2. Oil weathering

From the time the oil spreads over the water, under the influence of hydrometeorological conditions (wave, winds, currents, solar radiation, etc.), oil properties (density, viscosity, pour point, etc.), and discharge characteristics (instantaneous, continuous, surface, depth), several processes take place, directly or indirectly related, which disperse the oil and change its properties [13, 14]. These timedependent processes comprise physical, chemical, and biological ones, such as evaporation, dissolution, emulsification, dispersion, etc. (Figure 2). The combination of these processes is called oil weathering. In other words, weathering is a series of chemical and physical changes that cause spilled oil to break down and become heavier than water. Winds, waves, and currents may result in natural dispersion, breaking a slick into droplets which are then distributed throughout the water. These droplets may also result in the creation of a secondary slick or thin film on the surface of the water.

In general, weathering processes can be divided into three categories. Processes such as spreading, evaporation, dissolution, dispersion, and emulsification are rapidly occurring (within hours) and have immediate effects on the oil slick, as shown in Figure 3. Sedimentation, biodegradation and photo-oxidation operate more slowly (within months) and comprise the long-term mechanisms for the breakdown of hydrocarbons in the environment. Sedimentation, stranding, and oil-ice interaction are important processes, under distinct environmental conditions. Evaporation is probably one of the most significant processes that affect the surface

#### Figure 2.

Weathering processes that oil undergoes in the sea [15].

#### Figure 3.

Duration of oil slick processes in the sea.

oil particles, in sea or on coast, and therefore, it will play an important role in the construction of the models presented here.

In the following chart, the vertical axis portrays the physicochemical processes that oil is going through, and the time each one of them requires is depicted in the horizontal axis, whereas the line thickness denotes the most critical phase of each stage.

Natural processes may act to reduce the severity of an oil spill or accelerate the decomposition of spilled oil. More analytically, these natural processes are described below [15]:

• Evaporation occurs when the lighter substances within the oil mixture become vapors and leave the surface of the water. This process leaves behind the heavier components of the oil, which may undergo further weathering or may sink to the ocean floor. For example, spills of lighter refined petroleum-based products such as kerosene and gasoline contain a high proportion of flammable components known as light ends. These may evaporate completely within a few hours, thereby reducing the toxic effects to the environment. Heavier oils leave a thicker, more viscous residue, which may have serious physical and chemical impacts on the environment. Wind, waves, and currents increase both evaporation and natural dispersion.

## Oil Spill Dispersion Forecasting Models DOI: http://dx.doi.org/10.5772/intechopen.81764


**7. Conclusion**

The potential use of Microsoft Excel for digital image processing and analysis was successfully extended to application in biomedical imaging. This potential depends on the ability to easily extract RGB data from a variety of image file formats into Excel which is now established by the use of the RGB2X 1.0 program. However, other resources including some being online may be used in extracting the data and copying into Excel. With the data being available in Excel, the possibilities of accomplishing a wide range of biomedical image processing and analysis in Excel combined with the use of Visual Basic in Applications (VBA) is unlimited. The selection of image processing and analysis methods demonstrated in this paper including the different image enhancement methods only serve as evidence of the enormous possibilities. Other methods would need to be tested in Excel covering a wide range of images captured using different biomedical imaging modalities. This should include using image metrics to compare Excel processed image data with processing based on other software platforms. Also, the limitations of reducing larger images for visualizing as charts in Excel would need to be addressed in

future work. Finally, other aspects of image processing and analysis in Excel such as processing time requirement for completing different image processing tasks and image quality should be compared with other image processing software applications for increased understanding of the potentials and limitations of using Excel for biomedical image processing.


**Environment Data.** We rely on temperature traces retrieved from Deutsche Wetterdienst, which provides hourly temperature data on an open access

platform6 . Based on the coarse granularity of the data, we speed it up by a factor of 2 ∗ 24 ∗ 60, such that each hour is represented by half a minute. Furthermore, we interpolate data linearly in order to retrieve varying data for each second. Whereas this induce more environment changes than to be expected in a real world setup, it allows to shorten the overall experiment duration and to leverage the contribution of accuracy-driven strategies on the QoS. We select all the temperature data of the 31.05.2018 obtained by the weather station in Großenkneten, Lower Saxony. The data is illustrated in gure 8.27.

**Figure 8.27.:** Extracted and prepared Environment Temperature Data from a weather station in Großenkneten, Lower Saxony, during 31.05.2018 - 01.06.2018.

**QoS Cost Functions.** The sensing requirements of cloud services can be driven by dierent qualities. Whereas some cloud services may require a high accuracy without strict time constraints others demands a high accuracy under strong time constraints. While the former focuses on an analysis of the environment, the latter implies a certain reactivity of the IoT application to changes in the environment. Whereas the use case Predictive Maintenance focuses on an analysis, it can not be conclusively answered

6 https://opendata.dwd.de/climate\_environment/CDC/observations\_germany/climate/hourly/

which cost functions are best suited for evaluation. Therefore, two different sets of cost functions are used in this study as introduced in chapter 4. One set is consistent with the previous investigations and consists of a cumulative cost function for resources, time, and accuracy, such that 1 = + + . In the second set, we combine accuracy and time aspects in a cost function. Here, we look at the accuracy of temperature values in time, i.e. how accurate is the temperature data transmitted at a given time, perceived by the cloud application. Therefore, we use only a resource and a time-accuracy cost function, which uses perceived accuracy as input, such that 2 = + . We also introduce a temporal shift metric, which quanties how old the data perceived by the cloud service is at the end time of the experiment. In general, we construct linear cost functions as described in chapter 4.4. We compute the cumulative QoS costs after each experiment. We create a normalized QoS cost function set as described in section 8.6.1. Therefore, we construct a linear cost function for each quality which computes the QoS costs by comparing the measured QoS input metric to its worst value across all candidates. The construction of each cost function is explained below:

• Accuracy: While the internal documentation describes a message interval of 15 s, the resulting accuracy of the measurements depends on the environmental changes. Based on the worst measured accuracy > 0 % and the best accuracy of = 0 %, we construct the cost function as follows:

$$Q\_{Accuracy}(acc\_{exp}) = \frac{acc\_{exp}}{acc\_{worst}}$$

• Timeliness: There is no hard constraint on the timeliness described in documentation. Therefore, we rely on the worst measured value in the candidate sets > 0 s and a best timeliness of = 0 s, such that:

$$Q\_{Timelines}(T\_{exp}) = \frac{T\_{exp}}{T\_{worst}}$$

• Resources: Since the actual productively used system is statically provisioned and not operated on a cloud platform, we could not extract any operating costs for the resources. Therefore, we introduce a cost function, based on the highest average number of instances provided accumulated across all cloud services > 0 instance, and a best average provisioning of = 0 instance. Notably, there can not be 0 costs for resources, since there is at least 1 service instance active, per microservice. This results in the following cost function for a given number of instances :

$$Q\_{Resources}(P\_{exp}) = \frac{P\_{exp}}{P\_{worst}}$$

• Perceived Accuracy: This cost function represents the accuracy of the measured data perceived by the cloud service, therefore considering both, the sensing accuracy and processing delays. Based on the worst measured perceived accuracy > 0 % and the best accuracy of = 0 %, we construct the cost function as follows:

$$Q\_{PercivedAccuracy}(acc\_{exp}^{perciveed}) = \min(1, \frac{acc\_{exp}^{perciveed}}{acc\_{worst}^{perciveed}})$$

**Cloud Service Parametrization.** Based on systematic load tests on the processing service of the connected heating system in a test environment, we obtain an average processing time of 5.8 ms per message with a minor standard deviation of = 0.5 ms. The service exhibits I/O characteristics by waiting for an external system, which results in an average CPU utilization of = 83 % in a fully utilized scenario. We were not able to retrieve data on the dispatching service, the heating control. However, we decide to parameterize it with a lower service time of 2 ms and a CPU share of 1. Since the dispatching service waits for the processing service to serve the messages by conducting REST calls, it experiences wait times. In the initial state, each service has 1 active instance. According to internal documents, heating units send messages at a rate of one every 15 s. Therefore, we are taking this value as the basis for the initial transmission rate. This value is set by each connecting device, whereas each device sends at least one message, before being readjusted by ow control approaches.

**Connectivity Scenario.** Whereas connected heating can be expected to have a low variation in the number of connected heating units, we focus on a recovery scenario, e.g. after an outage of the cloud service. This allows to observe the impact of overload protection approaches and the auto-scalers and the combination of both. We rely on a connectivity pattern illustrated in gure 8.28, which we have extracted from a Wikipedia workload peak. We set the number of connected devices up to 2000.

