opti mization of resource allocation parameters in cloud

Optimization of Resource Allocation Parameters in

Cloud Environment Using Design of Experiments 1Anusha Bamini and 2Sharmini Enoch

1Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education,

Kumaracoil, Kanyakumari District, Tamilnadu, India.

[email protected] 2Department of Electronics Communication Engineering,

Noorul Islam Centre for Higher Education, Kumaracoil, Kanyakumari District,

Tamilnadu, India. [email protected]

Abstract

Cloud computing creates an illusion for cloud users by providing plenty of resources on the basis of pay-per-use concept under Infrastructure as a Service. These resources are allocated virtually based on user request. With the increasing challenges under this heterogeneous environment, it is very difficult to optimally allocate the resources to a job with better QoS and with less time. Significant systematic experiments are being conducted to allocate the virtual resources powerfully and optimally in cloud network. Lot of algorithms are implemented to increase the efficiency of resource allocation in consideration with QoS parameter. In this work we address the problem of optimal resource allocation using MBFO algorithm, and the better influencing parameter is identified using DoE tool. At first Scheduling with resource allocation strategy was improved by applying Modified Bacterial Foraging Optimization(MBFO) algorithm, and the simulation was conducted using CloudSim tool. Compared to various evolutionary algorithms MBFO gives better allocation result, in minimum time without negotiating QoS parameter. The execution parameters we considered are CPU, RAM and bandwidth and the output parameter we analyzed is time, cost and throughput. In this heterogeneous environment input parameter (resources) plays a major role while creating real time cloud network such as amazon, google, etc. But we dont know which input

International Journal of Pure and Applied MathematicsVolume 116 No. 22 2017, 217-232ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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parameter plays a major role for setting real time cloud with optimized result. To identify that the results obtained from the first half of the work was optimized using design of experiments (DoE) tool. This DoE is a tool which is not yet applied in cloud computing environment. So we took it as challenging issue for testing and optimization. Which is used to identify a influencing parameter and we get a optimized model of resource allocation parameters. The test result shows that the CPU utilization and bandwidth are major influencing parameters, whereas time has the insignificant role in optimization. And the P-value of resource optimization is less than 0.0001. So we can suggest MBFO model as the best optimized model, and the real time cloud environment can be created based on this model. Key Words:Resources, tasks, cloudsim, optimization, design of experiments.

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1. Introduction The area of distributed computing has various computing techniques from desktop computing, through Grid computing, and currently to Cloud computing. Cloud computing provides different things to different people. Variety of resources can be accessed from anywhere at any time based on pay as you go mode. Lot of definitions [25] and experiments [27] are conducted to represent cloud scalability and virtualization concept. Cloud computing is novel computing method which provides various services to the customers (IaaS, PaaS, SaaS). In cloud computing the user does not have or own any resources, all the resources are given to the user as a service. At the time of utilizing cloud resources, scheduling of cloud resources place a major role. Because cloud computing consists of lot of resources. Cloud is a group of resources; they are organized with each other. By using virtualization technology the user can use the resources enthusiastically. Without buying any hardware and software cloud computing permit the users to initiate their business or work. The cloud service providers (CSP) giving their resources for rent to the customer. The customer can use and return the resources based on his need. Therefore it provides an elastic application to the user.

Cloud computing is alienated into three types of users. It is i) cloud service providers; ii) cloud customers; iii) end users. The cloud service providers acquire the physical hardware and software resources as data centers and provide a virualization technology. Cloud computing supply lot of services and resources to the customer. The customers use those services as pay and go method. For example any online newspaper uses Amazon EC2 for hosting their web site. Here Amazon EC2 is the cloud computing provider and newspaper is the cloud customer. And newspaper readers are the end-users. During the morning era, number of end-users increases and stack on hosting servers increases. As a result response time of website increases. Here cloud computing providers can increase the number of hosting servers and bandwidth of this website to maintain a certain response time limit. And when the end-users reduce over-allocated servers can be released. The job scheduling [1, 12] and resource allocation [20, 4] polices are implemented, to use the resources efficiently and providing service to the customers.

