Intensified Scheduling Algorithm for Virtual Machine Tasks in Cloud Computing

Saranu, K. A.; Jaganathan, Suresh

doi:10.1007/978-81-322-2135-7_31

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…Resource Utilization [45] is a performance statistic or metric that is employed to calculate how well resources and devices are being used. A high price or value for utilization indicates that the service provider makes the utmost profit.…”

Section: Resource Utilization (Ru)mentioning

confidence: 99%

“…These algorithms take 20 chromosomes in the initial population, but instead of generating all these chromosomes randomly, they generate 19 chromosomes randomly, take the last chromosome using First Come First Serve (FCFS) in the GA_FCFS algorithm, and take the last chromosome using Round Robin (RR) in the GA_RR algorithm. Time-shared can be explained as a policy in which multiple jobs are allocated to a processor where the allocated job runs for some period of time before being preempted to allow other jobs to run [45,46]. In other words, time sharing means the central processor is made available to each entity in turn for a set amount of time and that entity is interrupted when the allotted time has elapsed, and the next entity then starts its execution.…”

Section: Hybrid Genetic Algorithmsmentioning

confidence: 99%

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A Genetic Algorithm-Based Virtual Machine Allocation Policy for Load Balancing Using Actual Asymmetric Workload Traces

et al. 2023

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Load balancing is a very important concept in cloud computing. In this work, studies are conducted on workload traces at Los Alamos National Lab (LANL). The jobs in this trace are asymmetric in nature as most of them have small time limit. Two hybrid algorithms, a Genetic Algorithm combined with First Come First Serve (GA_FCFS) and Genetic Algorithm combined with Round Robin (GA_RR), are proposed here. The results obtained are compared with the existing First Come First Serve (FCFS), Round Robin (RR) and Genetic Algorithm (GA). Makespan and Resource Utilization are used for the comparison of results. In terms of Makespan, it is observed that GA_RR outperforms the other methods for all the batch sizes. Although the performance of GA_FCFS is much better than that of the other three well-established algorithms FCFS, RR and GA, it is still worse than that of the GA_RR algorithm for all the cases. GA_RR performs best in terms of Resource Utilization also and GA_FCFS is a close competitor. Overall, GA_RR outperforms all the other algorithms.

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Section: Resource Utilization (Ru)mentioning

confidence: 99%

Section: Hybrid Genetic Algorithmsmentioning

confidence: 99%

A Genetic Algorithm-Based Virtual Machine Allocation Policy for Load Balancing Using Actual Asymmetric Workload Traces

et al. 2023

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“…The BPSO method, which employs an approach-based binary particle swarm optimization method, can be used to plan and balance a large number of heterogeneous virtual machines rapidly and efficiently [19][20][21][22][23][24][25][26][27][28][29]. First, we describe a new BPSO for task scheduling and load balancing in cloud computing.…”

Section: Proposed Systemmentioning

confidence: 99%

Optimization of Load Balancing and Task Scheduling in Cloud Computing Environments Using Artificial Neural Networks-Based Binary Particle Swarm Optimization (BPSO)

Alghamdi

2022

Sustainability

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As more people utilize the cloud, more employment opportunities become available. With constraints such as a limited make-span, a high utilization rate of available resources, minimal execution costs, and a rapid turnaround time for scheduling, this becomes an NP-hard optimization issue. The number of solutions/combinations increases exponentially with the magnitude of the challenge, such as the number of tasks and the number of computing resources, making the task scheduling problem NP-hard. As a result, achieving the optimum scheduling of user tasks is difficult. An intelligent resource allocation system can significantly cut down the costs and waste of resources. For instance, binary particle swarm optimization (BPSO) was created to combat ineffective heuristic approaches. However, the optimal solution will not be produced if these algorithms are not paired with additional heuristic or meta-heuristic algorithms. Due to the high temporal complexity of these algorithms, they are less useful in real-world settings. For the NP problem, the binary variation of PSO is presented for workload scheduling and balancing in cloud computing. Considering the updating and optimization constraints stated in this research, our objective function determines if heterogeneous virtual machines (VMs) Phave the most significant difference in completion time. In conjunction with load balancing, we developed a method for updating the placements of particles. According to the experiment results, the proposed method surpasses existing metaheuristic and heuristic algorithms regarding work scheduling and load balancing. This level of success has been attainable because of the application of Artificial Neural Networks (ANN). ANN has demonstrated promising outcomes in resource distribution. ANN is more accurate and faster than multilayer perceptron networks at predicting targets.

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“…A proficient task scheduling algorithm enhances the system throughput and helps service provider to provide good QoS. To get maximum benefit, scheduling is needed with distribution of load on the available resources [2]. The performance of scheduling algorithm is usually measured in terms of turnaround time, execution time, waiting time, cost etc.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Task Scheduling Algorithm through QoS Parameters for Cloud Computing

Monika¹,

Jindal²

2016

MATEC Web of Conferences

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Abstract. Cloud computing is an incipient innovation which broadly spreads among analysts. It furnishes clients with foundation, stage and programming as enhancement which is easily available by means of web. A cloud is a sort of parallel and conveyed framework comprising of a gathering of virtualized PCs that are utilized to execute various tasks to accomplish good execution time, accomplish due date and usage of its assets. The scheduling issue can be seen as the finding an ideal task of assignments over the accessible arrangement of assets with the goal that we can accomplish the wanted objectives for tasks. This paper presents an optimal algorithm for scheduling tasks to get their waiting time as a QoS parameter. The algorithm is simulated using Cloudsim simulator and experiments are carried out to help clients to make sense of the bottleneck of utilizing no. of virtual machine parallely.

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Intensified Scheduling Algorithm for Virtual Machine Tasks in Cloud Computing

Cited by 10 publications

References 3 publications

A Genetic Algorithm-Based Virtual Machine Allocation Policy for Load Balancing Using Actual Asymmetric Workload Traces

A Genetic Algorithm-Based Virtual Machine Allocation Policy for Load Balancing Using Actual Asymmetric Workload Traces

Optimization of Load Balancing and Task Scheduling in Cloud Computing Environments Using Artificial Neural Networks-Based Binary Particle Swarm Optimization (BPSO)

Optimization of Task Scheduling Algorithm through QoS Parameters for Cloud Computing

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