# A SMART RESOURCE MANAGEMENT MECHANISM WITH TRUST ACCESS CONTROL FOR CLOUD
COMPUTING ENVIRONMENT

Sakshi Chhabraa, Ashutosh Kumar Singhb
aPanipat Institute of Engineering and Technology, India
bDepartment of Computer Applications, National Institute of Technology,
Kurukshetra, India
<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

The core of the computer business now offers subscription-based on-demand
services with the help of cloud computing. We may now share resources among
multiple users by using virtualization, which creates a virtual instance of a
computer system running in an abstracted hardware layer. It provides infinite
computing capabilities through its massive cloud datacenters, in contrast to
early distributed computing models, and has been incredibly popular in recent
years because to its continually growing infrastructure, user base, and hosted
data volume. This article suggests a conceptual framework for a workload
management paradigm in cloud settings that is both safe and performance-
efficient. A resource management unit is used in this paradigm for energy and
performing virtual machine allocation with efficiency, assuring the safe
execution of users’ applications, and protecting against data breaches brought
on by unauthorised virtual machine access real-time. A secure virtual machine
management unit controls the resource management unit and is created to
produce data on unlawful access or intercommunication. Additionally, a
workload analyzer unit works simultaneously to estimate resource consumption
data to help the resource management unit be more effective during virtual
machine allocation. The suggested model functions differently to effectively
serve the same objective, including data encryption and decryption prior to
transfer, usage of trust access mechanism to prevent unauthorised access to
virtual machines, which creates extra computational cost overhead.

_Keywords_ Cloud Computing $\cdot$ Resource utilization $\cdot$ Trust access
control $\cdot$ Security $\cdot$ Load balancing $\cdot$ Resource allocation
$\cdot$ VM placement

##  1 Introduction

The demand for high-end computing equipment has steadily increased over the
last few years, which has caused the computational world to rapidly change [1,
2, 3, 4, 5, 6]. New computational paradigms including cloud computing, grid
computing, and cluster computing have emerged as a result of this progression.
Cloud computing has become one of these concepts that has experienced
substantial growth [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]. Three types
of cloud computing are available: public cloud, private cloud, and hybrid
cloud. Additionally, a variety of services are offered that fall essentially
into three categories: platform as a service (PaaS), infrastructure as a
service (IaaS), and software as a service (SaaS) The foundation of cloud
computing is Service-Oriented Architecture (SOA), which employs distributed
computing and virtualization technologies [19, 20, 21, 22, 23, 24, 25, 26, 27,
28, 29, 30, 31]. In SOA for cloud computing, users can access a shared pool of
resources across a network and customise the available resources to meet their
needs. Resource Management is a crucial component of virtualization and cloud
computing (RM). Resource management (RM) is a procedure that handles the
acquisition and release of resources. Flexible and on-demand resource
provisioning is achieved via virtualization methods [32, 33, 34, 35]. To do
this, either a new VM is built for each job that is received or it is added to
the user’s already-existing VM. When the job is finished, all the resources
are freed and added to the pool of available resources. According to the
Service Level Agreement (SLA) that is established between the service provider
and client, resource assignment is carried out.

Figure 1: Cloud Resource scheduling View

Resource management and consolidation are two key components of virtualization
technologies used in cloud. More isolated physical computers may be combined
into a virtualized environment using hypervisors in a cluster setting, using
fewer physical resources than ever before [36, 37, 38, 39, 40, 41, 42, 43, 44,
45, 46, 47, 48, 49, 50, 51, 52]. Even while the situation is improved, this
frequently falls short. Megawatts of electricity and thousands of physical
computers are needed for large-scale cloud installations [53, 54, 55].
Therefore, it is necessary to develop an effective Cloud computing system that
makes the most of the Cloud’s advantages while reducing its energy footprint.
However, by utilising cloud services to share the resources, this can
facilitate the system open to snooping, spoofing, and denial-of-service
assaults (DoS) assaults, trust issues etc. [56, 57, 58, 59, 60, 61, 62].
Because of this, the number of flaws in cloud computing is increasing between
the cloud service providers and the end clients.

Why we adopt efficient Resource Management? Resource management enables
managers to make the most of the personnel, resources, and equipment used on
their projects. Here are four compelling arguments for the process’ importance
in successful project management [63, 64, 65, 66, 67, 68, 69, 70, 71]. It’s
significant because:

  * •

Enhance resource utilisation: You may learn important information about how
your resources will be used through resource management analysis and
reporting.