**Figure 8.28.:** Connectivity Scenario based on a recovery of the IoT system.

**Candidates Parametrization.** Based on the properties of the case study and many experiments on this system, we congure each strategy as follows:

	- Heating Control Service Scaler: Since the heating control service dispatches received messages to the predictive maintenance, it has to wait for its processing. Therefore, we characterize it as a mainly I/O-intensive microservice and congure

a queue-based auto-scaler, which monitors the queue length of the consumed message queue as a metric for the services utilization. We set the lower threshold to 0 messages and the upper threshold to 200 messages.

– Predictive Maintenance Service Scaler: We congure the auto-scaler of the predictive maintenance service with a lower threshold of 20 % and an upper threshold with 30 %.

The cooldown duration after each scaling decision is set for both to 10 s. We set the range of available service replicas to 1 − 50.

=

= ¬

Whereas congestion is evaluated by a congestion observer with a window size 10 observing the message queue:

= ℎ ≥ 100

**Table 8.21.:** Measurements and QoS Costs based on the cost function set 1 for each strategy in the Connected Heating Case Study.

The presented conguration is used across all case study candidates. Whereas the previous investigations in chapter 8.6 concludes, that the QoS characteristics of a coupled setup is strongly aected by the coupling mechanism and the coupled strategies, we rely, if possible, on coupling mechanism which do not utilize QoS costs. This allows us, to evaluate the QoS conformance in consideration of all experiments. Furthermore, we aim to improve the comprehensibility of the case study by selecting a specic strategy, e.g. CEF, to use it as a representative of its runtime management class.

### **8.8.2. Q.7 – QoS Contributions in Overload Situation on the Example of an Industry-Based Cloud Application**

The results of each candidate are summarized in table 8.21 for the QoS cost function set 1 and in table 8.22 for the QoS cost function set 2.

**Table 8.22.:** Measurements and QoS Costs based on the cost function set 2 for each strategy in the Connected Heating Case Study.

**I. Baseline.** A static provisioning without further reconguration leads to a strong overload situation due to the strong increase of heating units without adjusting the capacity. This results in a high message processing delay caused by accumulated messages as shown in Figure 8.29b. Since the transmission rate is not degraded in this setup it collects and transfers measurements every 15 s which leads to a very high sensed accuracy of 1.7 %. However, based on the high message processing delay measured values arrive at the cloud service with a considerable temporal shift. At the end of the experiment, the measurement points are already delayed by 14 min, which strongly aects the perceived accuracy. Figure 8.29a shows the sensed and perceived accuracy for the rst connected heating unit. The perceived accuracy degrades to 15.43 %. In terms of the QoS cost functions, the baseline is in a mid range. In 1 its costs mainly consist of timeliness violations. In 2 its costs are mainly based on the perceived accuracy. Overall, the static provisioning is not suitable for coping with an outage and recovery scenario, since the messaging middleware is lled with over 240 000 messages at the end of the experiment.

**II. Isolated Auto-Scaling:** As shown in gure 8.30 the auto-scalers manage to counteract the strong increase in active heating units by providing resources. However, the provisioning speed of the auto-scalers is challenged by the cloud platform and the provisioning mechanism. A CPU update occurs approximately every 30 s and the provisioning of an instance takes more than 45 s. The congured cooldown duration of 10 s is negligible. A delay due to accumulating messages can not be avoided completely. While messages are delayed by only 4.3 s on average, this has a strong eect on the perceived accuracy. The sensed accuracy, as in the baseline, is 1.7 %, but the perceived accuracy is 9.0 %. Whereas this is a signicant improvement over the baseline, it is achieved on the expense of the provisioning many resources, on average 9.17 instances. In 1, isolated auto-scaling performs very well with a total cost of 1.1, because it is primarily resource based and there are minor time or accuracy costs. However, due to the cost function construction, the total cost is in favor of auto-scaling, since the baseline is penalized with xed resource costs of 0.22. In 2 an opposite eect can be observed and auto-scaling has the worst score of 1.36, since although perceived accuracy leads to low costs, resource costs dominate, since both are equally weighted. Overall, autoscaling is a suitable way to deal with such a scenario. While it is challenged by

**Figure 8.29.:** Baseline. Sensed and perceived environment and message processing delay during the experiment.

the speed in resource provisioning, it eventually manages to provide enough capacity to cope with the load.

**III. Isolated Flow Control:** Figure 8.31 shows the results for CEF. It shows that above a certain number of connected heating units, the pre-congured message interval leads to a load that exceeds the capacity of the cloud service. Therefore, the rst congestion avoidance cycle is triggered at about 6 min. Based on the capacity model of the process, the transmission rate is reduced signicantly and then, when the messaging middleware has recovered, the transmission rate is increased multiplicatively. Since the load increases con-

**Figure 8.30.:** Isolated Auto-Scaling. Measurements during the experiment.

tinuously, a point is reached where the congured rate can not be maintained. The cycles become longer and longer, because the transmission rate has to be degraded more strongly due to the higher number of heating units, while the capacity remains the same. Eventually, with the same number of units, the process will level o at a specic cycle. Due to the increasingly strong degradation of the transmission rate, fewer measurements are performed resulting in sensing intervals which extend several minutes. Overall, the approach leads to a strong reduction of the sensed accuracy, since the environment is only roughly captured by the few measurement points. On average the sensed accuracy is 16.63 %. Since the approach is able to avoid a queueing

delay to a large extent, the average queueing delay is 8.89 s. Due to the cycles, however, it exceeds the queueing delay from auto-scaling. The perceived accuracy for the rst device is 13.8 % and is therefore close to the sensed accuracy. The reason for this is that there is hardly any temporal delay, which means that the measurement points are inaccurate overall, but in time. In 1, CEF has the second best result, with QoS costs of 0.95. These are so low because the time costs are negligible and the accuracy costs are in the middle range. In 2, it reaches a total cost of 0.68, which is also relatively low. The good result is due to the mediocre accuracy at low resource costs. Overall, this approach is suitable for keeping the provisioning costs low. However, the congestion avoidance cycle leads to a certain pattern of perception that can result in not noticing important changes in the environment. Therefore, the low total costs within this experiment should be treated with caution.

**IV. Isolated Accuracy-Driven:** Given a target accuracy, the accuracy driven approach adapts the transmission interval based on the dierence between the current and previous measurement. While the target accuracy is congured to perceive the environment with an accuracy of 1.73 %, the accuracy actually achieved depends on both the environmental changes and the parametrization of the approach. In this experiment, the approach results in a sensed accuracy of 10.6 %. Since the approach potentially reduces the number of measurements, especially in episodes with few environment changes, a lower load on the cloud service can be assumed. However, it still exceeds the cloud services' capacity resulting in a very high queueing delay of 112 s. Nevertheless, there are signicantly fewer messages produced, which is reected in the average transmission interval. While it is 15 s for the baseline, it is 22 s for the accuracy-driven approach. The transmission rate adaptations illustrated in Figure 8.32c show how the approach uses the AIMD scheme to adjust the rate as the environment changes. Figure 8.32a shows that, overall, temperature values are measured — for the rst device — with a high degree of accuracy of 3.96 %. However, they arrive with a delay, resulting in a degraded perceived accuracy of 11.45 %. Using the cost function set 1, the approach results in a total cost of 1.43. This is the worst result, because although the resource costs are low, the time costs are high and the accuracy is only slightly above average. In 2 the approach takes the second highest place, since the perceived accuracy is very high despite the temporal shift. All in all, the ndings from [67] are conrmed in this experiment. It is an approach that can achieve a high accuracy and reduces the number of measurements needed. In contrast

**Figure 8.31.:** Isolated CEF. Measurements during the experiment.

to the other approaches, the focus is not on load and capacity but on cost reduction while maintaining a target accuracy.

