The sub-class LSTM includes LSTM [29], 2D
LSTM [30], Bi-LSTM [31], Crystal ILP [32], and FEMT-LSTM [33] which are pre-
dominantly based on functionality of LSTM models. Autoencoder sub-category
consists of Encoder+LSTM [34], CP Autoencoder [35], LPAW Autoencoder [36], and
GRUED [37] are derived by applying some useful modification in the traditional
autoencoders. Similarly, DBN+RBN [38], DBN+OED [39], DP-CUPA [40]; and es-DNN
[41], DNN+MVM [42], DNN-PPE [43], SG-LSTM [44] are located with sub-class Deep
Belief Network and sub-class Deep Neural Network, respectively. The Hybrid
learning class represents integration of several machine learning algorithms
and methods which encompasses ADRL [45], Bi-Hyprec [46], BG-LSTM [47], HPF-DNN
[48], FAHP [28], ACPS [49], and LSRU [50]. Likewise, Ensemble learning
involves concept of base-learners and decision making to estimate the final
outcome. KSE+WMC [51], FAST [52], SGW-S [19], ClIn [53], AMS [54], E-ELM [55],
and SF-Cluster [48] works enfold Ensemble learning. Finally, the Quantum
learning class comprises EQNN model [56] which is developed using the
principles of quantum computing and neural network learning.

##  4 Evolutionary Neural Network based Prediction Models

The neural networks in which the learning process is achieved with the help of
an evolutionary optimization algorithm are designated as Evolutionary Neural
Networks (ENN)s. Fig. 4 depicts a schematic and operational view of an ENN.
Consider a feed-forward neural network $n$-$p_{1}$-$p_{2}$-$q$ comprising of
$n$, $q$ nodes in input and output layers, and $p_{1}$ and $p_{2}$ nodes in
three consecutive hidden layers. A training input data vector:
{$\mathcal{D}_{1}$, $\mathcal{D}_{2}$, …, $\mathcal{D}_{n}$} $\in\mathcal{D}$
is passed to the input layer of this neural network, wherein each node of one
layer is connected to all the nodes of the consecutive layer with the help of
synaptic/neural weights {$w_{1}$, $w_{2}$, …, $w_{z}$} $\in\mathcal{W}$, $z$
is the total number of weight connections between any two consecutive neural
layers. The forward propagation of training input vector ($\mathcal{D}$) is
carried out via weighted connections using Eqs. (11), (12), and (13), $\uplus$
is a linear function computed at each neuron; and $\mathcal{B}$ is bias
vector. The values of weight connections {$w_{1}$, $w_{2}$, …, $w_{z}$}
determine impact of the input vector on the output vector of the neurons and
decide strength of synaptic inter-connections between neurons.

$\displaystyle\uplus=(\mathcal{D}_{1}\times w_{1})+(\mathcal{D}_{2}\times
w_{2})+...+(\mathcal{D}_{n}\times w_{n})$ (11)
$\displaystyle\mathcal{D}^{\dagger}\cdot{\mathcal{W}^{\dagger}}=(\mathcal{D}_{1}\times
w_{1})+(\mathcal{D}_{2}\times w_{2})+...+(\mathcal{D}_{n}\times w_{n})$ (12)
$\displaystyle\uplus=\mathcal{D}^{\dagger}\cdot{\mathcal{W}^{\dagger}}+\mathcal{B}$
(13)

The ENN prediction model learns by adjusting the values of bias and inter-
connection weights {$w_{1}$, $w_{2}$, …, $w_{n}$} $\in\mathcal{W}^{\dagger}$
of ENN with the aim of minimizing the prediction error. This learning process
is achieved by applying different evolutionary optimization algorithm which
selects the most optimal network from the random population of $\mathcal{Z}$
networks {$w^{\dagger}_{1}$, $w^{\dagger}_{2}$, …,
$w^{\dagger}_{\mathcal{Z}}$}. The algorithm repeatedly optimizes the values of
neural weight connections by updating the population of networks exploring and
exploiting the diverse population of networks extensively. During successive
epoch, the best network candidate is chosen by evaluating a fitness function
such as prediction error estimation using Root Mean Squared Error (RMSE), Mean
Absolute Percentage Error (MAPE) etc. To allow the online optimization of
ENNs, the learning process is periodically repeated with the previous and most
recent samples of workloads for their training and re-training in an off-line
mode while analysing the live workloads concurrently in the real-time. There
are several approaches proposed using ENN for cloud workload prediction and
this section further aimed at providing comprehensive discussion and analysis
of these approaches.

Figure 4:  ENN based load prediction

An ENN-based cloud workload prediction approach is proposed in [25] wherein a
three layered feed-forward neural network is trained using a Self adaptive
Differential Evolution (SaDE). During the learning process, the population of
networks is updated by applying exploration and exploitation operations using
three mutation strategies selectively followed by uniform crossover. This
approach produced improved accuracy over Backpropagation trained neural
network (BPNN) [57] because of multidimensional learning in former as compared
with optimizing single solution in later approach. Kumar et al. [26] have
proposed a Biphase adaptive Differential Evolution (BaDE) learning based
neural network that adopted a dual adaptation viz., at level of crossover
during exploitation process and mutation in exploration phase to improve the
learning efficiency of neural network. As a consequence, this work
outperformed SaDE [25] in terms of prediction accuracy. An auto-adaptive
neural network is developed in [58] wherein the network connection weights are
adjusted with Tri-adaptive Differential Evolution (TaDE) algorithm. In this
approach, the adaptation is appointed at level of crossover, mutation, and
control parameters generation level which allows enhanced learning the
prediction model. Kumar et al. [59] have used a BlackHole Optimization (BHO)
algorithm to optimize neural weights and develop a workload prediction model
for dynamic resource scaling. This evolutionary optimization algorithm updates
the movement of the stars i.e., randomly intialized network vectors and track
their position whether reaching an event horizon. Further, this work was
enhanced by modifying the existing BHO algorithm as enhanced BHO (i.e., E-BHO)
in [27] by including concepts of local and global blackhole (i.e., best
solution) during iterative learning process of the feed-forward neural
network. Also, this approach has computed the deviation in recent forecasts
and applied it to enhance the accuracy of the forthcoming predictions. Malik
et al. [28] have proposed an ENN based multi-resource utilization prediction
approach. In this work, Functional Link Neural Network (FLNN) with a hybrid
evolutionary algorithm comprising of Genetic Algorigm (GA) and Particle Swarm
Optimization (PSO) is applied for neural network weight adjustment during
learning process. It has been compared with FLNN, FLNN with GA (FLGANN), and
FLNN with PSO (FLPSONN) and validated its performance against these methods.

A pandect summary of existing ENN-based workload prediction models are
provided in Table II which highlights the major features, implementation
details, performance metrics, parameter tunings and intended computational
complexities during the learning process. The approaches discussed in [25, 26,
58] are based on Differential Evolution which needs tuning of multiple control
parameters including crossover-rate, mutation-rate, keeping track of dynamic
or fixed learning-period, maintaining records of the number of failure and
successful candidates during each epoch. While the prediction approaches
entailed in [59, 27] employing BHO keeps track of event-horizon radius only
and updates the next generation population depending on the comparison of the
fitness value of each candidate with the horizon radius. Hence, it can be
analysed that the consumption of training time and involved computational
complexity, number of training epochs are higher for DE based prediction
approaches as compared with that of BHO based prediction. Based on the
aforementioned factors, [27] is the most admissible among all the discussed
approaches.