  * •

Measures the effectiveness of the project: Making decisions based on resource
management makes it clear what is required to carry out new initiatives. You
can plan more effectively and increase project efficiency by tracking resource
usage and ROI.

  * •

Improve skill distribution: Resource management enables managers to predict
resource demand more precisely. Resource managers can find areas for capacity
and demand improvement by examining the current skills capacity and
comprehending the future demand.

  * •

Reduce the unnecessary cost: Resource management brings order to the chaos by
providing managers with the tools they need to maximise their resources and
reduce unnecessary spending. The data needed to analyse project and staff
costs and enhance planning and budgeting is already available. A perfect team
may also be maintained by distributing resources across tasks in accordance
with capabilities and skill sets [72, 73, 74, 75].

The two core stages of resource management in cloud computing are resource
provisioning and resource scheduling. Cloud resource provisioning is the
process of making virtualized resources available for user allocation. Fig. 2
illustrates the load balancing is categorized as:

Figure 2: Resource Management Categorization

A cloud service provider creates the appropriate number of virtual machines
and distributes them among customers in line with their demands when a user
requests resources from it. In compliance with quality of service (QoS)
criteria, SLA agreements, and resource matching to projected workloads,
resource provisioning is also responsible for providing for user demands.
Scheduling may be used to choose the appropriate activity based on the
necessary quality of service (QoS) requirements.
The storage of data and the hosting of numerous users present significant
security vulnerabilities in the context of the cloud. The user data is now
protected in the cloud by powerful technologies [76, 77, 78]. In the cloud
computing environment, this is becoming more complicated because to the
increased security threats associated with the traffic transmitted through
nodes. For instance, a hacker might introduce malicious software, which in
turn could exploit a flaw and harm or lower the network’s quality of service
[79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92]. In reality, one
hacked cloud user can operate as a possible entry point for a man-in-the-
middle attack (MITM), disrupt all connected users, leak data, utilise the
service excessively, and harm the client’s confidential data [93, 94, 95, 96,
97, 98, 99, 98.99, 100, 101, 102, 103, 104, 105, 106, 107]. Therefore, the
main problem of cloud-based IoT network is the design and construction of a
powerful system that can effectively guarantee security and privacy without
compromising and losing performance. Since lives are at risk, defence against
these attacks is crucial. Controlling user access and keeping an eye on the
entire system may be the most efficient way to solve such issues. To enable
high-performance computing, the security and privacy procedures need to be
examined with efficient resource utilization.
A smart and efficient resource management system is proposed, supported by a
set of defined security rules and a set of resource management strategies, is
required to solve the aforementioned issues in order to increase the system’s
intended security and performance. Collaborative nodes are used to monitor the
many traffic kinds in cloud computing that are integrated to support IoT
devices. The model will control a predetermined number of cloud nodes To
safeguard the network from attackers when a user is logged in, the proposed
component comprises a monitoring agent that assesses and updates the trust
level of connected users. Additionally, a certificate is sent to this user as
a trust authorisation if the trust level is upheld during user access. In
addition, it is suggested that resources be managed in an efficient find
appropriate resources based on SLA, while the computing resource nodes have
finite capacity constraints.
The remainder of the manuscript is structured as follows: A smart resource-
aware management model and other relevant studies in the literature is
presented in Section 2. The whole procedure of trust based access control
mechanism is explained based on SLA and real-time resource status in Section 3
of this article. In Section 4, we go through the highlights of efficiency of
our model, conclusion as well as future scope.

##  2 A Smart Resource-Aware Allocation Model

The architecture of configuring, resource-aware, optimising, allocating and
safeguarding policies for effective resource management is covered in this
section. The layout of our proposed model can be shown in the Fig. 3. The SLA-
formatted descriptions with optimized resource allocation based on an
intelligent resource-aware information manager. This conceptual model is the
primary safeguard that guarantees cloud service providers can fulfil a lot of
demands without going against SLA based on QoS criteria and dynamically
controls the resources features of the user and the workload mentioned needs.
Through the cloud workload management portal (CWMP), cloud consumers attempt
to run the workloads. The procedure of authenticating and authorising cloud
consumers is then completed.