**V. Flow Control & Accuracy-Driven:** Figure 8.33 shows the results of a coupling of CEF and ACC. The interaction of the two approaches is shown by the transmission adjustments. In case of sudden reductions, an overload situation has occurred, which activates CEF. Especially towards the end, CEF dominates strongly, because it is a very intensive overload situation. This results in a complex interaction between the approaches, which is primarily determined by the rule-based coupling mechanism. The coupling uses a simple threshold

**Figure 8.32.:** Isolated Accuracy-Driven. Measurements during the experiment.

based on the queue length to determine the overload state. For both ACC and CEF the coupling mechanism is transparent, which means that both approaches can not react to it. The adaptation behavior is generally more unsteady than in isolated CEF or isolated ACC. This is due to the fact that the trigger activates ACC even before CEF considers the overload situation as solved. ACC does not use a multiplier to smoothly recover the transmission rate. Instead, it uses an additive component that causes sudden increases, e.g. at 7.5 min. This sudden increase causes the queue to ll up with messages, resulting in an reactivation of CEF, which tries to recover the transmission rate through the multiplier component. Overall, there is little harmonic eect, which is also expressed in the fact that the message interval is signicantly

reduced by over 15 s compared to an isolated CEF with 37.8 s. This aects the sensed accuracy, which is almost the worst at 24.82 %. However, the perceived accuracy is much better with 14.11 %, because CEF prevents delays and because of the smaller cycles and more frequent short-term overload situations, allowing to sense more evenly distributed data points. Using the cost function set 1 it achieves total costs of 1.18 and is thus in the middle eld, since hardly any time and resource costs are incurred and the total costs are therefore primarily composed of the inaccuracy. In 2 the total cost is 0.89, which also consists of the average perceived accuracy and the static provisioning. All in all, the combination of these two approaches is suitable, unless a constant overload situation — as in this experiment — has to be assumed.

**VI. Auto-Scaling & Accuracy-Driven:** Figure 8.34 shows the results for a concurrent operation of the auto-scalers and ACC. Since ACC reduces the overall load by adjusting the transmission rate, the overload situation is mitigated. As with the isolated ACC, the average message interval is 22 s, which corresponds to a load reduction per device of roughly 30 %. The measured accuracy is 12.8 %, which is a slight degradation compared to the isolated ACC, which achieves 10.6 %. However, it can be assumed that a coupling with auto-scalers does not inuence the behavior of ACC. Therefore, we conclude, that dynamics in monitoring and eecting are responsible for this. Due to the reduction in the load by ACC, fewer resources are required overall, which is shown by an average of 5.76 provisioned instances, compared to 9.71 in isolated auto-scaling. Both share a similar average queueing delay, which dier in its progression. In isolated auto-scaling, the high provisioning results in only slight delays of less than 8 s towards the end of the experiment. Nevertheless, the time delay at the end of the experiment is with 563 s signicantly higher than in coupled operation with only 289 s. Nevertheless, this has hardly any eect on the perceived accuracy, e.g. 14.11 % for coupled operation, while it is 9 % percent for isolated auto-scaling. However, it should be noted that the perceived accuracy depends on the message processing delay, sensed accuracy and actual environmental changes, and the combination of these factors seems to have been advantageous for the auto-scaler in this time-limited experiment. Based on the rst cost function set 1, this coupling is in the upper range with 1.28 and feeds the costs primarily from accuracy and resources. In the set 2, the coupling leads to high costs based on the inferior perceived accuracy. Despite these results, a coupling of these approaches is quite con-

**Figure 8.33.:** CEF & Accuracy-Driven. Measurements during the experiment.

ceivable, since it dynamically reduces the load, yet achieves a high degree of accuracy and can thus act as a supplement to resource provisioning.

**VII. Auto-Scaling & Flow Control:** Figure 8.35 shows the adaptation behavior of the concurrent coupling of the auto-scalers and CEF. Compared to the isolated CEF, congestion avoidance cycles have become shorter due to an increased capacity by the auto-scalers. The capacity estimation of the approach is able to react to it by decreasing the transmission rate to a lesser degree. While the auto-scalers are able to react to the overload situation, their eectiveness is reduced by the high congestion avoidance eciency of

**Figure 8.34.:** Auto-Scaling & Accuracy-Driven. Measurements during the experiment.

CEF. Thus, with an average of 2.8 provisioned instances, signicantly fewer resources are used than in an isolated auto-scaling with 9.17 instances. CEF dominates the runtime management, which is also evident from the average message interval. On average, messages are sent every 20 s, which is only slightly below the average of an isolated operation with 23 s. Nevertheless, this setup provides a high congestion avoidance, since messages do not experience a particularly high delay. Thus, at the end of the experiment the measurement points arrive with a delay of less than one minute, which is a signicant improvement compared to isolated auto-scaling. Resource provisioning via the auto-scalers induce dynamics that reduce the adaptation

quality of CEF. This results in an overall lower measured accuracy of 25 %, while the perceived accuracy is around 18.45 %. Both values are signicantly worse than in the isolated approaches. In isolated auto-scaling, the measured accuracy is very high with 1.75 %, while the perceived accuracy is good at 9 % despite a time oset of almost 9 minutes. Both in cost function set 1 and 2 the strategy results in relatively high QoS costs. The reason for this is an antagonistic coupling eect degrading the adaptation quality of both approaches. However, the temporal shift is small and thus potentially a high perceived accuracy can be achieved in overload situations. As pointed out in the previous investigation in chapter 8.6, CEF dominates in a concurrent coupling. Therefore, it can be assumed that better results can be expected with a rule-based coupling or other ow control candidate.

**VIII. Auto-Scaling & Flow Control & Accuracy-Driven:** Figure 8.36 shows the measurements during the experiment. In contrast to a coupling of CEF and ACC a signicantly higher accuracy of 8.77 % is achieved, which is based on an increased capacity by resource provisioning. The accuracy also exceeds that of the coupling of CEF with auto-scalers, since we observed an antagonistic eect on the accuracy. This is mainly due to the additional coupling with ACC, which has already shown in the coupling with auto-scalers that it is able to reduce the overall load with a moderate loss of accuracy. All in all, this coupling is convincing in multiple dimensions. However, transmission rate adaptations based on CEF result in a strong transmission rate adaptations and a light resource provisioning. The messages experience only minor processing delays, such that the accuracy perceived by the cloud service is very high at 7.98 %. The provisioned resources are with with 2.95 instances in the mideld and slightly above the auto-scaler and CEF coupling. Overall, the coupling achieves low QoS costs in both cost function sets 1 and 2. The reason is, that the sensed and the perceived accuracy is relatively high with only minor resource costs. Therefore we consider this as the most promising approach for runtime managing sensing cloud applications and deem it as a signicant improvement over the other approaches. We assume that there is room for optimization, since, as already observed in the previous experiments, a coupling with CEF dominates auto-scaling.

**Figure 8.35.:** Auto-Scaling & Flow Control. Measurements during the experiment.

#### **8.8.3. Threats to Validity**

The threats to validity of the case study are discussed below.

**Internal validity.** As in the ShapeShifter case study presented in chapter 8.6, we derive the input for the QoS functions by monitoring the adjusted transmission rate, the number of provisioned resources and the queueing delay. In contrast to this investigation, we do consider an environmental model in order to quantify the accuracy degradations by the transmission rate reduction. Since the calculation is based on a provided environment model,

**Figure 8.36.:** Auto-Scaling & Flow Control & Accuracy-Driven. Measurements during the experiment.

we can exclude any inuencing third factor on the accuracy. Furthermore, this case study system consists of multiple cloud services, which make it more challenging to exclude a third factor, e.g. based the additional communication layer, which aects the overall processing rate. This may aect the resource and timeliness cost function by changes in the capacity. However, since the experiments are running over a longer period of time and we execute the experiments on an isolated Kubernetes Cluster, we deem the extent of it as negligible. Nevertheless, we have noticed, that the resulting processing rate is below the expected processing rate.

**External validity.** By preseting a case study, which utilizes environment data to quantify accuracy violations, we expect an increased generalizability for sensing cloud applications. We deem the ndings as relevant for cloud applications, which may experience strong connectivity variations. While the study focuses on overload situations, the approaches examined here and their eects are not limited to these. In particular, the coupling of auto-scaling with accuracy-driven collection approaches shows the possibility of dynamically reacting to changes in the environment and thereby provisioning resources.

**Construct validity.** While the system in the study is based on a real environment, the actual cloud application is minimalistic, with a deterministic response time per message. However, since the interaction of the two cloud services creates dynamics that can also be observed in real systems, we consider the setup sucient to represent such an application. In addition, as in previous investigations in this thesis, the smart devices do not experience latencies, which means that approaches that adjust the transmission rate have an advantage over auto-scaling. We assume that the system would react more slowly with latencies, without negating its fundamental eect. Whereas many smart devices are connected in wireless networks, the heating units are installed using the cable connection of the customers home. Since the customers are regionally distributed in Germany, we assume that there is only a minor latency based on the distance and the stable network. Furthermore, we have utilized a single environment model for all smart devices, which does not hold true in practice. Since the smart devices connect to a dierent point of time, they perceive the environment with a dierent accuracy.

**Reliability validity.** We assume that the experimental design of the case study has been described in sucient detail. Therefore we expect the results to be reproducible.