TABLE II:  Summary and Comparison of ENN-based Prediction Approaches

Notable Contributors (Timeline) |  Model/ Approach/ Framework |  Workflow/ Strategy |  Datasets |  Implementation/ Simulation tool |  Predicted parameters |  Error metrics |  Results or Remarks
---|---|---|---|---|---|---|---
Kumar et al. [25] (2018) |  ANN-SaDE |  Feed-forward neural network is trained with self-adaptive differential evolutionary algorithm |  NASA and Saskatchewan |  MATLAB |  Number of requests per unit time |  MSE |  reduced error up to 0.001 and accuracy improved by 168 times over BP
Kumar et al. [26] (2020) |  ANN-BaDE |  three mutations stategies and three crossover strategies based adaptation within DE algorithm optimizes ANN |  NASA, Saskatchewan, Google Cluster traces |  Python |  Number of requests, CPU, memory usage |  MSE |  accuracy improved up to up to 91% and 97% over SaDE and BP, respectively
Saxena et al. [58] (2020) |  ANN-TaDE |  three mutations stategies and three crossover strategies with control parameter-tuning based adaptations within DE algorithm optimizes ANN |  NASA and Saskatchewan |  Python |  Number of requests per unit time |  RMSE |  accuracy improved by 97.4% and 94.8% over BP and SaDE, respectively
Kumar et al. [59] (2016) |  Neural Network with BlackHole Optimization |  Blackhole algorithm is utilized in the learning process of neural network provoded with pre-processed training data |  HTTP traces from NASA, Calgary and Saskatchewan web servers |  MATLAB |  Number of requests per unit time |  MSE |  error reduced upto 134 times over BP
Kumar et al. [27] (2021) |  Self directed workload forecasting method (SDWF) |  the forecasting error trend is captured by computing the deviation in recent forecasts and applied to enhance the accuracy of further predictions |  NASA and Saskatchewan HTTP traces, Google Cluster |  MATLAB |  Number of requests, CPU, memory usage |  MSE |  error up to 99.99% over compared methods
Malik et al. [28] (2022) |  FLGAPSONN: FLNN +GA+ PSO |  Pre-processed training data is passed to FLNN which is optimized with hybrid algorithm of GA and PSO |  Google Cluster traces |  Python |  CPU, memory, disk usage |  MAE |  improved accuracy by 21.87%, 13.75%, and 30.55% over FLPSONN, FLGANN, and FLNN, respectively

##  5 Deep Learning based Prediction Models

Deep Learning models are a class of prediction models which have immensely
influenced the field of cloud computing. A conceptual and operational design
of deep learning strategy is illustrated in Fig. 5, wherein, the pre-processed
training input data samples: {$\mathcal{D}_{1}$, $\mathcal{D}_{2}$, …,
$\mathcal{D}_{n}$} are passed into a deep learning algorithms such as Long
Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Deep Belief Networks
(DBN), Deep Feed-forward Neural Network (DNN), Autoencoders, Recursive Neural
Network (RNN) and so on for the learning process. The essential
hyperparameters {$Hyp1$, $Hyp2$, …, $HypN$} of the respective deep learning
algorithms are tuned and re-tuned periodically to create and update the deep
learning based prediction model. The trained model is further evaluated using
validation data to estimate its performance and accuracy. Accordingly, a deep
learning based model optimized with best or most admissible hyperparameters is
obtained.

Figure 5: Deep Learning based Prediction Operative View

For instance, LSTM based deep learning algorithm is applied for the
prediction. The resource usage of actual load
($\mathcal{Z}^{Ac}_{\mathcal{RU}}$) stored as a historical workload is fed as
input into neural network input layer for prediction of future resource usage.
LSTM-RNN based prediction model comprises of cells containing four neural
network layers, where previous cell ($\mathcal{X}^{t-1}$) information is
passed to current cell ($\mathcal{X}^{t}$). The first layer
($\mathcal{G}_{1}$) applies Eq. (14) to decide the amount of previous resource
usage information ($\mathcal{CF}_{\mathcal{RU}_{i}}^{t}$) which is tranferred
to the next state; where $\mathcal{WT}$ is weight matrix, $\mathcal{B}$ is a
bias value, $\mathcal{Z}^{Pr}_{t-1}$ and $\mathcal{Z}^{Pr}_{t}$ are previous
output and current input, respectively. The cell state is updated using two
network layers viz., $sigmoid$ layer ($\mathcal{G}_{2}$) which decides the
values to be updated ($\mathcal{I}^{t}$) using Eq. (15), and $tanh$ layer for
generation of a new candidate values vector ($\hat{\mathcal{X}}^{t}$) using
Eq. (16). Finally, Eq. (17) combines both outputs to update cell state.

$\mathcal{CF}_{\mathcal{RU}_{i}}^{t}=\mathcal{G}_{1}(\mathcal{WT}_{\mathcal{CF}}\cdotp[\mathcal{Z}^{Pr}_{t-1},\mathcal{Z}^{Ac}_{t}]+\mathcal{B}_{\mathcal{RU}})$
(14)
$\mathcal{I}^{t}=\mathcal{G}_{2}(\mathcal{WT}_{\mathcal{I}}\cdot[\mathcal{Z}^{Pr}_{t-1},\mathcal{Z}^{Ac}_{t}]+\mathcal{B}_{\mathcal{I}})$
(15)
$\hat{\mathcal{X}}^{t}={tanh}(\mathcal{WT}_{\mathcal{X}}\cdot[\mathcal{Z}^{Pr}_{t-1},\mathcal{Z}^{Ac}_{t}]+\mathcal{B}_{\mathcal{X}})$
(16)
$\mathcal{X}^{t}=\mathcal{CF}_{\mathcal{RU}_{i}}^{t}\times\mathcal{X}^{t}+\hat{\mathcal{X}}^{t-1}\times\mathcal{I}^{t}$
(17)

The RU of predicted traffic ($\mathcal{Z}^{Pr}_{t+1}$) for different VMs
hosted on a server are aggregated to determine any overload proactively and
alleviate it by migrating VMs with highest predicted RU to an efficient
server. A comprehensive survey of the existing workload prediction models
belonging to four distinct categories of deep learning approaches are
discussed in the subsequent sections.

###  5.1 LSTM-RNN

A fine-grained cloud workload prediction model using long short-term memory
based recurrent neural network (LSTM-RNN) is presented in [29] which is
capable of learning a long-term dependencies and producing a high accuracy for
host load prediction. Tang [30] has proposed a two-dimensional LSTM neural
network cell structure by utilizing a hidden layer week-based dependence and
weights parallelization algorithm. This work has improved LSTM algorithm by
providing the mathematical description of parallel LSTM algorithm and its
optimization with an error back propagation method. Its performance is
validated using the real workload of the Shanghai Supercomputer Center. Tuli
et al. [34] have proposed an automatic straggler (slow processing tasks)
prediction and mitigation method for cloud environment using an encoder LSTM
network that addressed heterogeneous host and volatile task characteristics.
The encoder analyses the resource usage and load information and passes the
information to the LSTM. Further, an exponential moving average of input
matrices is taken into account to prevent the LSTM model from diverging. A
storage workload prediction approach named CrystalLP based on LSTM neural
network is introduced in [32]. In this approach, a storage workload time-
series model is developed which collects the intended workload patterns that
helps in precise and adaptive scheduling with load balancing. Thereafter, LSTM
based workload predictor is implemented which is trained or optimized with an
algorithm composed of an integration of stochastic gradient descent (SGD)
together with the Adam optimizer. Gao et al. [31] have presented a multi-layer
Bi-directional Long Short Term Memory (Bi-LSTM) based task failure prediction
algorithm. It comprises of one input layer, two Bi-LSTM layers, one output
layer and the Logistic Regression (LR) layer to predict whether the tasks are
failed or finished. Unlike traditional LSTM which uses only forward state, Bi-
LSTM operates on both forward and backward states to allow more accurate
estimation of the weights of both closer and farther input features. Ruan et
al. [33] have established a turning point prediction model for cloud server
workload forecasting considering cloud workload features. Thenafter, a cloud
feature-enhanced deep learning model with rule-filtering based Piecewise
Linear Representation (PLR) algorithm is build for workload turning point
prediction. The performance evaluation of this model illustrated its
prediction accuracy effectiveness in terms of improvement in F1 score over
existing state-of-the-art methods.

###  5.2 Auto-encoder

An efficient canonical polyadic (CP) decomposition based deep learning model
is proposed in [35] for prediction of industrial workloads in cloud, wherein,
a CP auto-encoder is constructed by converting a basic autoencoder into tensor
space with the help of bijection. In this model, the basic auto-encoder in the
CP decomposition format is followed by the stacked autoencoder model in the CP
decomposition format. The stacked autoencoder is created to learn the relevant
features of the workload information and the CP decomposition is employed to
compress the features substantially for improving the training efficiency.
Chen et al. [36] have established a deep Learning based Prediction Algorithm
for cloud Workloads (L-PAW) which included a Top-Sparse Auto-encoder (TSA) for
an effective extraction of the essential representations of workloads. This
approach integrated GRU and recurrent neural network (RNN) to evict the long-
term memory dependencies for prediction of forthcoming cloud workloads with
enhanced accuracy.