The services in cloud computing are available in many IT companies like Amazon, Google, Microsoft, Yahoo, Sales force, etc. IT data centers are paying attention on CPU, memory, IO, and network utilization and throughput metrics. In broad, a Service Level Agreement is carried out between the cloud user and the service provider (CSP). Service provider in cloud is accepting service request from the user within the QoS requirement. Optimum resource allocation is necessary for the provider to minimize cost [25]. Therefore we need to use an optimal number of system resources to minimize the overall processing time. This paper discusses the optimal allocation of resources with less time and the best influencing parameters are selected using design of experiments to set the


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cloud environment properly and effectively. The parameters choosed for this work is bandwidth and time.

Design of Experiment (DoE) [21] is used; during the set-up of new manufacturing processes it is consequently significant to know the impact of the variables that are used to achieve optimum performance and to understand the effect on the output of natural variation. With four or more variables, understanding the impact of altering variable values becomes complex, particularly if the effects of interactions between variables is considered. Interaction effects are often more significant to output characteristics than single variable effects. Design of Experiments (DoE) enables these multifaceted circumstances to be unstated, thus gaining an in-depth knowledge of the process. This in turn can express the job to choose the right control variables and permissible ranges for the situation and alteration of those variables. Using DoE methods it is also probable to fully optimize the process and improve the performance. In or work DoE is used to choose the optimized resource value.

The paper is structured in the subsequent format. Section 2 mentions the previous works which are mostly supporting to this work. Section 3 presents the job scheduling structure to explain about the scheduling process. Section 4 presents the concept of proposed MBFO algorithm. Section 5 shows the experimental and simulated results of MBFO algorithm compared with PSO and BFO algorithms. Section 6 describes the optimization of process parameter using DoE, which is used to optimize the results. Section 7 demonstrates experimental results for influencing parameters. And finally section 8 addresses the conclusion.

2. Related Work Resource allocation and task scheduling was presented in the papers [33, 15, 4, 27, 16, 32]. Most of the papers concentrating on reducing the job completion time and reducing the cost of resource utilization. Scheduling all-encompassing systematic tasks [31] on supercomputers and other computing systems are considered by a variety of researchers in the earliest research. In scrupulous, we notice an emergent consideration in [1, 6, 12]. There is a growing attention for resource allocation for efficient workflows happening in cloud computing. K. Liu et al, [14] focus on SLA based service providing for the software as a service application. The work focuses on reducing the time and cost of allocation using SLA agreements. To guarantee SLAs, venture software providers in the industry allocate dedicated virtual machines for all users [7, 2], thus the scheduling response time is calculated for each response. One assumption is that set of cloud customer tasks are submitted in cloud, all with a given rank of demand d for cloud resources, and a pool of virtualized servers, all with different capacity. More number of scheduling algorithms is proposed for distributed computing [20], [30], [29]. Most of the algorithms [31], [32], [33] are applied for cloud scheduling based on its suitability. Goal of the scheduling algorithms are achieving better performance. The concept of


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scheduling the bag-of-task (BoT) application was proposed [16] in agent based scheduling concept. In this paper 14 scheduling concepts are executed concurrently. Based on the size of the task time is allocated for sharing the resources. The proposed elastic resource allocation technique will dynamically allocate and reallocate the resources. The result shows that the BoT was allocated and reallocated efficiently. The precedence constrained scheduling of parallel applications on heterogeneous computing systems (HCSs) was proposed in [17]. This proposes a parallel bi–objective hybrid genetic algorithm to reduce the energy consumption and increase the makespan. The energy consumption was minimized by using a method of dynamic voltage scaling (DVS). Results show that it dominates the previous algorithm in terms of completion time, makespan and energy consumption.