#### **8.8.4. Discussion**

This chapter presented a case study based on an industry-relevant cloud solution. This involved intelligent heating control units that continuously transfer measured values over messaging middlewares to a cloud service. Based on the thesis' focus on overload protecting approaches, we investigated

an outage and recovery scenario. We investigated auto-scaling, a congestion avoiding ow control and nally an accuracy driven collection approach. Each approach was deployed in isolation and in coupling with the other approaches. We were able to show that a coupling of all three approaches yields the most benets, since it keeps timeliness violations to a minimum and is able to signicantly reduce the overall load with a comparatively low loss of accuracy. Each approach on its own has clear disadvantages. Runtime management with CEF leads to strong cycles, which result in erratic sampling. ACC does not consider time aspects and is therefore unsuitable for overload situations, but can signicantly reduce the total load depending on changes in the environment through dynamic adaptation. Popular autoscaling techniques are too sluggish to cope with the rapidly increasing load, which ultimately leads to overprovisioning with a high time violation. We consider the ndings of the case study to be meaningful enough to be valid for other sensing cloud applications.

## **8.9. Characteristics of TCP-inspired Flow Control**

In this section, we investigate the characteristics of the TIF AIMD in a distributed setup. We focus on the AIMD scheme based on its superior e ciency and fairness in distributed setups compared to other increase/decrease schemes [22]. We conduct the investigation on the Bosch IoT Cloud using the ShapeShifter case study.

#### **8.9.1. Experimental Design**

We provision the microservice statically and let the smart devices connect over time using a connectivity pattern. We capture the fairness and obtained transmission rate. We compare the results of a distributed and centralized setup. We adapt the ShapeShifter-IoT case study to propagate the binary congestion state to smart devices. Each smart device utilizes a dedicated AIMD-based request scheme. Each smart device receives the congestion state after transmitting data by piggy-backing.

**AIMD Scheme.** The AIMD scheme is congured with the additive increase parameter = 0.1 and the multiplicative decrease parameter = 0.9. The transmission rate adaptation is limited to a minimum of 0.2 to ensure that an adaptation step occurs within a foreseeable period of time in a severe overload situation.

**Congestion Observer.** The congestion observer to monitors the queue length with a moving average size of 10 and deems the cloud solution as overloaded if a threshold of 10 is exceeded. The congestion observer retrieves every second an update from the infrastructure.

**Connectivity Scenario.** We rely on a connectivity pattern, which has an increase, a steady and a decrease phase, ranging from 0 to 12 devices.

**Fairness.** We measure the fairness in a distributed setup by applying Jain's Fairness Index, which is dened in [43] and is applicable to any resource sharing problem. The index is bounded between 0 and 1, whereas 1 means absolute fairness.

**Supported Transmission Rate.** In order to illustrate the quality of adaption decisions, we compare the transmission rate adaptations to a theoretical supported transmission rate. Since we control the processing rate of the cloud solution, it is not subject to strong variations resulting in a nearly deterministic processing rate . The optimal transmission rate is based on the processing rate, the number of devices and an upper boundary as follows: = ( ,)

**Experiment Setup.** We set the experimental duration to 10 minutes and set a xed processing rate of 10.0 and an initial transmission rate and upper boundary of = = 1.0 .

**Figure 8.37.:**Adaptation behavior for each smart device in a distributed setup. It achieves fairness in a steady state and converges to the supported transmission rate.

#### **8.9.2. Q.8.1 – How does varying load aect the fairness of adaptations in a distributed setup?**

Figure 8.37 shows the transmission rate of each smart device during the experiment. Figure 8.38 shows the achieved fairness at each point of time and on average during the experiment.

With an average fairness of 0.95, the AIMD scheme maintains its eciency and fairness characteristics in distributed setups. The results show, that the approach is vulnerable to a changing number of connected devices, especially if the number of devices is increasing. Since a smart device starts with a certain initial transmission rate, it requires some round-trips to let itself and the other devices converge towards a fair share of the processing capacity of the cloud application. If the number of connected devices remains steady, the fairness converges quickly towards 1.0, demonstrating absolute fairness.

**Figure 8.38.:** Average and current Jain's Fairness Index. The fairness increases greatly for a steady state with a xed number of devices. The fairness is especially vulnerable to a changing —and especially increasing— number of connected devices.

### **8.9.3. Q.8.2 – How does a distributed setup aect the adaptation quality?**

Figure 8.40 shows the adapted transmission rate, the supported transmission rate and the number of connected devices during the experiment in a centralized setup. Figure 8.39 shows the adaptation behavior in a distributed setup.

**Centralized Setup.** The approach achieves an average service utilization of 65 %, a queueing delay of 6.2 secs and a queue length of 24.5 messages. The service utilization is strongly degraded by a substantially lower initial transmission rate value compared to the processing rate of the cloud application, resulting in the need of many round-trips to converge to this value. However, this eect is mitigated by the increasing number of devices. In the steady state in the middle section of the experiment it achieves an average service utilization of 87.1 %.

**Distributed Setup.** The approach achieves an average service utilization of 70.1 % during the experiment and 84.0 % in a steady state. However, by the

**Figure 8.39.:** Adaptation behavior and number of the connected devices during the experimental run in a distributed setup.

**Figure 8.40.:** Adaptation behavior and number of the connected devices during the experimental run in a centralized setup.

degraded adaptation quality, congestions are on average more severe, which is reected by an average queueing delay of 6.9 secs and an average queue length of 32.3 messages.

**Discussion.** Since each smart device adapts itself its transmission rate eventually converges to the supported transmission rate of the cloud application. Connecting devices start with the precongured initial transmission rate and require some round trips to achieve a fair send rate in accordance to already connected devices. This degrades the overall quality of the adaptation. The experiment shows, that the distributed approach is able to let the transmission rate adaptation of the smart devices converge towards a fair share of the processing rate of the cloud application. However, compared to the centralized approach, it results in increased amplitudes of the saw-tooth like adaptation behavior, especially for a changing number of connected devices.

#### **8.9.4. Discussion**

The investigation has shown the applicability of the AIMD scheme in distributed setups for congestion-avoiding cloud applications. However, a distributed setup is associated with a quality reduction of the adaptations. A distributed setup can be considered for smart devices utilizing a wireless sensor link, such that a high frequency is associated with costs. In this case, the available energy level can be used for reconsidering adaptation decisions. As presented in [67], devices can adapt the transmission rate based on changes in the environment, such that the resolution increases if there are many changes in the environment or decrease if there is no change.

# **9. Related Work**

This section presents works related to the contributions of the thesis.

## **9.1. Performance Metrics for Scaling Decisions**

In this section we discuss works related to auto-scalers and performance metrics. To the best of our knowledge there is no work which evaluates performance metrics for scaling decisions in relation to the microservice characteristics. First, we introduce works related to the evaluation of auto-scalers. Then, we present works related to the evaluation of performance metrics. Finally, we present auto-scalers which are closely related to the context of the evaluation, e.g. by addressing the challenge of scaling microservices with varying resource characteristics.

#### **9.1.1. Evaluation of Auto-Scalers**

PEAS [56] is an auto-scaling evaluation framework based on the queuing theory. The presented model abstracts cloud applications as a G/G/N stable queue, which capacity is parameterized by a rate based on the average number of requests per time unit that a service instance can handle with an acceptable service time. The proposed model is limited to single service applications and does not allow to optimize auto-scalers considering a complex interaction of services. We have adopted the presented model for the evaluation of performance metrics and rened it to represent single-service Cloud-IoT applications. A queueing theory based model for multi-service applications could be part of future work, e.g. by integrating it with queueing petri nets simulations, as introduced by Kounev [48].

SimuLizar [10] is a performance analysis tool which supports software architects in the design of elastic software systems. However, SimuLizar lacks the

capability to model an asynchronous communication via messages queues [47]. Furthermore, it relies on stochastic arrival patterns to induce workload on the system, requiring an extension to support smart device as message inducing and adaptable entities. Therefore, it is in its current state not suitable to evaluate auto-scaling decisions based on message queues.

### **9.1.2. Evaluation of Performance Metrics**

Dickel et al. [25] present an evaluation of auto-scaling metrics for scaling stateful IoT gateways. The evaluated metrics are the CPU utilization, the number of concurrent connections and the throughput per gateway. They concluded, that each of the metric on its one is faced with limitations: the CPU utilization does not consider the workload based on the number of concurrent clients and their messaging rate, the number of connected clients does not consider the workload per connection and the throughput per gateway does not consider the number of connected clients. The evaluation lacks elasticity measures to quantify the dierence. However, they state, that auto-scaling based on service demands outperforms the CPU utilization. Overall, we deem the work as closely related, since it evaluates performance metrics for scaling decisions. However, whereas it evaluates metrics for auto-scaling, it focuses on CPU-bound (stateful) IoT gateways whereas this evaluation focuses on stateless microservices with varying resource characteristics.