###  5.3 Deep Belief Network

Qiu et al. [38] have proposed a Deep Belief Network (DBN) composed of
multiple-layered Restricted Boltzmann Machines (RBMs) and a regression layer
for prediction of cloud workloads. In this model, DBN extracts the high level
features from all VMs and the regression layer is used to predict the
forthcoming load on VMs. It learns significant patterns efficiently using
prior knowledge in an unsupervised manner. Zhang et al. [39] developed a DBN
approach based load prediction model which is a stacked RBM and used
Backpropagation algorithm to minimize its loss function. It incorporated
analysis of variance and Orthogonal Experimental Design (OED) techniques into
the parameter learning of DBN and have achieved a high prediction accuracy
over ARIMA. Also, a similar DBN-based approach is presented in [60] which can
capture high variances in cloud metric data without handcrafting specified
feature for short term resource demands and long-term load prediction. Wen et
al. [40] have presented a DBN and Particle Swarm Optimization (PSO) based CPU
usage prediction algorithm named as DP-CUPA. This algorithm includes three
main steps: pre-processing of training data samples; adoption of
autoregressive and grey models as base prediction models; and training of DBN.
The PSO is utilized for estimation of DBN parameters during learning process.

###  5.4 Deep Neural Network

Xu et al. have proposed an efficient supervised learning-based Deep Neural
Network (esDNN) algorithm [41] to extract and learn the features of historical
data and accurately predict future workloads. The multivariate data is
converted into supervised learning time series and a revised GRU is applied
which can adapt to the variances of workloads to achieve accurate prediction
and overcome the limitations of gradient disappearance and explosion. A DNN
based workload prediction method (designated as DNN-MVM) is developed in [42]
to handle the workload prediction from multiple virtual machines. It employed
a pre-processing and feature selection engine to handle data directly from
these virtual machines. The model classifies data based on historical loads to
provide enhanced information or knowledge to the cloud service provider for
resource management and optimization. It is useful to predict the peak demands
of resources in the future. This model is validated using Grid Workload
Archive (GWA) dataset. Bi et al. [44] have proposed another DNN based workload
prediction model wherein a logarithmic operation is performed ahead of task
smoothening to minimize the standard deviation. Thenafter, a Savitzky-Golay
(S-G) filter is applied to eliminate the extreme points and noise interference
in the sequence of the original data. The DNN based LSTM (SG-LSTM) is employed
to extract complicated characteristics of a task time series. A Back
Propagation Through Time (BPTT) algorithm is implemented by adopting gradient
clipping method to eliminate a gradient exploding problem and optimization of
the model is accomplished using Adam Optimizer. The model is evaluated with
Google Cluster dataset to confirm its efficacy over existing methods. A DNN
learning based Power Prediction Engine (DNN-PPE) is proposed in [43] which
includes data acquisition, data pre-treatment, and prediction modules.
Recursive auto encoder is utilized for short-time fine-grained prediction that
can track the rapid changes of the power consumption within a data centre.

TABLE III: Comparative Summary of Deep Learning-based Prediction Approaches

Notable Contributors (Timeline) |  Model/ Approach/ Framework |  Workflow/ Strategy |  Datasets |  Implementation/ Simulation tool |  Predicted parameters |  Error metrics |  Results or Remarks
---|---|---|---|---|---|---|---
Kumar et al. [29] (2018) |  LSTM-RNN |  Combinations of networks in loop retains learning information, performs specific operation to produce output for next network in loop |  Web server HTTP traces |  MATLAB |  No. of requests per unit time |  MSE |  reduced RMSE up to 0.00317
Tang [30] (2019) |  2-D LSTM |  Input data is analysed on week dependence basis and weights parallelization algorithm is used for improved optimization of LSTM |  l workload of the Shanghai Supercomputer |  Not mentioned |  Workload over days |  MSE |  accuracy improved for large-scale real-time cloud services
Tuli et al. [34] (2021) |  Encoder LSTM |  Encoder is used for input matrices preparation and LSTM predicts the load information |  PlanetLab traces |  CloudSim and Python |  CPU, Memory, Bandwidth |  MSE, MAPE |  reduced execution time, resource contention, energy and SLA violations by 13%, 11%, 16% and 19%
Gao et al. [31] (2020) |  Bi-LSTM |  Training data propagates via input layer, two Bi-LSTM layers, one output layer and LR layer during learning process |  55,55,55 tasks traces |  Tensorflow in Python |  task failure rate |  F1-Score |  93% accuracy and 87% task failure correctly predicted
Ruan et al. [32] (2021) |  CrystalLP |  Time-series model collects load patterns and LSTM trained with SGD+Adam optimizer predicts load |  Web search archive SPC traces |  Keras library, Python |  Request size |  MAPE, RMSE, MAE |  achieved 1.10% improvement in MAPE, and better performance in MAE over existing methods
Ruan et al. [33] (2022) |  FEMT-LSTM |  a turning point prediction model considering cloud workload features followed by feature-enhanced deep learning model is developed |  Google Cluster, Alibaba, HPC Grid workloads |  Keras library, Python |  CPU usage |  binary cross-entropy, F1, precision, recall |  F1 score is improved by 6.6% over existing approaches
Zhang et al. [35] (2018) |  CP auto-encoder |  canonical polyadic decomposition compresses the features and stacked auto-encoder learns the patterns for prediction |  PlanetLab traces |  MATLAB |  CPU utilization |  MAPE, RMSE |  achieves a higher training efficiency and prediction accuracy for industrial workloads
Chen et al. [36] (2019) |  L-PAW auto-encoder |  TSA extracts workload patterns and GRU+RNN prdicts the upcoming load |  TensorFlow 1.4.0, Python |  DUX-cluster, Alibaba, and Google cluster |  CPU, memory, disk I/O usage |  MSE, CDF |  outperformed the accuracy of LSTM, RNN, GRU
Qiu et al. [38] (2016) |  DBN+RBMs |  DBN extracts significant patterns while learning and regression is used for prediction |  PlanetLab traces |  CloudSim |  CPU utilization |  MAPE |  improved the performance up to 1.3% over existing method
Zhang et al. [39] (2017) |  DBN+OED |  Pre-processed data is passed into DBN+OED model which is tuned with Backpropagation algorithm |  Google Cluster traces |  Python |  CPU, RAM usage |  MSE |  MSE achieved in the range [$10^{-4}$, $10^{-3}$]
Wen et al. [40] (2020) |  DP-CUPA |  DBN predicts the load information and PSO estimates the fitness values of its tuning parameters |  Google Cluster traces |  not mentioned |  CPU usage |  MSE, MAPE, MAE |  outperformed autoregressive, DBN, Grey model
Xu et al. [41] (2022) |  es-DNN |  supervised learning converts multi-variate data into time-series and modified GRU is applied |  Alibaba and Google Cluster traces |  TensorFlow 2.2.0 in Python |  CPU usage per time-unit interval |  MAPE, MSE, RMSE |  reduced number of active hosts efficiently and optimized cost
Bhagtya et al. [42] (2021) |  DNN-MVM |  Selected data from multiple VMs is classified, preprocessed and passed to DNN-MVM for learning process |  Grid Workload Archive (GWA) traces |  Google Colab using Keras |  CPU, Memory, and Disk Utilization |  MSE |  achieved more than 85% prediction accuracy for each resource
Bi et al. [44] (2019) |  SG-LSTM |  S-G filter provides smoothen data to LSTM for more accurate prediction |  Google traces |  Python |  CPU, Memory |  Logarithmic RMSE, $R^{2}$ |  outperformed BPNN, LSTM, SG-LSTM, and SG-BPNN
Li et al. [43] (2016) |  DNN-PPE |  Preprocessed data is passed to Recursive Auto Encoder |  world cup 98 (WC98) and Clark net traces |  Python |  Power, No. of requests |  - |  79% error reduction over canonical prediction

##  6 Hybrid Prediction Models

The prediction models based on hybrid machine learning combines different
machine learning algorithms (say $N$ algorithms) and feed the outcome of one
algorithm to another (one-way) to create an efficient machine learning model
for precise and accurate predictions. These hybrid models are build using
various collaborations such as ‘Classification \+ Classification’;
‘Classification \+ Clustering’; ‘Clustering \+ Clustering’; ‘Clustering \+
Classification’; ‘Classification \+ Regression’; ‘Clustering \+ Regression’
etc. These combinations are selected as per the requirement and challenges
respective to the intended problem to minimize features noise and biasness,
reduce variance, and enhance the accuracy of prediction. A schematic
representation of hybrid prediction model is depicted in Fig. 6.