A scheduling algorithm based on berger model [34] was designed to establish the dual fairness constraint in virtualized cloud. The fairness of resource allocation was judged by the application of justice function. The results showed that the user tasks and the fairness were efficiently executed. A Biogeography-Based Optimization (BBO) was proposed to sole binary integer problem in job scheduling through better solution adaptation strategy [35]. In BBO, the GA and ACO strategies were incorporated to generate a new set of solutions, at each iteration; the Mann-Whitney test was performed to evaluate the performance results of BBO algorithm. The results proved that the BBO performance was better than the GA and PSO algorithms. An Improved Genetic Algorithm (IGA) was proposed for job scheduling by speeding up the process of GA [36]. The proposed model has five components such as preprocessing unit, job schedulers, users, and data center and data center manager. The preprocessing unit encoded the attributes into users’ job attribute vector, which included expected instruction count, job deadline and delay cost.

The cloud workflow systems, virtualization, parallel to numerous extra workflow systems, job allocation, scheduling [13, 9, 28, 29] is a significant concept. It explicitly resolves the execution of the system. From [26] workflow scheduling is divided into two types. They are i) best-effort based scheduling ii) QoS constraint based scheduling. Execution time and execution cost was reduced by applying the best effort based scheduling concept. Using this quality of user satisfaction level is increased. The QoS constrained scheduling schedule jobs by considering the QoS parameters. An optimal task scheduling and resource allocation was proposed using Particle Swarm Optimization (PSO) based fitness function [8]. To balance the load the PSO based fitness function was applied to reduce the make span and to maximize the processing capacity. The results showed that the PSO based method resulted in less execution time and cost. Lot of algorithms [18, 11] such as PSO algorithm [10], Genetic algorithm [12] is used for choosing best parameters, which are optimal to the system. In the present work, the first half considers cloudsim as a simulation tool and the BFO algorithm was implemented to get better resource allocation with minimized time compared with previous works. Second objective of this


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work is to calculate the scheduler performance with the factors (CPU utilization, RAM, bandwidth) and their relations using DoE [21]. Design of experiments is employed in the present work for designing the process parameters to statistically analyze the inference. The primary parameter for resource allocation was designed by doing experiments, and the process parameters were optimized. Optimization of the process parameter using robust optimization tools with empirical value is crucial to maximize the performance.

3. Proposed Job Scheduling Structure In job scheduling the various Jobs submitted from different users are independent of each others. In the different jobs submitted from different users are run in different or same virtual machine of physical resources. Each submitted job can be sub divided into large number of tasks. DAG (Directed Acyclic Graph) is used to signify the probable enslavement present among these tasks. Large numbers of users submit their tasks at any time and various requirements. Due to the drawback in the DAG workflow Classification and Regression Tree (CART) [31] is applied in this work. Classification and regression trees are the prediction models for data creation and applied for creating different decision rules quit effectively. Data partition can be applied by using CART models. This partitioning of the data can be expressed graphically as a decision tree. In this work (1) the classification tree is used for selecting the best Virtual Machine (VM), which will exactly satisfy the user needs, (2) Regression tree is applied to decide how many resources can be provided to the user.

In fig. 1 the workflow is structured in the form of CART (Classification and Regression Tree). It was denoted as G=(V, E), where V= {T1,…, Tn}. The number of tasks ‘T’ can be allocated to number of virtual machines (V1, V2,...,Vn). Each single task must be scheduled to single virtual machine. The proposed BFO algorithm optimally schedule the tasks to virtual machine to attain the objective. Task level scheduling have been splitted into three steps,

Step 1: Get the QoS Constraints for İndividual Task. Step 2: Task to VM Assignment. Step 3: Implement Scheduling Algorithm.

Figure 1: Task to VM Allocation


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The objective function of the proposed model minimize the execution time and increase the throughput of task scheduling structure. The execution time is defined as the time taken to complete the given task in a given interval of processing time and schedule. The analytical formula for execution time is defined in Equation (1).

𝐸𝐸𝐸𝐸 = 𝐶𝐶𝐶𝐶𝐶𝐶 × 𝐶𝐶𝐼𝐼𝐼𝐼𝐼𝐼𝑐𝑐𝑐𝑐𝑐𝑐𝐼𝐼𝑐𝑐 × 𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐𝐶𝐶𝑐𝑐𝑡𝑡𝑡𝑡𝑡𝑡 (1)

Where execution timer is represented as 𝐸𝐸𝐸𝐸 with 𝐶𝐶𝐶𝐶𝐶𝐶 denoting the cycles per instruction, 𝐶𝐶𝐼𝐼𝐼𝐼𝐼𝐼𝑐𝑐𝑐𝑐𝑐𝑐𝐼𝐼𝑐𝑐 represents the instruction count based on the number of tasks and the CPU time is represented as 𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐𝐶𝐶𝑐𝑐𝑡𝑡𝑡𝑡𝑡𝑡 .