Rao et al. [61] presents a fuzzy control-based resource allocation for cloud applications. They state as one of the challenges the non-uniformity of cloud resources, which means, that users can experience variations in the capacity of the application based on the cloud environment. Based on a E-Commerce benchmark, they measured for a CPU utilization of 80 % variations in the response time up to 150 %. They conclude, that the CPU utilization can not readily translated to application-level performance.

#### **9.1.3. Related Auto-Scalers**

In this section, we briey present auto-scalers which overcome the limitations of relying on a specic performance metric, e.g. the CPU limitation.

Control-Theory based works, as presented in [61] or [58] rely on heterogeneous monitoring metrics for scaling decisions since the eectiveness of

controllers strong relates to the values of the control parameters. Thresholdbased rules approaches, as presented by Bauer et al. [8], address the challenge by relying on a service demand estimation. The service demand denotes the time a unit of work spends on a specic resource, e.g. CPU, and is statistically estimated. They compare the performance to a CPU-based auto-scaler and evaluate, that it achieves a higher degree of elasticity and SLO conformance. The ndings support the results, since an auto-scaler based on the service demand is not faced with the same limitations as on the CPU utilization. Khaleg et al. [1] discuss the ndings of the performance metric evaluation in this thesis and present an auto-scaling approach which is agnostic towards the microservice characteristics. It consists of a controller, which monitors the QoS of microservices in terms of the response time in order to recalibrate the thresholds through an optimization technique. Based on the characteristics of the microservice they deploy a memory, a CPU and a queue length based auto-scaler.

## **9.2. Feedback Control of Smart Devices**

In this section we discuss related work to the ow control of smart devices. First, we discuss feedback control approaches which provide overload protection by congestion control. Then, we present approaches which adjust the collection behavior of smart devices for other non-functional qualities of IoT applications.

#### **9.2.1. Congestion Control**

A model for congestion control in IoT is presented by Huang et al. [40] with the goal to maintain service access. The model aims to cope with the limited amount of resources in IoT, including network bandwidth and service capacities. It is based on an improved Random Early Discard (RED) algorithm is employed to guarantee a fast response time of the IoT services. The system is modeled as a queue, in which packages are dropped at a certain probability if the queue length exceeds a threshold. Furthermore, the arrival rate will be reduced, which resembles an implicit feedback control. Therefore, the approach aim to maintain the service access by dropping packets. In contrast to the approaches presented in this thesis, it aims to capture bottlenecks

including network bandwidth, and does not make any assumption about the services capacity. By using an implicit performance model in our approaches, we aim to fairly share the capacity across the smart devices without dropping messages. Future work may compare the presented approach with ours based on the impact on the data quality.

Nylander et al. proposed control-theoretical approaches for feedback control in [54] and [53]. In [54] they focus on combining the actions of a load balancer with user experience degradation techniques, in order to handle capacity shortages and achieve a predictable response time. In [53] they rene the degradation controller to additionally provide formal guarantees. A loadbalancing controller decides based on the response time of services, if the request should be degraded. Whereas we consider this approach as promising to control congestions by data quality reductions we deem the imposed loadbalancing architecture as a challenge to its feasibility. The reason is, that we do not consider load-balancing as a concern of the service operator, since it is fully managed by cloud platforms, e.g. CloudFoundry or Kubernetes.

#### **9.2.2. Collection Strategies**

Siris et al. [67] present approaches to eciently collect IoT data while achieving a target data accuracy, response time, energy and privacy protection. The context of this work is an IoT platform, which collects data from smart devices in specic intervals. The accuracy-driven approach maintains a target accuracy and adapts the measurement rate based on changes in the data, whereas the time-driven approach adjust the time period between measurement requests in order to ensure, that the elapsed time to the last measurement is below a specic threshold. The energy-driven strategy adjusts the measurement interval with accuracy and energy cost considerations and the privacy-driven strategy adds noise to measurements. The results show, that AIMD adaptations of the measurement periods are robust to dierent types of measurements. The approaches are closely related to the work of the thesis by utilizing AIMD schemes but dier in goals. In contrast to our work, the proposed time-driven strategy aims to cope with network delays in order to maintain the timeliness of data, whereas our approaches aim to cope with capacity shortages.

Based on the battery limitations of many smart devices energy-eciency is a concern of collection strategies. Ogawa et al. [55] proposes a measurement

rate reduction of eld sensing devices in dependency to detected conditions. The approach proposed by Nolan et al. [52] aims to extend the operating lifetime of power-constrained devices by an adaptive messaging rate, to minimize the energy-intensive transmission of data via a wireless link. Whereas the rst approach reduces the load on the network and the second approach increases the operating lifetime both do not consider the state of the processing application.

Sato et al. [64] present a feedback control theory based model of real-time IoT applications, which represents smart devices as controlled objects. They introduce a smart station, which noties a crowd to space evenly by controlling the number of people which have to leave. They analyze the functional performance of the controller using numerical analysis. Whereas the modeling technique is focused on functional capabilities it can be rened to support performance-based goals, e.g. transmission rate adaptations based on the current accuracy.

# **9.3. QoS Optimization of Cloud Applications**

Bauer et al. [9] present a systematic search approach for distributed cloud services. By searching for minimal resource congurations in terms of the resource costs for a given load intensity they create a Pareto set. A resource conguration is considered as ecient, if it is able to meet time-based SLOs. However, in order to be applicable to sensing cloud applications, the load intensity should emerge from the number of active smart devices and their transmission rate conguration. Therefore the search problem has to be extended in a dimension with the number of devices in order to obtain the optimal resource conguration in a specic connectivity scenario.

An IoT application model and a back tracking approach is presented by Brogi and Forti [14] to support a QoS-aware deployment of IoT applications on fog infrastructures. First, they introduce QoS proles, which captures latency and bandwidth features. Then, they model fog infrastructures, consisting of things, nodes, cloud data centers and communication links. Finally, they model the IoT application as a triplet of things, software components and infrastructure. Based on the model they introduce a search which consists of a pre-processing and backtracking search phase to nd an eligible deployment. Therefore, the

approach focuses on the deployment in the design-phase without considering the elasticity of services or smart device recongurations.

Palladio [11] is a method to analyze the QoS properties of component-based applications based on an application model with a focus on performance and reliability. It is extensively used in research and provides a rich tool-support for performance analysts. Klinaku et al. [47] conduct a case study to assess the applicability of Palladio for microservices and cloud technologies. They conclude, that Palladio is able to provide acceptable prediction results for microservices in cloud environments but is in its current state not feasible for elasticity and cost-eciency analysis.

## **9.4. Frameworks for Self-Adaptive Systems**

In the following we present frameworks for self-adaptive systems which are closely related to SEIA.

Garlan et al. [31] present Rainbow, a highly adopted framework for selfadaptive systems, which has been extensively evaluated in terms of eectiveness [20] and robustness [15]. Rainbow can be considered as a reference implementation of the MAPE-K loop and provides functionalities to be extended. However, it aims to support web applications and lacks concepts of cloud applications. We have adopted the graph based representation of applications in our application model. Therefore, we consider the software architecture model and the probe and eector bindings of SEIA as transferable to Rainbow.

HOGNA is a platform for self-adaptive applications in cloud environments presented by Barna et al. [3]. In its core it is a MAPE-K loop, which provides out-of-the-box implementations, e.g. a performance model, tailored to web applications. Besides, it has deployment support based on a topology description. Whereas it aims to provide a platform for the prototype creation and evaluation of adaptive feedback loops, it is tailored to web applications deployed in cloud environments. Therefore it does not provide an application model with eectors and probes to to manage sensing cloud applications.

# **10. Conclusion**

In this chapter the thesis is concluded. First, we summarize the contributions of this work in section 10.1. Section 10.2 discusses the benets of the presented approaches and section 10.3 the assumptions and limitations. In section 10.4 we outline future work.

## **10.1. Summary**

This thesis presented QoS cost functions and time-driven ow control approaches to improve the runtime management of sensing cloud applications. The contributions of this thesis address the Research Questions (RQs) discussed in section 1.4. In the following we summarize the contributions:

**C1:** Evaluation of the impact of capacity and resource demand variations on scaling decisions. We obtained insight in selecting performance metrics by evaluating the inuence of microservice characteristic variations on the elasticity of threshold-based rules auto-scaler (RQ 1). The elasticity of CPU-based auto-scalers is greatly decreased if the CPU share of processing messages decreases, e.g. due to an increased wait time or computation capacity. Queue-based auto-scalers outperforms CPU-based auto-scalers for scaling microservices with varying wait time. Additionally, the presented model allows to predict the achieved elasticity of an auto-scaler based on the CPU or message queue metrics.