Figure 6: Hybrid Prediction Model

In this model, the raw and complex data samples are pre-processed by filtering
and smoothening the data samples via extraction of significant features,
aggregation, and scaling or normalization. The pre-processed data samples
passes through a series of $N$ machine learning algorithms feeding their
outomes as an input to other machine learning algorithm, generating a hybrid
prediction model for performance measurement and deployment for the intended
purposes. The significant key contributions related to hybrid approach based
workload prediction models are discussed below.

Kardani et al. [45] have developed a hybrid Anomaly-aware Deep Reinforcement
Learning-based Resource Scaling (ADRL) for dynamic resource scaling in the
cloud environment. It presents an anomaly detection method for Deep
Reinforcement Q-Learning-based decision making scheme that identifies
anomalous states in the system and triggers actions accordingly. This work
includes two levels of global and local decision-makers to govern the
necessary scaling actions. ADRL improved the QoS with essential actions only
and increased stability of the system. A hybrid Recurrent Neural Network (RNN)
based prediction model named ‘BHyPreC’ is proposed in [46] which comprises of
Bidirectional Long Short-Term Memory (Bi-LSTM) on top of the stacked LSTM and
GRU for prediction of VM resource usage. It improves the non-linear data
analysis capability of Bi-LSTM, LSTM, and GRU models separately and confirmed
better accuracy compared with other statistical models. It uses combined grid
search technique of historical window size to optimize the model and determine
the best possible set of window size. Bi et al. [47] proposed integrated deep
learning method named ‘BG-LSTM’ which incorporates BiLSTM and GridLSTM to
achieve high-quality prediction of workload and resource time series. During
preprocessing, it applies a filter of Savitzky-Golay (SG) to reduce the
standard deviation before smoothing workload. It can effectively extract
complex and non-linear features of relatively longer time series and achieve
high prediction accuracy. A Hierarchical Pythagorean Fuzzy Deep Neural Network
(HPFDNN) is proposed in [61] to predict the amount of cloud resources
requirement. The neural representations of original sampling data are used as
a supplementary approach for clear interpretations of true results which is
beyond the use of fuzzy logic. The users can determine the expected quantity
of cloud services utilizing the forecasts of the deep neural network which
will help in reducing cost.

A hybrid autonomous resource provisioning model based on MAPE-k control loop
for multi-tier applications is presented in [28]. It is a combination of the
Fuzzy Analytical Hierarchy Process approach named as ‘ FAHP’. The experimental
results indicate that the proposed solution outperforms in terms of allocated
virtual machines, response time, and cost compared with the other approaches.
An Adaptive Classified Prediction Scheme (ACPS) is proposed in [49] which
first categorises the workloads into different classes that are automatically
assigned for different prediction models according to workload features.
Further, the problem of the workload classification is transformed into a task
assignment by establishing a mixed 0–1 integer programming model which is
solved quickly by utilizing an improved branch and bound algorithm. Peng et
al. [37] have applied a GRU based Encoder-Decoder network named ‘GRUED’
containing two Gated Recurrent Neural Networks (GRNNs) to address these
issues. It has been evaluated via experiments for the prediction of multi-
step-ahead host workload in cloud computing. Shuvo et al. [50] have proposed a
novel hybrid-method named ‘LSRU’ for improving the prediction accuracy. LSRU
is an integration of LSTM and GRU for short-time ahead prediction along with
long-time ahead prediction with sudden burst of workload.

TABLE IV: Comparative Summary of Hybrid Machine Learning-based Prediction
Models

Notable Contributors (Timeline) |  Model/ Approach/ Framework |  Workflow/ Strategy |  Datasets |  Implementation/ Simulation tool |  Predicted parameters |  Error metrics |  Results or Remarks
---|---|---|---|---|---|---|---
Kardani et al. [45] (2021) |  ADRL: Deep RL+Q-Learning |  Deep Q-learning based RL model to respond CPU and memory bottleneck problem that help in resource scaling and decision making |  Web-based Rice University Bidding System (RUBiS) |  Python with Java-based CloudSim |  CPU, Memory, Response-time |  MSE |  improved QoS and stability
Karim et al. [46] (2021) |  BiHyPrec: Bi-LSTM + LSTM + GRU |  Pre-processed data is given to collaborative model of Bi-LSTM, LSTM, and GRU units that accomplishes a deep learning-based approach to effectively tackle the complexity and non-linearity of time series data |  Bitbrains traces |  Python and Google Colaboratory |  CPU usage |  MSE, MAPE, MAE, RMSE |  performs better over ARIMA, LSTM, GRU, Bi-LSTM
Bi et al. [47] (2021) |  BG-LSTM: Bi-LSTM + GridLSTM |  Savitzky-Golay (SG)is used to smoothen data which is passed to hybrid model of Bi-LSTM and GridLSTM for prediction |  Python |  Google Cluster traces |  CPU and RAM usage |  MSE, RMSLE, $R^{2}$ |  better accuracy over SG-LSTM, SG-Bi-LSTM, SG-GridLSTM
Chen et al. [61] (2021) |  HPFDNN: Hierarchical Pythagoras + Fuzzy DNN |  Pythagorean fuzzy logic, neural representations, and deep neural network are integrated and network training method with adaptive learning rate is adopted to minimize the cost of cloud services for users |  Carnegie Mellon University dataset |  Not mentioned |  Number of requests |  Accuracy cost and total cost |  saved 202.48 dollars for real-time requests over existing methods
Khorsand et al. [28] (2018) |  FAHP |  FAHP and SVR algorithms are developed for workload prediction and resource provisioning and the appropriate autoscaling decisions |  Clark Net, NASA, Synthetic workload |  CloudSim and Open source RUBIS |  Request arrival, response time, cost |  MSE, NMSE, RMSE, RMSSE |  Reduces cost of rental resources for cloud service provider with QoS
Liu et al. [49] (2017) |  ACPS |  Distinct prediction models are automati- cally assigned depending on the workload features |  Google Cluster traces |  Python |  Response time and Cost |  Mean error, mean relative prediction error (MRPE) |  Prediction error is reduced by 40.86% over Linear Regression
Peng et al. [37] (2018) |  GRUED |  GRU encoder maps a variable-length workload sequence to a fixed-length vector, and the GRU decoder maps the vector representation back to a variable-length future workload series |  Google Cluster and Dinda workloads |  Python, UNIX system |  CPU, Job arrivals |  MAE, MAPE, RMSE, root mean segment squared error (RMSSE) |  Reduce prediction error over LSTM
Shuvo et al. [50] (2020) |  LSRU: LSTM + GRU |  Some statistical methods such as AR, ES, ARMA, and ARIMA for forecasting and passed to LSTM and GRU combined unit for an improved prediction accuracy |  Bitbrains |  Kaggle |  CPU, Disk, memory, bandwidth |  MAE, MAPE, RMSE, MSE |  Reduce prediction error over LSTM and GRU

##  7 Ensemble Prediction Models

An ensemble approach involves the use of multiple ‘Base Prediction’ ($BP$)
models or ‘Experts’ to forecast the expected future outcome of an event. The
final outcome of an ensemble model is computed by combining the forecasts of
each expert using a voting engine. The conceptual architecture of an ensemble
based predictive approach is illustrated in Fig. 7.

Figure 7: Ensemble Prediction Model

The historical and live data samples are pre-processed and a ‘sliding window’
is prepared and the input data vector thus created is given as input to all
the base predictors {$BP_{1}$, $BP_{2}$, …, $BP_{z}$}, where $z$ is the number
of base predictors used for ensemble learning. The estimated outcome of each
base predictor is assigned a weight value indicating their significance in the
final prediction outcome. The weight allocation and their updation requires a
learning process using suitable optimization method such as multi-class
regression, priority based method, or evolutionary learning algorithm. The
ensemble learning approach is more effective over the individual prediction
method that suffers from challenges like, high variance, low accuracy, feature
noise and bias. Basically, in ensemble approach, the various machine learning
models work independently of each other to give a prediction and a voting
system (hard or soft voting) based on weighted values associated to each base
predictor determines the final prediction. The prominent models based on
ensemble learning are discussed below.