Instruction count (IC) is inversely proportional to Cycles Per Instruction (CPI) and Clock Time (CT). Second parameter we considered is throughput. Throughput refers to how much data is processed for a given amount of time. It is used to measure the performance of hard drives and RAM, as well as network connection. Throughput (TP) is calculated by equation (2).

𝐸𝐸𝐶𝐶 = 𝐸𝐸𝐸𝐸𝐼𝐼𝑡𝑡𝑠𝑠𝑡𝑡𝐸𝐸𝐸𝐸𝐼𝐼𝑡𝑡𝑠𝑠𝑡𝑡 + 𝐺𝐺𝐸𝐸

× 100 (2)

Where 𝐸𝐸𝐶𝐶 specify the throughput which is obtained by the product of ethernet frame size denoted by EFsize and group of tasks denoted by GT. The next parameter resource utilization is calculated by the number of allotted resources. Allotted resources are minimum to execute the large size of tasks for better utilization. Based on total available resources an allotted resources the throughput is calculated.

𝑅𝑅𝑡𝑡𝐼𝐼𝑐𝑐𝑐𝑐𝑅𝑅𝑐𝑐𝑡𝑡 𝑈𝑈𝑐𝑐𝑡𝑡𝐶𝐶𝑡𝑡𝑠𝑠𝑈𝑈𝑐𝑐𝑡𝑡𝑐𝑐𝐼𝐼 (%) = 𝑁𝑁𝑐𝑐𝑡𝑡𝑁𝑁𝑡𝑡𝑅𝑅 𝑐𝑐𝑜𝑜 𝐴𝐴𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐𝑐𝑐𝑡𝑡𝐴𝐴 𝑅𝑅𝑡𝑡𝐼𝐼𝑐𝑐𝑐𝑐𝑅𝑅𝑐𝑐𝑡𝑡𝐼𝐼𝐸𝐸𝑐𝑐𝑐𝑐𝑈𝑈𝐶𝐶 𝑁𝑁𝑐𝑐𝑡𝑡𝑁𝑁𝑡𝑡𝑅𝑅 𝑐𝑐𝑜𝑜 𝐴𝐴𝐴𝐴𝑈𝑈𝑡𝑡𝐶𝐶𝑈𝑈𝑁𝑁𝐶𝐶𝑡𝑡 𝑅𝑅𝑡𝑡𝐼𝐼𝑐𝑐𝑐𝑐𝑅𝑅𝑐𝑐𝑡𝑡𝐼𝐼

(3)

4. Scheduling Using Modified MBFO Algorithm

Bacterial Foraging Optimization is an evolutionary technique based on E.coli bacteria. Application of group foraging strategy of a swarm of E.coli bacteria in multi-optimal function optimization is the key idea of this algorithm. Bacteria search for nutrients is a manner to maximize energy obtained per unit time. Individual bacterium also communicates with others by sending signals. During foraging of the real bacteria, locomotion is achieved by a set of tensile flagella. Flagella help an E.coli bacterium to tumble or swim, which are two basic operations performed by a bacterium at the time of foraging.

Bacterial Foraging optimization theory is explained by following steps. • Chemotaxis. • Swarming. • Reproduction. • Eliminational-Dispersal.