We have shown that thresholds based on message queue metrics are robust towards resource demand and capacity variations. The elasticity of CPU-based auto-scalers is greatly decreased if the CPU share of processing messages decreases, e.g. due to an increased wait time or computation capacity. We have shown, that these result in drastic changes of the SLO conformance for the CPU utilization.

**C2:** Design of QoS cost functions for sensing cloud applications and a QoS optimization framework. The proposed QoS cost functions are able to capture the eect of resource and transmission rate recongurations on the qualities of sensing cloud applications (RQ 2). The presented optimization framework allows to integrate a simulation model and optimization method to search for runtime or resource conguration candidates to minimize the cumulative QoS costs.

The presented QoS cost functions aim to be pragmatic, such that a service operator can construct them based on a SLA. A set of cost function can be used to search for an optimal conguration at design time. Based on the availability of most of the presented QoS input metrics in cloud enviroments, e.g. by probing message broker or cloud platforms, the QoS cost functions can also be used for decision making at runtime. Furthemore, we have instantiated the optimization framework using the proposed QoS cost functions to evaluate the presented ow control approaches in case studies.

**C3:** Development of ow control approaches to overload protect sensing cloud applications. The developed ow control approaches address overload situations by adjusting the transmission rate. They aim to maintain the QoS by improving the timeliness on the expense of accuracy. Therefore, they are a complementary runtime mechanism to auto-scaling, which maintains the QoS on the expense of resource costs. The evaluation on the example of a sensing cloud application shows, that the approaches are able to maintain a QoS with varying overload intensities with a comparable performance to auto-scalers (RQ 3).

We presented an approaches based on the congestion avoidance mechanisms of TCP (TIF) and rened them with a capacity-estimation module (CEF). We evaluated that both approaches are able to cope with capacity shortages by transmission rate adjustments. However, they do probe the capacity by inducing minor congestions of an intensity based on the conguration of the approach. We evaluated, that both approaches are ecient enough to perform similar to auto-scalers. Thus they provide a complementary mechanism to auto-scaling to cope with capacity shortages by reducing the load to a degree which avoids congestions.

**C4:** Design of rule-based coupling approaches to combine ow control with auto-scaling. We present rule-based coupling approaches for coupling ow control approaches with auto-scaling. This allows to leverage the mechanisms of auto-scaling and ow control in managing sensing cloud applications. We evaluate, that a coupling is able to improve the QoS conformance (RQ 4).

We proposed rule-set based couplings mechanisms to combine auto-scalers with ow control approaches. Overall, we evaluated three techniques: concurrent, QoS-based rules and fuzzy rules coupling. The eciency of a concurrent coupling emerges from the coupled strategies, which have to be tuned carefully. In our case study, it has shown to be benecial to use a rule set to decide which strategy to activate. The QoS-based rules have shown the highest QoS conformance but depend on heavy optimization to properly congure the thresholds. The fuzzy rules are able to stabilize the the QoS conformance in all application scenarios and provide a reusable set of rules. Especially for sensing cloud applications with non-linear cost functions a coupling has resulted in a substantially improved QoS conformance.

**C5:** Design of a sensing cloud application model for self-adaptive systems. We present sensing cloud application model for self-adaptive systems consisting of the cloud topology and eectors and probes. This model enables to design runtime management approaches.

We instantiated the application model with the SEIA framework to conduct experiments on case study systems to empirically answer RQs in multiple publications [32], [33], [34]. Therefore, we demonstrated the adequacy of the abstraction level of the application model by instantiating it for multiple cloud applications architectures in heterogeneous environments (RQ 5). By presenting an application model which contains relevant components of cloud applications, e.g. microservices and message queues, we enable a service operator to design runtime strategies with transmission rate and resource conguration adjustments.

# **10.2. Benefits**

The contributions of this thesis enable service operators to improve the QoS conformance of sensing cloud applications by selecting and conguring runtime management approaches based on a set of QoS cost functions. In the following we describe the benets of the work from the perspective of a service operator.

The QoS cost functions enable a service operator to capture the impact of transmission rate and resource congurations on the QoS of sensing cloud applications. Therefore, it enables to optimize the transmission rate and resource conguration in terms of the cumulative QoS costs in design time. It also enables to create Pareto curves to analyze the trade-os in resource conguration costs and achievable data quality. Additionally, the conguration of runtime management approaches can be optimized in design time in respect to an application scenario.

The advantages of the ow control approaches are to provide another runtime tool to cope with capacity shortages or to fully utilize the available capacity. They enable a service operator to maintain the QoS in scenarios in which auto-scalers face limitations, e.g. based on economical or resource constraints. Sensing cloud applications may exhibit cost functions which are non-linear, which makes a combination of ow control and auto-scaling benecial for the QoS conformance, by enabling to cope with capacity shortages with a combination of both. Furthermore, they can be applied to fully utilize the available capacity, e.g. if the cloud application is accuracy-driven.

The presented framework SEIA reduces the eort to prototypical develop runtime management approaches and deploy it on heterogeneous environments. With the application model, probes and eectors it enables a tool set to design complex runtime strategies. The framework provides default implementations for adaptation logic, e.g. threshold-based rules auto-scalers, and for cloud environments, e.g. CloudFoundry and Kubernetes, and can be extended by other developers.

## **10.3. Assumptions and Limitations**

In the following, we discuss assumptions and limitations of the contributions.

**Performance Metrics Evaluation.** The evaluation of the performance metrics assumed microservices which are stateless. Therefore, the analytical and the simulation model are based on this assumption. This limits the results of the evaluation to scaling stateless microservices only. Furthermore, we assumed microservices which consume messages of a single message queue. Therefore the evaluation of scaling decisions based on queueing metrics is limited to this architectural design. Furthermore, we have abstracted the messages in terms of CPU and wait time. The model and the evaluation assumes, that only homogeneous messages are processed, whereas in practice a microservice may process messages of multiple classes.

**QoS Cost Functions.** The presented set of QoS cost functions assume that the smart devices of a sensing cloud application perceive the environment with one sensor. However, IoT applications often require the input of multiple sensors on the smart devices. Additionally, the QoS cost functions assume a continuous transfer of data, whereas in practice smart devices may transfer data on events. Therefore the QoS cost functions are limited to sensing cloud applications with a single data stream.

**Flow Control.** The presented ow control approaches are based on the assumption, that the load on the cloud application only depends on the transmission conguration of the smart devices. Therefore, they are limited to manage sensing cloud applications with smart devices transmitting only one type of messages. Adaptation decisions are based on the assumption, that each message has an equal resource demand. Whereas the ow control approaches are fair in respect of sharing the capacity equally across all smart device, they may not result in fairness in respect to accuracy. The reason is, that the changes in the environment sensed by each smart device can vary greatly and may require a dierent sensing rate to achieve the same accuracy. Therefore, the approaches assume that adjusting the transmission rate of each smart devices results in a comparable change of the accuracy. The presented approaches are limited to continuous message transfers and are not suitable to manage event-based message transfers with unpredictable workloads. Furthermore, they heavily rely on message queues to recognize congestions or estimate the capacity of the cloud service. Therefore, they impose the architectural restriction, that the cloud service processes messages by consuming a message queue.

**SEIA.** The presented framework is able to create and operate runtime strategies for sensing cloud applications. A limitation of the framework is the lack of an automated deployment of a cloud topology. In the current state, a service operator has to provide an application model to the framework and bind it to a deployed cloud application, which induce additional eort.

## **10.4. Future Work**

In the following we discuss the future work categorized in identied areas.

**Performance Metrics Evaluation.** The performance metrics evaluation is limited to stateless microservices consuming messages of a message queue. Therefore, the evaluation can be extended to case studies with a more complex workow in order to identify more insight into the metric selection and its impact on the elasticity of auto-scalers.

**Flow Control.** Future ow control approaches could aim to achieve fairness in terms of the accuracy, such that the capacity of a cloud application is shared across the smart devices in a manner, which results in a similar accuracy. Since many IoT applications sense the environment with multiple sensors, it could be benecial to consider multiple transmission rate con gurations per smart device in adaptation decisions. The concern of the presented ow control approaches is to manage the timeliness dimension of sensing cloud applications on the expense of accuracy. Therefore, the approaches can be extended to manage or consider other qualities, e.g. battery or privacy. Furthermore, we have evaluated rules-based coupling mechanisms for ow control approaches with auto-scaling. More sophisticated ow control approaches could be application-aware and make both resource and

transmission rate reconguration decisions. Future work could also focus on integrate existing approaches of resource demand estimation into the ow control approaches, e.g. LibReDe [68]. This could resolve the architectural constraint of relying on a message queue for performance estimation.