Singh et al. [51] have addressed the problem of extensive range of workloads
prediction by extending and adapting two online ensemble learning methods
including Weighted Majority (WM) and Simulatable Experts (SE). The classical
SEs are extended from binary outcome space to k-outcome space to make them
suitable for solving any k-class problem (designated as ‘kSE’). The Weighted
Majority ensemble model parameters are regenerated incrementally making these
algorithms computationally more efficient and suitable for handling massive
range of online data streams (designated as ‘WMC’). These models are evaluated
using large datasets of 1570 servers and have verified that approximately 91%
servers can be correctly predicted with the extended versions of these
algorithms. Feng et al. [52] have proposed an ensemble model for Forecasting
workloads with Adaptive Sliding window and Time locality integration named
FAST. An adaptive sliding window algorithm is developed considering
correlations of trend and time, and random fluctuations of forthcoming
workloads to maximize accuracy of prediction with lower overhead. Also, a time
locality concept for local-predictor behavior is accomplished for the error-
based integration strategy. The entire model is integrated by developing a
multi-class regression weighting algorithm. The performance of the model is
validated using Google Cluster trace datasets. An integrated model for
temporal prediction of workloads is proposed in [19] which combines
Savitzky–Golay (SG) filter and wavelet decomposition with stochastic
configuration networks to predict approaching workload. In this model, a task
time series is smoothened using SG filter and decomposed into components via
wavelet decomposition method. This model named ‘SGW-S’ is able to characterize
the statistical features of both trend and detailed components and achieved an
improved performance with faster learning.

A cloud resource forecasting model named ‘CloudInsight’ (ClIn) based on
ensemble prediction approach is proposed in [53]. This model meticulously
locates the most admissible machine learning prediction approach by training a
statistical features based classifier for the accurate estimation of job
arrivals. It employs a number of local predictors or experts and builds an
ensemble prediction model using them by dynamically determining the
significant weights (or contributions) of each local predictor. The adaptive
weight scores are optimized at regular intervals with the help of multi-class
regression with a SVM classifier for selection of the most appropriate
prediction model with highest accuracy during respective prediction interval.
This model is tested using three different categories of workloads including:
web, cluster and high performance computing workloads to prove its efficacy
over existing prediction models. Baig et al. [54] have proposed an ensemble
and adaptive cloud resource estimation model named ’Adaptive Model Selector’
(AMS). This model determines the most admissible machine learning prediction
approach by training a statistical features based classifier for the accurate
estimation of resources utilization. It builds a classifier using Random
Decision Forest (RDF) to predict the best model for a given sliding window
data by training and re-training periodically at regular time-intervals. The
selected features and identified prediction methods are logged as training
data and passed to sliding window for learning process. The performance of
this model is evaluated using Google Cluster, Alibaba, and Bitbrains datasets.
Kumar et al. [55] have presented an ensemble learning based workload
forecasting model named ‘E-ELM’ that uses extreme learning machines and their
associated forecasts are weighted by a voting engine. In this work, a
metaheuristic algorithm inspired by blackhole theory is applied to decide the
optimal weights. The accuracy of the model is evaluated for CPU and memory
demand requests of Google cluster traces and for CPU utilization of PlanetLab
VM traces. Chen et al. [48] have proposed a self-adaptive prediction method
using ensemble model and subtractive-fuzzy clustering based fuzzy neural
network (ESFCFNN). The user preferences and demands are characterized and an
ensemble prediction model is constructed using several base predictors.

TABLE V: Comparative Summary of Ensemble Learning-based Prediction Approaches

Notable Contributors (Timeline) |  Model/ Approach/ Framework |  Workflow/ Strategy |  Datasets |  Implementation/ Simulation tool |  Predicted parameters |  Error metrics |  Results or Remarks
---|---|---|---|---|---|---|---
Singh et al. [51] (2014) |  kSE+WMC |  SE is extended to k-outcome space and WM is improved for incremental and computationally efficient learning process |  Dataset G(1570) |  Not mentioned |  server workload |  MAPE |  89% accurate predictions as compared with 13–24% for baseline algorithms
Feng et al. [52] (2022) |  FAST |  The adaptive sliding window considers all types of workload trends with time locality concept for error-based integration is developed to enhance prediction accuracy |  Google Cluster traces |  Python |  CPU, memory |  Absolute Error, RMSE, $R^{2}$ |  improved prediction accuracy by 14.99% to 27.55on RMSE and 22.57% to 76.86% on $R^{2}$
Bi et al. [19] (2019) |  SGW-S |  SG filter and wavelet decomposition is integrated with stochastic configuration networks to predict workload with high accuracy |  Google Cluster traces |  Not mentioned |  task arrival rate |  MSE, $R^{2}$ |  Use of SG-filter with wavelet decomposition helps improve the prediction accuracy
Kim et al. [53] (2020) |  ClIn: ensemble with multi-class regression |  Different local predictors constitutes an ensemble prediction model by dynamically determining and updating the significant weights of each predictor |  Web, cluster, HPC Grid workloads |  Python |  Job arrival per unit time |  Normalised RMSE, Absolute Error |  Up to 15%–20% less under-/over-provisioning with high cost-efficiency and low SLA violations
Baig et al. [54] (2019) |  AMS |  Predict the forthcoming resource demand with the best predictor model as selected by the RDF classifier which is updated periodically with time |  Alibaba, Bitbrains, Google Cluster |  Python |  CPU |  RMSE, MAE |  improved prediction accuracy from 6%-27% over current methodologies
Kumar et al. [55] (2020) |  E-ELM |  ELM based local predictors are trained and selected by employing a weight updation method with a help of a metaheuristic algorithm |  MATLAB |  Google Cluster and PlanetLab traces |  CPU, memory |  RMSE, MAE |  RMSE reduced up to 99.20% over existing methods
Chen et al. [48] (2015) |  Subtractive-fuzzy clustering |  user preferences and demands are characterized into an ensemble prediction model subtractive-fuzzy clustering based fuzzy neural network |  Data Flow Statistics traces |  CloudSim |  Network resource traces |  MSE, MAE |  Effective in resource demand prediction

##  8 Quantum Neural Network based Prediction Models

A Quantum Neural Network (QNN) based prediction model is an intelligent model
for prediction with the machine learning capabilities of neural network and
computational proficiency of quantum mechanics to achieve prediction accuracy
with high precision. Basically, it is a neural network comprising of Qubit
neurons and Qubit weights instead of real-numbered values and the training
data information is also propagated in the form of Qubits. A Qubit is
represented as a one, a zero, or any quantum superposition of these states.
Mathematically, it can be realized as:
$|\Psi\textgreater=\alpha|0\textgreater+\beta|1\textgreater$, where $\alpha$
and $\beta$ are complex numbers specifying the probability amplitudes of
states $|0\textgreater$ and $|1\textgreater$ respectively. The schematic
representation of QNN based prediction model is shown in Fig 8 which comprises
of one input, multiple hidden and one output layers having $n$, $p$ and $q$
qubit nodes, respectively which represents $n$-$p$-$q$ Qubit network
architecture. The inter-connection weights between Qubit neurons of different
layers are also taken in the form of Qubits that are adjusted during learning
process.

Figure 8: Quantum Neural Network

The state transitions of Qubit neurons are derived from the various Quantum
gates such as Rotation gate, Controlled-Not gate etc. The training data
samples values are extracted and aggregated into a specific time-interval such
as 5 minutes which are scaled in a specific range using a normalization
function. The normalized data values are transformed into quantum state values
or Qubits by applying the effect of qubit rotation using Eqs. (18) and (19);

$\displaystyle{y^{In}}_{i}=f({\Theta_{i}}^{In})$ (18)
$\displaystyle\Theta_{i}=\frac{\pi}{2}\times\mathcal{D}_{i}$ (19)

where $\mathcal{D}_{i}$ is the $i^{th}$ input data point, $\Theta_{i}$ is
$i^{th}$ Quantum input point to the network. The QNN model extracts relevant
and significant patterns from actual workload and analyzes $n$ previous
workload values to estimate forthcoming workload information at the next
$(n+1)^{th}$ instance of time within the datacentre. The learning process of
QNN is derived by a Qubit based optimization algorithm that can manipulate,
explore, and exploit Qubits to regenerate the population and prevent the
problem of stagnation. The workload prediction methods using QNN is still at
an infancy stage.