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The chemo tactic step was described by the equation; ( ) ( )

( ) ( )ii

iin∆∆

∆=φ (4)

Where ( )ni∆ is a n-dimensional randomly generated vector with elements within the following interval: [-1,1]. After that, each bacterium ( )lkji ,,θ (where kj,and l are the chemo tactic, reproduction and elimination-dispersal counters, respectively) modifies its position as indicated in Eq. 5, where ( )iC is the step size for search direction ( )iφ . Eq.5 represents the swim of a bacterium

( ) ( ) ( ) ( )iiClkjlkj ii φθθ +=+ ,,,,1 (5)

The BFO algorithm can be used to allocate the tasks to virtual machines. But some problems may occur at the time of allocation. They are1) Some of the task may not allocated to virtual machines 2) Some tasks allocated to more than one virtual machines, it may waste the resource and time 3) last problem was premature convergence. The proposed MBFO algorithm solve these problems. The algorithm was defined as initialization of input parameters S, Ns, Nc, Nrs, Ned, Ped, C(i). Perform elimination dispersal, reproduction loop steps. Using equation (3) perform chemotatic steps. Using the operation of swim go to next VM by the Eq. (5).

Algorithm: MBFO Algorithm Step 1: Initialize input parameters S, Ns, Nc, Nrs, Ned, Ped, C(i) Step 2: Elimination dispersal loop: l=l+1 Step 3: Reproduction loop: k=k+1 Step 4: Chemotaxis loop: j=j+1

Calculate the chemotaxis step using ( ) ( )( ) ( )ii

iin∆∆

∆=φ

Swim and go to next bacteria using ( ) ( ) ( ) ( )iiClkjlkj ii φθθ +=+ ,,,,1 Step 5: If j<Nc go to step 4.

If bacteria life is not over continue chemotaxis Step 6: Reproduction Sort bacteria chemotaxis parameter C(i) Step 7: Elimination dispersal with probability Ped.

Do the optimization process and search for new location. Otherwise end.

5. Simulation Result of MBFO Compared with PSO and BFO

The cloudsim is used as an experimental tool for proposed BFO algorithm and compared with existing PSO algorithm. The steps of the MBFO algorithm was mentioned in previous algorithm. In this work, bacteria’s are tasks assigned and


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the dimension shows the number of tasks totally waiting. The value assigned to each task is a availability of resources. Each bacteria perform chemo taxis step and swim, tumble to reach best resource. Table. 1 shows the number of resource parameter used and its quantity. Simulation experiment is implemented with 100 virtual machines with 1000 tasks. Totally 10 iterations are calculated for each independent tasks to get optimal result. The parameters execution time, cost and throughput was calculated. We compared these parameters with the PSO algorithm, BFO algorithm and Modified BFO algorithm. The result of comparison between different algorithm improves the execution time. The graph (fig. 2, 3, 4) shows shortest execution time, cost and throughput for 10 iterations. And the resource utilization was analyzed over PSO, BFO and MBFO algorithms in fig. 5.

Table 1: Resource Parameter for Simulation Setup

Figure 2: Execution Time Figure 3: Execution Cost

Figure 4: Throughput Figure 5: Resource Utilization

6. Optimization of Process Parameter Using DoE

Design of Experiment objective is explained in [21]. In our work the process parameters are optimized using the design of experiments, to identify the best parameter. A series of pilot desertification trials were conducted to evaluate the relevance of various process parameters in reducing the time value. After

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finalizing the influential parameters, experiments were conducted as per the design by varying the input parameters to record the outcome, the time value (Table 2). ANOVA model is executed for the resource allocation process and the result is shown in Table 2. The p-value of the ANOVA model was less than 0.0001 it specifies the model we chosen is significant, and specifically the major important concept is the ratio of tasks (factor A) with F- value of 19.82. The parameter CPU (factor B) is also important variable having F-value of 3.47, whereas the other factors such as memory and bandwidth have less influence on the other values.