Figure 3: Proposed Architecture

Following authentication, this model requests that the cloud consumer needs
(SLA) be submitted, which the authorised cloud consumer does. They next submit
a request for the availability of a specific resource with the necessary
details for the execution of their task. It analyses the numerous workload
details that the cloud consumer requested before taking the information from
the relevant task. For multi-tenancy, we took into account various cloud
providers that communicate with one another and update their new guidelines
and regulations for cloud resources.
The purpose of Workload Manager is to examine several aspects of a cloud
workload to ascertain whether or not it is feasible to move an application to
the cloud. The various cloud workloads each have their own set of
specifications and characteristics. This study offers guidance for the
execution strategy as well. It is divided into three sub-units: the workload
queue, the workload description, and the bulk of the tasks. Every task that a
cloud consumer submits to be executed is taken into account. In the first
stage, a client requests that a job be executed on resources that can satisfy
the client’s QoS requirements. The resource information collector receives the
assignment from the clients to determine if any resources satisfy the
specified QoS restrictions and provide a profit. The QoS parameters are used
by the Smart RA model service to identify the best, operating resource.

Both the runtime estimator and the profit estimator get in touch with the RIC
and ask for information about each resource’s state in order to calculate
runtime and profit. Both estimators receive information about the state of the
resources once the RIC searches its resource information base. The admission
controller examines the estimated runtime and profit values after runtime and
profit have been calculated in order to identify resources that are suitable
for meeting SLA requirements. Only for divisible jobs, the divisible job
scheduler makes a policy to reduce overall runtime through the job division in
the event that adequate resources cannot be found. whether the newly chosen
resources meet the user’s needs or not. The architectural view of proposed
model is illustrated in Fig. 3.
Numerous users from all around the world submit requests for programme
execution to a resource management unit. These applications may be submitted
in any format imaginable, including those for high-performance computing,
scientific processes, the military, the pharmaceutical or research industry,
internet commerce and marketing, etc. These programmes are split up by the
resource management unit into one or more tasks or components that can run
simultaneously on several virtual machines. A virtual network of virtual
machines with permitted access connections is created when all the virtual
machines executing a component of a shared application need to communicate and
exchange information with one another. This information about permitted
virtual machine links is kept and updated by the resource management unit in a
resource information agent.
The current method demonstrates a generic cloud computing environment and the
function of resource management inside a cloud environment, where cloud
architecture consists of groups of servers connected by router networking
devices to exchange information as needed. Numerous physical servers with the
same or different resource capacities make up each cluster. The resources
might consist of a CPU, RAM, and other things. RAM on the disc, bandwidth,
etc. For the purpose of handling requests from cloud user applications,
virtual machines—which might take the shape of compute, storage, or networking
instances—are hosted on servers. The resource management unit receives
application requests from the cloud users

$(User1,User2,...,Usern)$

for processing at the cloud infrastructure. Applications containing requests
are sent to the Resource Management Unit by different cloud users

$(User1,User2,...,Usern)$

Additionally, it simplifies the management of virtual machines, including
scheduling because of the ineffective resource distribution across virtual
machines, placement, migration, and management of over- or under-loading
scenarios. Additionally, it assists with task management by carrying out the
work of breaking down user applications into smaller components or tasks,
assigning these tasks to specific virtual machines, and scheduling their
execution. The response for the user retrieved via Resource Management Unit is
produced by integrating the output processed tasks of an application. The
new/unknown as well as known attacker virtual machines allow the suggested
methodology to prevent data security threats. Different virtual machines that
are installed on varied servers around the datacenter are given different
duties by the resource management unit. The CPU, memory, disc, and bandwidth
consumption during task execution by various virtual machines are tracked, and
a workload/resource use data store is established and updated on a regular
basis. A machine learning-based workload analyzer is trained and retrained
using the data stored in this data store as input. This workload analyzer is
used to forecast future resource usage on various servers and virtual
machines. In order to effectively manage load, the resource management unit
uses estimated resource usage information to make a variety of proactive
decisions pertaining to load management, such as creating, terminating, or
migrating different virtual machines, handling over/under-load conditions of
servers, etc.