The only work established thus far using QNN based model for workload
prediction is proposed in [56]. This model exploits the computational
efficiency of quantum computing by encoding workload information into Qubits
and propagating this information via network for estimatimation of the
workload or resource demands with enhanced accuracy proactively. The rotation
and reverse rotation effects of the C-NOT gate served the activation function
at the hidden and output layers to optimize the Qubit weights. Self Balanced
Adaptive Differential Evolution (SB-ADE) algorithm is developed to optimize
qubit network weights. This model is evaluated using three different
categories of workloads where the prediction accuracy is substantially is
improved over the existing approaches. A workload prediction model using
complex numbers, is presented in [62] where a high capability of learning and
better accuracy is applied to multi-layered neural networks with multi-valued
neurons (MLMVN) prediction model in less time.

##  9 Performance Evaluation

###  9.1 Experimental Set-up

The simulation experiments are conducted on a server machine assembled with
two Intel® Xeon® Silver 4114 CPU with a 40 core processor and 2.20 GHz clock
speed. The computation machine is deployed with 64-bit Ubuntu 16.04 LTS,
having 128 GB RAM. All the selected best prediction works based on
Evolutionary Neural Network (ENN), Ensemble Learning (EL), Hybrid Learning
(HL), Deep Learning (DL), Evolutionary Quantum Learning were implemented in
Python 3.7 with the details of various intended parameters with their values
listed in Table VI.

TABLE VI: Experimental set-up parameters and their values Preiction Models | Parameter |  Values
---|---|---
Evolutionary Neural Network | Input nodes |  10
Hidden nodes |  7
Maximum iteration |  250
Training data size |  70%
Mutation learning period |  10
Crossover learning period |  10
Size of population |  15
Training algorithm |  Differential Evolution
Ensemble Learning | #ELM networks |  [10,100]
Input nodes |  [7,70]
Hidden nodes |  [5,50]
Population size |  20
Maximum iteration |  100
Accuracy threshold |  0.007
Training data size |  70%
Training algorithm |  Blackhole Optimization
Hybrid Learning | Input nodes |  100
Epochs |  20-50
Batch size |  16
Activation function |  tan$h$
Training data |  70%
Number of epochs |  500-1000
Batch size |  1-4
Training algorithm |  Adam Optimizer
Deep Learning | Deep learning libraries: |
tensorflow |  0.12.1
keras |  1.2
Training data |  70%
Number of epochs |  500-1000
Neurons |  4-10
Batch size |  1-4
Training algorithm |  Gradient descent
Quantum Learning | Input nodes |  10
Hidden nodes |  7
Output nodes |  1
Number of epochs |  50
Training data |  70%
Population size |  15
Mutation learning period |  5
Crossover learning period |  5
Training algorithm |  Quantum Differential Evolution

###  9.2  Data Sets

The performance analysis and comparison of various machine learning based
prediction models are executed using three different benchmark datasets
including CPU and memory usage traces from Google Cluster Data (GCD) [63] and
CPU usage from PlanetLab (PL) virtual machine traces [64]. GCD workload
provides behavior of cloud applications for the cluster and big data analytics
such as Hadoop which gives resource: CPU, memory, and disk I/O request and
usage information of 672,300 jobs executed on 12,500 servers collected over a
period of 29 days. The CPU and memory utilization percentage of VMs are
obtained from the given CPU and memory usage percentage for each job in every
five minutes over a period of twenty-four hours. PL contains CPU utilization
of more than 11000 VMs measured every five minutes during ten random days in
March-April, 2011. The respective values of resource usage are extracted and
aggregated according to different prediction-intervals such as 5, 10, 20, ….,
60 minutes. These values are re-scaled in the range [0, 1] using the
normalization formula stated in Eq. (1). Table VII shows the statistical
characteristics of the evaluated workloads.

TABLE VII: Characteristics of evaluated workloads

Workload | Duration | Jobs | Mean(%) | St.dev
---|---|---|---|---
GCD-CPU ($G^{C}$) | 10 days | 2 M | 21.84 | 13.62
GCD-memory ($G^{M}$) | 10 days | 2 M | 19.55 | 16.6
PL-CPU ($P^{C}$) | 10 days | 1.5 M | 19.77 | 14.55

###  9.3 Evaluation Metrics

Forecast accuracy of the prediction models are evaluated using following error
metrics:

Mean Squared Error ($\mathcal{MSE}$): It is one of the well known metric to
measure the accuracy of prediction models, which puts high penalty on large
error terms. The model is considered to be more accurate if its score is
closer to zero. The mathematical representation of the metric is mentioned in
Eq.(20), where $m$ is the number of data points in the workload trace,
$\mathcal{Z}^{Ac}(t)$ and $\mathcal{Z}^{Pr}(t)$ are actual and predicted
workload values, respectively at $t^{th}$ instance.

$\mathcal{MSE}=\frac{1}{m}\sum_{t=1}^{m}(\mathcal{Z}^{Ac}(t)-\mathcal{Z}^{Pr}(t))^{2}$
(20)

Mean Absolute Error ($\mathcal{MAE}$): In mean squared error the square of
higher error values may receive more weightage which can effect the accuracy
of prediction. While $\mathcal{MAE}$ assigns equal weight to each error
component and measures the accuracy of the prediction model by computing the
mean of absolute differences between actual ($\mathcal{Z}^{Ac}(t)$) and
predicted ($\mathcal{Z}^{Pr}(t)$) workloads at $t^{th}$ time-instance as shown
in Eq. (21). It produces a non negative number to evaluate the forecast
accuracy and if it is close to zero, forecasts are very much similar to actual
values.

$\mathcal{MAE}=\frac{1}{m}\sum_{t=1}^{m}|\mathcal{Z}^{Ac}(t)-\mathcal{Z}^{Pr}(t)|$
(21)

###  9.4 Results

The performance of different prediction models including Evolutionary Quantum
Neural Network (EQNN) [56], Ensemble Learning (EL) [55], Hybrid Learning [50],
Deep Learning (DL) [29], Evolutionary Neural Network (ENN) [25]; are
thoroughly investigated and compared using extensive range of heterogeneous
cloud applications and variable resource utilization by VMs. We have evaluated
and compared the different types of learning-based models for $\mathcal{MSE}$
with confidence metrics, $\mathcal{MAE}$, Absolute Error Frequency (AEF), and
time elapsed in Training (TT).

####  9.4.1 Mean Squared Error

Fig. 9 compares mean values of three different categories of workloads
including GCD-CPU traces (Fig. 9a), GCD-Memory traces (Fig. 9b), and PL-CPU
traces (Fig. 9c). It is observed from the figures that $\mathcal{MSE}$ varies
differently with the variety of prediction models and increases with the size
of prediction interval from 5 to 60 minutes. The is due to the fact that with
increment in the size of prediction window, the number of avaiable training
data samples decreases. The prediction accuracy with respect to reduction in
the values of average $\mathcal{MSE}$ follows a trend: EQNN $<$ EL $\leq$ HL
$<$ DL $<$ ENN. Also, it is notified that the difference among prediction
errors ($\mathcal{MSE}$) is lesser for shorter prediction interval which
increases with the decrement in the number of training data samples with
growing prediction window-size. Hence, it can be concluded that for short-term
prediction, all types of prediction models produce an expected level of
prediction accuracy, and the major difference in the performance of prediction
arises with the long-term prediction intervals. The obtained experimental
values demonstrates that Quantum learning-based prediction model (EQNN) is
providing least prediction error for majority of the prediction intervals and
most of the data traces while evolutionary learning-based prediction model
(i.e., ENN) produces highest $\mathcal{MSE}$ values for majority of the
experimental cases. For GCD-CPU (Fig. 9a), EQNN is performing consistently
best among all the approaches because of the employment of Qubits and Quantum
superposition effects which imparts precise and intuitive learning of
correlations and relevant patterns during learning process. Moreover, EL gives
lesser $\mathcal{MSE}$ values than HL, DL, and ENN because of involvement of
multiple base prediction models which adaptively selects the best prediction
model each time and rejuvenates the learning process by updating the weights
associated with the respective respective base predictors. The prediction
errors for HL is lesser as compared with DL by reason of the integration of
filtering and smoothening approaches (by leveraging various filtering methods
like SG-Filters or using GRU to improve the accuracy while minimizing the
drawback of LSTM) before actual prediction. In HL, two or more approaches
combines cooperatively by diminishing the limitations of each intended
approach and thus producing an effective prediction model to forecast resource
usage of extensive range of cloud workloads. The experiments of DL are
performed using LSTM based prediction method which performs better than ENN
for all the cases including $G^{C}_{5}$ to $G^{C}_{60}$. The resultant graph
shown in Fig. 9b reveals similar trends for the GCD-Memory traces where HL and
EL show closer performance i.e., lesser than EQNN but superior than DL and ENN
based prediction models. The results achieved using DL based prediction model
entails improved accuracy in terms of lesser values of $\mathcal{MSE}$ than
ENN for all the respective experiments except for the cases of $G^{M}_{30}$
and $G^{M}_{60}$. Fig. 9c represents the comparison of $\mathcal{MSE}$ for PL
CPU traces, wherein the difference among prediction error values is slight but
significant that supports the aforementioned trend of performance. Futhermore,
the confidence metrics are computed for the achieved $\mathcal{MSE}$ results
as shown in Table VIII, wherein error margin (EM) and confidence-interval (CI)
are reported for all the experimental cases.