**QoS Cost Functions.** The QoS cost functions only consider data from one sensor. In practice, smart devices sense the environment via multiple sensors. Therefore it is reasonable to introduce an aggregation method in order to support to quantify the overall accuracy of a sensing cloud application based on the accuracy of each sensor.

**SEIA Framework.** Future work can ease the development eort by extending the framework with implementations for probing and eecting existing IoT platforms. Additionally, it can be enriched with additional models, e.g. security or privacy, to increase the capabilities of the framework. Since the application model is graph-based it can be benecial to enable a transformation of existing application models to SEIA, such that an IoT application with runtime approaches can be designed in a tool like SimuLizar and then operated via SEIA.

**Validation.** Based on the small case studies systems it can be benecial to validate the contributions on larger case study systems. This can help to identify the shortcomings of the approaches. Additionally, it would help to identify future research directions based on the experienced challenges.

# **Bibliography**

# **A. Appendix**

# **A.1. Publikationsliste**

Im Folgenden sind die im Zuge dieser Arbeit veröentlichten Publikationen aufgeführt:

# **The Karlsruhe Series on Software Design and Quality**

ISSN 1867-0067

Die Bände sind unter www.ksp.kit.edu als PDF frei verfügbar oder als Druckausgabe bestellbar.

#### Band 25 Sebastian Michael Lehrig Efficiently Conducting Quality-of-Service Analyses by Templating Architectural Knowledge. ISBN 978-3-7315-0756-7

#### Band 26 Georg Hinkel

Implicit Incremental Model Analyses and Transformations. ISBN 978-3-7315-0763-5

#### Band 27 Christian Stier

Adaptation-Aware Architecture Modeling and Analysis of Energy Efficiency for Software Systems. ISBN 978-3-7315-0851-9

#### Band 28 Lukas Märtin

Entwurfsoptimierung von selbst-adaptiven Wartungsmechanismen für software-intensive technische Systeme. ISBN 978-3-7315-0852-6

#### Band 29 Axel Busch

Quality-driven Reuse of Model-based Software Architecture Elements. ISBN 978-3-7315-0951-6

#### Band 30 Kiana Busch

An Architecture-based Approach for Change Impact Analysis of Software-intensive Systems. ISBN 978-3-7315-0974-5

### Band 31 Misha Strittmatter

A Reference Structure for Modular Metamodels of Quality-Describing Domain-Specific Modeling Languages. ISBN 978-3-7315-0982-0

#### Band 32 Markus Frank

Model-Based Performance Prediction for Concurrent Software on Multicore Architectures. A Simulation-Based Approach. ISBN 978-3-7315-1146-5

#### Band 33 Manuel Gotin QoS-Based Optimization of Runtime Management of Sensing Cloud Applications. ISBN 978-3-7315-1147-2

# The Karlsruhe Series on Software Design and Quality

**Edited by Prof. Dr. Ralf Reussner**

IoT applications perceive and interact with the environment via smart devices and elastic cloud services. The load on the cloud service is induced by the connected smart devices, which continuously transfer messages containing data about the environmental state. The resource configuration of the cloud service influences its message processing capacity. Therefore, a service operator, who operates an IoT application, is faced with the challenge of configuring the smart devices and the cloud service in a manner, which achieves a high data quality at low operational costs.

This work supports the service operator by introducing cost functions for data qualities, which are influenced by the interplay of smart device and cloud service configurations. These cost functions enable to search for a cost minimal configuration for specific scenarios. Furthermore, this work presents flow control approaches that cope with capacity shortages of cloud services by reducing the message rate of smart devices in a controlled manner. While this can have a negative impact on the accuracy of measurements, it stabilizes processing delays and ensures, that the IoT application remains available in severe overload scenarios. Lastly, this work introduces a coupling approach of flow control of smart devices with automatic resource provisioning of cloud services via auto-scalers.

**33**

**Manuel Gotin**

**QoS-Based Optimization of Runtime Management**

**of Sensing Cloud Applications**

ISSN 1867-0067 ISBN 978-3-7315-1147-2

Gedruckt auf FSC-zertifiziertem Papier 9<PHONE_NUMBER>72


**Part 1**

**Advances in Scientific and Theoretical Modeling Aspects**

**0**

**1**

**Mass Transfer Between Stars:**

This chapter discusses photometric methods of studying mass transfer between stars in close binary star systems, and explains how researchers use the variations in the light received from far away stars to determine the properties of the stars and the nature of mass flow between them. A new photometric method of modelling mass transfer in a certain class of binary stars

Binary systems undergoing mass exchange are said to be *interacting* and are referred to as *interacting binary stars*. The interacting binaries discussed in this chapter are Algol-type binary stars (see Section 2), or similar systems. Considerable attention in the field of interacting binary stars has recently been paid to systems containg compact objects (white dwarfs, neutron stars, black holes, etc.) and has left many systems of more normal stars, such as Algols, neglected. This circumstance actually provides opportinities for researchers to study those systems that have been studied in the past century, but have been neglected over the last couple decades. Some emphasis in this chapter is placed on a certain class of interacting

Basic concepts such as the *light curve* and *ephemeris* determination are introduced in section 3. Section 4 explains how a time-dependent ephemeris can be detected and used to calculate the rate at which an interacting binary's orbital period is changing due to mass transfer between the stars, and also to calculate the rate as which the mass is transferring. The *ephemeris curve* is an important tool in the photometric study of interacting binary stars because it is used to

As the study of interacting binaries continues, we're learning more about their evolution. Interacting binaries can even be used as a sort of laboratory for testing current theories of stellar evolution. Throughout our studies, we've learned that mass transfer between stars can be quite variable. Accretion structures have been detected around the mass-gaining component, and it has been determined that the presence and/or stability of the accretion structures depend on several factors including the size and period of the system's orbit. The mass transfer stream in Algol systems with short orbital periods (about 3 days or less) strikes the accreting star more directly and an accretion disk is not likely to form. On the other hand, stable accretion disks do tend to form in systems with longer orbital periods (greater than about 6 days), where the accretion is not as direct. Systems with intermediate periods can exhibit wild variations, on timescales as short as one orbital cycle or less, due to a variable or transient accretion structure resulting from the system's oscillations between states of direct

**1. Introduction**

is introduced.

binary stars that undergo strange variations in light.

verify that mass transfer is taking place.

**Photometric Studies**

*Kutztown University of Pennsylvania*

Phillip A. Reed

*U.S.A.*


**5. Conclusion**

or mathematical universes that fade in, fade out, and change in volume across time," advises Dr. Burley. "Creating the design can be a process whereby the dots to be connected may not be apparent and the sequence to connect the dots is not apparent either. Some people are not comfortable with such puzzles and others enjoy the challenge," states Dr. Burley. "The sponge city competition typifies such a challenge. It is creating a new set of relationships and

**Figure 11.** A series of 3-D models exploring the three dimensional properties and possibilities in the sponge city design

**Figure 10.** A series of drawings exploring ground plane patterns for the sponge city design. (copyright 2016 Na Li and

Yiwen Xu).

282 Land Use - Assessing the Past, Envisioning the Future

(copyright 2016 Na Li and Yiwen Xu).

standards previously unexplored and untested," reveals Dr. Burley.

Knowledge about water, its management, and use continues to grow and evolve. An analytical approach developed and employed in one part of the world may eventually become important in other parts of the world. This has been the case in the development of non-toxic water-quality estimation methods developed in the USA and being considered elsewhere. In addition, the special needs of selected societies and cultures may necessitate the evolution of new functions and design standards in planning and design. Applications of these standards will continue to evolve and be applied in different manners across the world. In future, some of these relatively newer equations and applications may be included in textbooks and studied by most landscape and civil engineering students.


*Supporting open minds since 2005*

Tendons http://dx.doi.org/10.5772/intechopen.73878 Edited by Hasan Sözen

### Contributors

Rita de Cassia Marqueti, Fabricio Reichert Barin, Ivo Vieira de Sousa Neto, Gracielle Vieira Ramos, Kwang-Il Lee, Kwang-Won Lee, Ju-Woong Jang, Janina Burk, Nazım Karahan, Barış Yılmaz, Murat Kaya, Justin Yousef, Tawheed Ahmad, Summaira Jan, Saima Rashid, Mayur Nayak, Rahul Yadav, Stephanie C. Torres-Ayala, Jorge A. Vidal, Amanda P. Marrero-González, Alvaro Bravo-Martínez, Luis R. Rodriguez-Ortíz, Hasan Sözen

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**3. Enlargement of cell**

cells. Although 2-step deposition [8] (spin-coating PbI2 and dipping in the CH3NH3I solution) was also performed in air, the efficiency was lower compared with that by multiple spincoating, as observed in **Figure 4(b)**. It is believed that the CH3NH3PbI3 phase was embedded inside pores of the mesoporous TiO2 layer during one- or two-time spin-coating. After the inside pores of the mesoporous TiO2 were completely filled with the perovskite compound, only the perovskite layer might be formed on the mesoporous TiO2 layer by four-time spin-

**Figure 7.** (a) High-resolution TEM image and (b) structure model of CH3NH3PbI3. (c) Lattice image of TiO2.

illumination.