##  3 Trust-based Access Control Mechanism

The storage of data and the hosting of numerous users present significant
security vulnerabilities in the context of the cloud. The user data is now
protected in the cloud by powerful tools [108, 109, 110, 111, 112, 113, 114,
115, 116, 117, 118, 119, 120]. The cloud computing environment is becoming
more complex due to the increased security threats associated with traffic
transmitted over nodes.

Figure 4: Trust-based Secure Mechanism

For illustration, a hacker may use malevolent apps may take advantage of a
vulnerability to harm or decrease network quality of service [121.122, 121].
In actuality, a single hacked node can create a Man-in-the-Middle attack
(MITM) access point, disrupt all connected users, leak data, abuse the
service, and corrupt data in the cloud nodes. A rogue internal user might
launch an MITM attack, which could endanger the cloud network by intercepting,
hacking, injecting, and filtering incoming data from end users. Therefore, the
main problem of a wireless cloud-based IoT network is to design and build a
powerful system that can effectively guarantee security and privacy without
sacrificing and losing speed. Since lives are at risk, defence against these
attacks is crucial. Controlling user access and keeping an eye on the entire
system may be the most efficient way to solve such issues. To enable high
performance computing, the security and privacy procedures need to be
examined. Due to their limited resources, cloud nodes find it extremely
difficult to handle numerous queries at once. In this situation, the computing
performance of the cloud can be significantly reduced. In addition, providers
might not be able to reach the desired performance without effectively
securing access to cloud node resources in an IoT network.
The security system’s implementation can go wrong and cause major problems. In
light of this, it is crucial to choose the security methods to be used, the
necessary modules/elements, and the established performance standards
carefully. To the best of our knowledge, improving a system’s security does
not always entail degrading its speed. For instance, security methods should
be established as a fundamental component of the cloud ecosystem because if
they weren’t, their performance would be affected by assaults like malware,
resource abuse, etc. IoT gadgets and wireless sensors make up the bulk of
cloud systems. The wireless network gives attackers unprecedented flexibility
to interrupt and intercept sensitive data being shared if it is not disguised
and encrypted. However, we provide resource management strategies and security
measures for cloud computing in order to decrease the system’s overall
management costs by taking into consideration diverse and flexible activities.
According to the suggested model’s embodiment, Fig. 4 depicts a full
perspective of a trust-based secure mechanism and performance-efficient load
management model in a cloud environment. By submitting requests to the
resource management unit in the form of various apps, cloud customers are able
to take use of cloud infrastructure services. According to the task
management, these applications are separated into tasks, which are then
allocated to execute on various virtual machines depending on whether the
task’s resource needs are less than or equal to those of the virtual machine,
as managed by the virtual machine management.

The objective of this paper is to manage each new cloud user’s access. It
determines the trust level of such a system access request. The cluster head
then assigns each user a specific authorisation that includes information
about his trust level. The resource allocation process is followed by the
usage of the authorisation. This user doesn’t restart the access process on
his or her subsequent access in that little cell since they already have
authorization, they merely need to present it.

##  4 Conclusion and Future scope

In order to answer the concerns that customers have regarding quality,
efficiency, and maximising the fulfilment of their demands, these new cloud-
based solutions first determine the security and privacy standards that are
now in effect. We thoroughly grasp the significance of secure cloud computing
and resource effectiveness in order to evaluate our ideas. Load balancing
helps to improve workload allocation across various computer resources because
security helps to avoid data leaks and disposals. The planning that is being
considered or suggestion relates to:

  * $\checkmark$

For increased scalability in cloud computing, machine learning algorithms may
be utilised to forecast the incoming data rate or application demands.

  * $\checkmark$

Meta-heuristic optimization can be used to augment some of the models, which
are heuristic optimization models.

  * $\checkmark$

The suggested technique may be utilised to enhance various QoS criteria for
better cloud services, such as elasticity, predictability, security,
performance, SLA violation, and response time.

  * $\checkmark$

LB algorithms that have been developed can be evaluated in the future at
actual processes as Montage, EpiGenomics, CyberShake, LIGO, and SIPHT.

  * $\checkmark$

Other learning paradigms, such as deep learning, learning influenced by
quantum mechanics, capsule networks, etc., can be used to construct the load
balancing models.

  * $\checkmark$

The degree of confidence in cryptographic services is increased by decision-
makers choosing the best cloud service provisioning and using it in practise
there. [1].

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