(a) GCD-CPU traces (b) GCD-Memory traces  (c) PlanetLab CPU traces

Figure 9: Mean Squared Error  TABLE VIII: Confidence metrics for Mean Squared
Error

Dataset | PWSa | Metrics | EQNN | EL | HL | DL | ENN
---|---|---|---|---|---|---|---
GCD-CPU | 5 | EM | 5.3012E-06 | 1.3824E-06 | 2.3666E-06 | 1.8460E-06 | 2.7320E-06
CI | 8.799E-03 - 8.801E-03 | 1.079E-03 - 1.080E-03 | 1.169E-03 - 1.170E-03 | 1.799E-03 - 1.800E-03 | 2.199E-03 - 2.200E-03
10 | EM | 1.7969E-06 | 3.6608E-06 | 5.4890E-06 | 4.5940E-06 | 3.2681E-06
CI | 1.398E-03 - 1.140E-03 | 1.992E-03 - 1.993E-03 | 3.239E-03 - 3.241E-03 | 3.895E-03 - 3.900E-03 | 4.599E-03 - 4.600E-03
30 | EM | 2.1890E-06 | 1.4865E-06 | 6.8200E-05 | 4.9826E-06 | 6.8210E-06
CI | 1.298E-03 - 1.299E-03 | 2.989E-03 - 2.990E-03 | 3.733E-03 - 3.747E-03 | 4.179E-03 - 4.180E-03 | 5.069E-03 - 5.071E-03
60 | EM | 1.1743E-06 | 1.702E-06 | 6.476E-06 | 5.8040E-06 | 8.2199E-06
CI | 2.280E-03 - 2.282E-03 | 3.798E-03 - 3.801E-03 | 4.229E-03 - 4.231E-03 | 4.179E-03 - 4,181E-03 | 2.39E-05
GCD-Memory | 5 | EM | 5.7890E-05 | 6.7740E-06 | 1.7841E-06 | 8.7341E-06 | 1.6839E-05
CI | 1.079E-03 - 1.081E-03 | 1.369E-03 - 1.370E-03 | 1.919E-03 - 1.920E-03 | 1.9391E-03 - 1.941E-03 | 4.090E-03 - 4.093E-03
10 | EM | 2.0799E-05 | 4.0514E-06 | 4.9616E-06 | 6.6035E-06 | 2.5002E-05
CI | 2.507E-03 - 2.512E-03 | 3.829E-03 - 3.830E-03 | 3.909E-03 - 3.910E-03 | 4.099E-03 - 4.101E-03 | 5.597E-03 - 5.603E-03
30 | EM | 6.8200E-05 | 6.4132E-05 | 7.1294E-05 | 5.813E-05 | 2.5785E-05
CI | 3.329E-03 - 3.331E-03 | 5.593E-03 - 5.606E-03 | 6.953E-03 - 6.954E-03 | 9.307E-03 - 9.310E-03 | 8.667E-03 - 8.673E-03
60 | EM | 4.8290E-05 | 9.9990E-05 | 1.5458E-05 | 2.4786E-06 | 3.5760E-05
CI | 5.615E-03 - 5.625E-03 | 6.799E-03 - 6.801E-03 | 7.818E-03 - 7.822E-03 | 10.0991E-03 - 10.0996E-03 | 9.696E-03 - 9.703E-03
PlanetLab CPU | 5 | EM | 4.2810E-06 | 4.6890E-06 | 4.6170E-06 | 6.8910E-06 | 2.3980E-06
CI | 2.559E-03 - 2.560E-03 | 3.699E-03 - 3.700E-03 | 4.199E-03 - 4.200E-03 | 4.739E-03 - 4.740E-03 | 5.794E-03 - 5.795E-03
10 | EM | 8.4210E-06 | 9.8140E-06 | 7.1100E-06 | 8.7642E-06 | 1.0890E-05
CI | 3.499E-03 - 3.510E-03 | 4.092E-03 - 4.093E-03 | 5.899E-03 - 5.900E-03 | 6.099E-03 - 6.101E-03 | 7.058E-03 - 7.061E-03
30 | EM | 6.2814E-05 | 8.6411E-05 | 1.8230E-05 | 6.4210E-06 | 8.6810E-06
CI | 4.423E-03 - 4.436E-03 | 5.451E-03 - 5.468E-03 | 6.538E-03 - 6.541E-03 | 7.899E-03 - 7.900E-03 | 7.669E-03 - 7.670E-03
60 | EM | 8.4210E-05 | 9.9610E-05 | 6.4280E-05 | 2.8136E-05 | 9.8134E-05
CI | 6.811E-03 - 6.824E-03 | 9.270E-03 - 9.289E-03 | 10.075E-03 - 10.088E-03 | 10.449E-03 - 10.450E-03 | 10.690E-03 - 10.709E-03

a PWS: Prediction Window Size, EM: Error Margin, CI: Confidence Interval

####  9.4.2 Mean Absolute Error

The comparison of resultant values of $\mathcal{MAE}$ obtained for different
prediction models over various datasets is presented in Fig. 10. Similar to
the $\mathcal{MSE}$, the $\mathcal{MAE}$ values follows the common trend of
the performance for all the three evaluated workloads including GCD-CPU (Fig.
10a), GCD-Memory (Fig. 10b), and PL-CPU (Fig. 10c). As depicted in the three
aforementioned consequent bar graphs, the $\mathcal{MAE}$ values decreases in
the order: EQNN $<$ EL $\leq$ HL $<$ DL $<$ ENN. Further, it is observed that
the difference among prediction errors ($\mathcal{MAE}$) is increasing with
the growing prediction window-size because of the decrement in the number of
training data samples. Hence, it can be concluded that for short-term
prediction, all the types of prediction models produces an expected level of
prediction accuracy, and the major difference in the performance of prediction
arises with the long-term prediction intervals. The reason behind this is that
as the number of training samples decreases, there is not enough learning of
the patterns and the respective prediction models begins underestimating the
relevant information from the training dataset, resulting into a lesser
ability of developing necessary correlations and performance degradation with
diminished pattern learning.

(a) GCD-CPU traces (b) GCD-Memory traces  (c) PlanetLab CPU traces

Figure 10: Mean Absolute Error

####  9.4.3 Training time

The comparison of training-time elapsed during learning process of the various
prediction models over distinct workload traces for the prediction window-size
of 30 minutes is illustrated in Fig. 11. HL and DL based models consumed
similar time of training which is least among the training time of all the
prediction models which is due to the usage of Gradient descent and Adam
optimizer based training algorithms. While the time elapsed in the training of
ENN, EL, and EQNN is longer by reason of usage of evolutionary optimization
during learning process. The training time for EL is higher than EQNN and ENN
due to the engagement of multiple base prediction models which are
simultaneously during the learning process. Contrary to this, the single
network based prediction models are used during the learning process of EQNN
and ENN, where EQNN consume more time than ENN. The reason behind this is the
employment of Qubits and Quantum mechanics based network weight optimization
process which uses highly complex computation dealing with complex numbers
required for the generation and updation of qubit-based network weights. This
discussion concludes the trend for training time: DL $<$ HL$<$ ENN $<$ EQNN
$<$ EL. However, the efficiency and applicability of the various prediction
models is not affected because the training is a periodic task and can be
executed in parallel on the servers equipped with enough resources.