The IPCE spectrum of the photovoltaic cell with the TiO2/CH3NH3PbI3/spiro-OMeTAD structure exhibits photoconversion efficiencies between 300 and 800 nm, which nearly agrees with the measured energy gaps of 1.51 eV [37] for the CH3NH3PbI3 compound. This indicates that excitons might be effectively generated in the perovskite compound layers upon light

An energy level diagram of TiO2/CH3NH3PbI3 photovoltaic cells is summarized as shown in **Figure 8(a)**. The electronic charge generation is caused by light irradiation from the FTO substrate side. The TiO2 layer receives the electrons from the CH3NH3PbI3 crystal, and the electrons are carried to the FTO. On the other hand, the holes are carried to the Au electrode through the HTL of spiro-OMeTAD. For these processes, the devices were produced in air,

coating, which would result in the highest efficiency.

224 Nanostructured Solar Cells

Enlargement of the cell area is especially mandatory to enable the use of perovskite devices such as actual commercial solar cell panels [38]. The photovoltaic properties of perovskite-type solar cells with a substrate size of 70 mm × 70 mm were investigated [39].

The photovoltaic devices consisted of a CH3NH3PbI3 compound layer, TiO2 electron transport layers, and spiro-OMeTAD hole-transport layer, prepared by a simple spin-coating technique. The effect of the distance from the center of the cell on conversion efficiency was investigated based on light-induced *J–V* curves and IPCE measurements. A photograph of a perovskite solar cell measuring 70 mm × 70 mm and a schematic illustration of the arrangement of Au electrodes on the substrate are shown in **Figure 9(a)** and **(b)**, respectively.

**Figure 9.** (a) Photograph of perovskite solar cell measuring 70 mm × 70 mm. (b) Schematic illustration of arrangement of Au electrodes on the substrate.

The measured short-circuit current density, open-circuit voltage, fill factor, and photoconversion efficiency of the present TiO2/CH3NH3PbI3 cell as a function of the distance from the center of the cell are shown in **Figure 10**(**a**–**d**), respectively. The highest efficiency was obtained for the electrode at 12.7 mm from the cell center, which provided a photoconversion efficiency of 3.15%, a *VOC* of 0.653 V, a *JSC* of 13.0 mA cm−2, and a *FF* of 0.371. Due to the long diffusion length of exciton [40], the *JSC* values were nearly constant at ~12 mA cm−2 for all electrodes on the solar cell, as observed in **Figure 10(a)**. Although the *FF* value slightly decreased as the distance (*d*) from the center of the cell increased, the deviation was small, as observed in **Figure 9(c)**. On the other hand, the value of *VOC* depended fairly on the *d* values, as observed in **Figure 10(b)**, which led to decreased efficiency, as shown in **Figure 10(d)**. The dependency of *VOC* values on the *d* values might be related to the thickness of CH3NH3PbI3 layer prepared on the large substrate by the spin-coating method. The low *VOC* and *FF* values would be related to the coverage ratio of CH3NH3PbI3 at the TiO2/CH3NH3PbI3 interface, and further multiple spincoating of CH3NH3PbI3 layers on the TiO2 mesoporous layer would improve the coverage of CH3NH3PbI3 on the TiO2 mesoporous layer, which would induce the increase in the conversion efficiency of the solar cells.

IPCE spectra of electrodes at 4.2, 12.7, and 22.8 mm from the cell center are shown in **Figure 10(e)**. All spectra show similar changes on the wavelength, which agrees with the *JSC* results shown in **Figure 9(a)**. The perovskite CH3NH3PbI3 structure showed photoconversion within the whole measurement range of 300–800 nm, which nearly agrees with the reported energy gaps for the CH3NH3PbI3 phase. Control of the energy levels of the conduction band and valence band is important for carrier transport in the cell. The conversion efficiencies obtained for the present cells are lower than the previously reported values. It might be difficult to control the uniformity of the layer thickness and interfacial structure using the spin-coating. In the present work, the samples were prepared in air, which might result in a decrease in the efficiency of the present cells, and perovskite crystals with higher quality and a uniform surface should be prepared in future works.

The photovoltaic devices consisted of a CH3NH3PbI3 compound layer, TiO2 electron transport layers, and spiro-OMeTAD hole-transport layer, prepared by a simple spin-coating technique. The effect of the distance from the center of the cell on conversion efficiency was investigated based on light-induced *J–V* curves and IPCE measurements. A photograph of a perovskite solar cell measuring 70 mm × 70 mm and a schematic illustration of the arrangement of Au

**Figure 9.** (a) Photograph of perovskite solar cell measuring 70 mm × 70 mm. (b) Schematic illustration of arrangement

The measured short-circuit current density, open-circuit voltage, fill factor, and photoconversion efficiency of the present TiO2/CH3NH3PbI3 cell as a function of the distance from the center of the cell are shown in **Figure 10**(**a**–**d**), respectively. The highest efficiency was obtained for the electrode at 12.7 mm from the cell center, which provided a photoconversion efficiency of 3.15%, a *VOC* of 0.653 V, a *JSC* of 13.0 mA cm−2, and a *FF* of 0.371. Due to the long diffusion length of exciton [40], the *JSC* values were nearly constant at ~12 mA cm−2 for all electrodes on the solar cell, as observed in **Figure 10(a)**. Although the *FF* value slightly decreased as the distance (*d*) from the center of the cell increased, the deviation was small, as observed in **Figure 9(c)**. On the other hand, the value of *VOC* depended fairly on the *d* values, as observed in **Figure 10(b)**, which led to decreased efficiency, as shown in **Figure 10(d)**. The dependency of *VOC* values on the *d* values might be related to the thickness of CH3NH3PbI3 layer prepared on the large substrate by the spin-coating method. The low *VOC* and *FF* values would be related to the coverage ratio of CH3NH3PbI3 at the TiO2/CH3NH3PbI3 interface, and further multiple spincoating of CH3NH3PbI3 layers on the TiO2 mesoporous layer would improve the coverage of CH3NH3PbI3 on the TiO2 mesoporous layer, which would induce the increase in the conversion

IPCE spectra of electrodes at 4.2, 12.7, and 22.8 mm from the cell center are shown in **Figure 10(e)**. All spectra show similar changes on the wavelength, which agrees with the *JSC* results shown in **Figure 9(a)**. The perovskite CH3NH3PbI3 structure showed photoconversion within the whole measurement range of 300–800 nm, which nearly agrees with the

electrodes on the substrate are shown in **Figure 9(a)** and **(b)**, respectively.

of Au electrodes on the substrate.

226 Nanostructured Solar Cells

efficiency of the solar cells.

**Figure 10.** Measured (a) short-circuit current density; (b) open-circuit voltage; (c) fill factor and (d) conversion efficiency of TiO2/CH3NH3PbI3 cell as a function of the distance from the center of the cell. (e) IPCE spectra of the same cell.

As a summary, perovskite solar cell devices with a substrate size of 70 mm were produced by a spin-coating method using a mixture solution. The photovoltaic properties of the solar cells and the size effect of the substrate were investigated by *J–V* and IPCE measurements, and the dependency of their conversion efficiency on the distance from the center of the cell was investigated. Nearly constant values of short-circuit current density were obtained over a large area, due to the long exciton diffusion length of the CH3NH3PbI3 compound. The open-circuit voltage fairly depended on the distance from the center of the cell, which led to a change in conversion efficiency. Optimizing the layer thickness and structure would be important for improving the performance of the devices.


**Diagnostic, Prognostic and Predictive Biological Markers in Bladder Cancer Illumination of a Vision 2.0**

Edited by

Thorsten Ecke and Thomas Otto

Printed Edition of the Special Issue Published in *International Journal of Molecular Sciences*

**Diagnostic, Prognostic and Predictive Biological Markers in Bladder Cancer —Illumination of a Vision 2.0**

## **Diagnostic, Prognostic and Predictive Biological Markers in Bladder Cancer —Illumination of a Vision 2.0**

Editors

**Thorsten Ecke Thomas Otto**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

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