Table 2: ANOVA Result for Resource Allocation Method Source Sum of Squares df Mean Square F Value P Value

Prob>F Model 2354.61 14 168.19 4.78 0.0011 A-Tasks 697.94 1 697.94 19.82 0.0005 B-CPU 122.21 1 122.21 3.47 0.8659 C-Memory 1.04 1 1.04 0.030 0.0034 D-Bandwidth 423.73 1 423.73 12.04 0.0195 Residual 528.10 15 35.21 Total 2882.71 29

The perturbation plot is used to compare the various parameters to decide which are mostly influencing (Fig. 6). The response value was plotted by varying a single factor greater than its choice while holding the other factors at constant midpoint value. The factor A implies that the time value decreases with decrease in number of tasks. The Fig. 6 shows that the parameters CPU, RAM, bandwidth are influencing best with the time value. When compared to the values CPU utilization and bandwidth are the two most influencing values. The F-value of 4.78 mention that the model is effective. The Model F-value of 0.24% shows that noise may occur. This model is significant by considering the F value is less than 0.0500. If the F value is greater than 0.1000 means the model is not significant. The "Lack of Fit F-value" of 7.43 implies the Lack of Fit is significant. The actual factor final equation is:

Time = +52.32104+5.01625* Tasks+0.5517* CPU+3.56051E-003* Memory -0.099384 * Bandwidth-0.057077* Tasks * CPU-5.13889E-005* Tasks * Memory+0.030486* Tasks * Bandwidth-4.93034E-005* CPU * Memory +2.77237E-004* CPU * Bandwidth-1.54728E-005* Memory * Bandwidth-0.37046* Tasks2-1.31469E-003 * CPU2-2.80206E-008 * Memory 2-4.82448E -004* Bandwidth2

Figure 6: Perturbation Chart for Optimized Parameters

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7. Experimental Results for Influencing Parameters

The goal of the DoE experiment is to test the values of influencing parameters. The number of tasks entered into the system, each task is evaluated with the three parameters CPU utilization, time and bandwidth. These three parameters are compared with each other to find the best influencing value. In our experiment graphs are generated for all combination of the above parameters with number of tasks ranged from 200 to 500. Every possible edge is created for the CART workflow. Occasionally we have several outputs and we have to compromise to attain enviable results. The favoured solution is to consent on a measurement by which opposing choices can be compared to produce a model of data from each alternative, and compare average results. The greatest average result will be our preference.

Figure 7: Surface Response Model of Task Vs CPU Figure 8: Surface Response Model of Task Vs

Memory

Figure 9: Surface Response Model of Task Vs

Bandwidth Figure 10: Surface Response Model of CPU Vs

Memory

Figure 11: Surface Response Model of CPU Vs Bandwidth

Figure 12: Surface Response Model of Memory Vs Bandwidth

The performance metrics are chosen for the comparison of execution time for every job. By assigning all tasks to a processor the sequential execution time is considered. In our simulation graph are generated for all combination of the above parameter. The task versus time of reaction in shown in Fig. 7. When the number of tasks increases the utilization of CPU also increased. Based on the type of job the utilization time is calculated. When compared to other models

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the tasks in CART model reduce the time of execution. Task and memory utilization graph is shown in fig. 8. At the time of executing number of tasks the CPU utilization is also increased. Fig. 9 is plotted with task versus bandwidth. Bandwidth is differed for each system, at the time of execution of a system. In cloud environment also the bandwidth was differed on allocating a virtual machine. Based on the resources choose bandwidth also varied. In Fig. 10 CPU and memory values are plotted. Just the utilization of resources is compared. The Fig. 11 is plotted with CPU and bandwidth. In this the percentage of CPU utilization was compared with the bandwidth. The Fig. 12 shows the result of memory versus bandwidth. From the experimental results shows that the parameters are used efficiently and optimally with less time.

8. Conclusion In this work the first phase of the work is simulated using cloudsim by applying MBFO algorithm. In scheduling and resource allocation, the jobs are executed in various service providers with set of virtual machines. Each virtual machine are having different configuration, with different resources. In this proposed work 100 virtual machines are created to execute 1000 tasks. And the algorithm is executed for 15 times to get optimized result. The final result shows that MBFO outperforms with PSO and BFO with less time, cost and the throughput was increased. From the result of first phase the best optimal parameter is choose using the DoE. The design experiments are valuable for studying, the factors that may affect a product or process is used to analyze the results of such experiments. The analysis of variance result provides the p- value as 0.0011. This shows that our process parameters are optimally influenced with one another. At last the result clearly showed that completion time, bandwidth utilization and CPU utilization are effectively utilized with less time.

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