(a) GCD-CPU traces (b) GCD-Memory traces  (c) PlanetLab CPU traces

Figure 11: Training time consumption

####  9.4.4 Absolute Error Frequency

The prediction error achieved for the various comparative models is measured
and analysed by evaluating absolute prediction error and comparing its
frequency for all three workloads. Fig. 12 compares the frequency of absolute
error (Actual value-Predicted value), where EQNN yields least error for the
majority of the cloud workloads as compared with the other four types of
prediction models. The high frequency of absolute error for a prediction model
indicates the consistent potency and stable tendency for yielding maximum
prediction accuracy. The absolute error frequency observed for GCD-CPU traces
(Fig. 12a), GCD-Memory (Fig. 12b), and PL-CPU traces (Fig. 12c) follows a
common trend: ENN $<$ DL $<$ HL $<$ EL $<$ EQNN. The reason behind such a
trend is that EQNN employed Qubits population along with evolutionary
optimization to allow an improved intuitive learning of patterns which
concedes effective learning of extensive range of dynamic workload patterns
with optimum accuracy. EL follows EQNN because of the involvement of the
learning capability of multiple base predictor models which precisely learns
the relevant information from the varying types of workloads. The other
prediction models also show slightly lesser but acceptable frequency of
prediction error which varies according to the learning capabilities and
optimization algorithms involved in their learning process.

(a) GCD-CPU traces (b) GCD-Memory traces  (c) PlanetLab CPU traces

Figure 12: Absolute Error Frequency

###  9.5 Trade-offs and Discussion

All the machine learning algorithms have some trade-offs in relation to the
adaptive prediction of extensive range of workloads. Likewise, the key
difference among various ENNs-based prediction approaches is the evolutionary
optimization algorithm applied for the learning process that directly impacts
the performance of the prediction approach. The different evolutionary
optimization algorithms vary in exploration and exploitation methods involved
in the population update process and control parameters tuning process. The
evolutionary optimization approach having lesser number of hyperparameters for
tuning is more preferable as compared to the one having higher number of
tuning hyperparameters. For instance, a Blackhole learning algorithm is a
parameterless algorithm having lesser time and space complexity and predicts
with higher accuracy than Differential Evolutionary algorithm that involves
tuning of hyperparameters including crossover-rate, mutation-rate, learning
rate etc. Though the Deep learning approach learns the natural variations of
the data samples faster as compared to the evolutionary learning based feed-
forward neural network models, they need larger number of data samples for
training to estimate the output precisely. Also,deep learning approach having
higher complexity computationally, requires expensive GPUs and high processing
machines which scales up the cost of their applications. It has been observerd
that deep learning algorithms perform better in integration with other machine
learning approaches such as random forest for feature extraction and compiles
predicted output with the cooperation of the other classification approaches.
On the other hand, hybrid and ensemle learning approaches involve combined
operation of multiple machine learning algorithms consume higher space and
time complexity over the single unit machine learning approaches. Undoubtedly,
they adapt to the unseen data and extensive range of workloads with higher
efficiency over deep and evolutionary learning approaches because of inclusion
of several machine learning approaches at a common platform. Among the hybrid
and ensemble learning approaches, the ensemble approach is more adaptable as
it considers prediction output from all the considered machine learning
algorithms and applies weight optimization for selection of the predicted
outcome associated to these learning algorithms while generating the final
output. The quantum neural network based learning aproach is most efficient
among all the discussed approaches which is validated from the experiments for
the accurate prediction of varying workloads. In QNN, the usage of qubits
derived from complex numbers having higher diversity over real-numbered
network weight values, enables to generate more intuitive pattern and learning
of complex relations and helps to predict the output with higher accuracy.

Finally and most important, the diversity of cloud services such as IaaS,
PaaS, SaaS, FaaS, etc., has a significant impact while deciding the
heterogeneity of approaching workloads, and cloud service provider is bound to
provide seamless quality and capacity of resources. There is no perfect
guideline to select the best model for a particular cloud service because the
resource demands vary dynamically for all the cloud service models. Also, the
cloud workloads vary because of various features including resource capacity
(viz., CPU, memory, bandwidth, etc.) utilization; priority constraints such as
deadline of execution; cost of execution; the amount of variability in the
number of job requests over a time period, etc. However, these features
(except the amount of variability in the number of job requests over a time
period) do not have significant impact on the learning capability of different
prediction models because the corresponding data samples are generated
periodically, as event recordings for every type of workload, reporting all
the relevant information over a timestamp. These data samples are used for
training prediction models which show varying accuracy and training
computation cost for the estimated workload over the same time period.
However, the prediction models can be selected based on the priorities of
constraints, such as; for promising high availability and deadline
sensitivity, high accuracy prediction models should be selected irrespective
of high computation and training time. On the other hand, if there is a
constraint of minimum execution cost, the prediction model with lesser
computation and training cost is a better option to minimize the processing
cost of the respective workload. Further, the workload prediction model can be
chosen depending on the diversity of the workloads such as highly random,
periodically variable, randomly variable, uniformly or non-uniformly
diversifying, etc. Based on the above discussion of the characteristics,
design, and capability of the considered machine learning models, it can be
postulated that EQNN, Hybrid, and Ensemble models are more suitable for highly
random and diversified workloads as they are more capable of learning and
handling highly variable and heterogeneous traffic data patterns that involve
large amounts of dynamic data, multiple variables with complicated
relationships, and even multi-step time series traffic data. While the
Evolutionary learning model is preferably suitable for periodically and
uniformly variable workload observations recorded sequentially over equal
time.

##  10 Conclusions and Future Directions

This paper presented a comprehensive survey and performance evaluation based
comparison of the machine learning based workload prediction models for
resource distribution and managemment in cloud environments. The operational
design, utility, motivation, and challenges of the workload prediction
approach are discussed. Based on the differences in the conceptual and
operational characteristics of various prediction models, a classification and
taxonomy of machine learning driven prediction models is presented. The
leading prediction approaches respective to each prediction model is
thoroughly discussed. Further, all the discussed prediction models are
implemented on a common platform for an extensive investigation and comparison
of the performance of these models. Based on the intensive study and
performance evaluation, a trade-off among these prediction models and their
applicability are discussed to conclude the holistic study of the cloud
workload prediction models. In future, Explainable Artificial Intelligence
(XAI) approach can be utilized to build more robust workload prediction models
with retraceable mechanism that will help characterize accuracy, transparency,
fairness, and prediction outcomes in AI-powered resource management. Further,
the efficiency of QNN prediction models can be improved by optimizing the
qubit network with a lightweight optimization algorithm and reducing its
computational complexity.

## Acknowledgments

This research is supported by the National Institute of Technology,
Kurukshetra, India and Goethe University, Frankfurt, and Austrian Science Fund
(FWF) and the German Research Foundation (DFG), grant I 4800-N (ADVISE),
2020-2023. We thank the reviewers and editor for helping improve the
manuscript.

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|  Deepika Saxena is a Postdoctoral Research Associate at Goethe University,
Frankfurt. She earned her Ph. D. degree from the Department of Computer
Applications, National Institute of Technology (NIT), Kurukshetra, India. She
received her M.Tech (CSE) degree from Kurukshetra University Kurukshetra,
India in 2014. Her major research interests are Neural Networks, Evolutionary
Algorithms, Resource Management, and Security in Cloud Computing.
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|  Jitendra Kumar is an Assistant Professor in the Department of Computer
Applications, National Institute of Technology Tiruchirappalli, India. He
earned his doctorate from the National Institute of Technology Kurukshetra,
India in 2019. His current research interests include Cloud Computing, Machine
Learning, Data Analytics, Parallel Processing.
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|  Ashutosh Kumar Singh is working as a Professor in the Department of
Computer Applications, National Institute of Technology Kurukshetra, India. He
has more than 20 years research in various Universities of the India, UK, and
Malaysia. He received his PhD from Indian Institute of Technology, BHU, India
and Post Doc from Department of Computer Science, University of Bristol, UK.
He is also Charted Engineer from UK. His research area includes Verification,
Synthesis, Design and Testing of Digital Circuits, Data Science, Cloud
Computing, Machine Learning, Security, Big Data.
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|  Stefan Schmid is a Professor at the Technical University of Berlin,
Germany, working part-time for the Fraunhofer Institute for Secure Information
Technology (SIT) in Germany as well as for the Faculty of Computer Science,
the University of Vienna in Austria. MSc and Ph.D. at ETH Zurich, Postdoc at
TU Munich and the University of Paderborn, Senior Research Scientist at T-Labs
in Berlin, Associate Professor at Aalborg University, Denmark, and Full
Professor at the University of Vienna, Austria. He received the IEEE
Communications Society ITC Early Career Award 2016 and an ERC Consolidator
Grant 2019.
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