Multi-Objective Task and Workflow Scheduling Approaches in Cloud Computing: a Comprehensive Review

Hosseinzadeh, Mehdi; Ghafour, Marwan Yassin; Hama, Hawkar Kamaran; Vo, Bay; Khoshnevis, Afsane

doi:10.1007/s10723-020-09533-z

Cited by 63 publications

(40 citation statements)

References 90 publications

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“…(16) According to formula (36), the membership function S of the Gaussian cloud model is calculated. (17) e membership function is used to update the new position of the whale in the swimming process as shown in formula (37). (18) End if (19) End For (20) End For Output: the best F obj and matrix A (Task-VM mapping).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…(9) Calculate the membership function S of the Gaussian cloud model according to formula (36). (10) e membership function is used to update the new position of the whale in the swimming process as shown in formula (37). ( 11 Computational Intelligence and Neuroscience according to the process in Algorithm 3, or spiral search to update the position.…”

Section: Multiobjective Task Scheduling Algorithm-gcwoa Algorithm Based On Gaussian Whale-cloud Optimization Inmentioning

confidence: 99%

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GCWOAS2: Multiobjective Task Scheduling Strategy Based on Gaussian Cloud‐Whale Optimization in Cloud Computing

Sun

et al. 2021

Computational Intelligence and Neuroscience

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An important challenge facing cloud computing is how to correctly and effectively handle and serve millions of users’ requests. Efficient task scheduling in cloud computing can intuitively affect the resource configuration and operating cost of the entire system. However, task and resource scheduling in a cloud computing environment is an NP-hard problem. In this paper, we propose a three-layer scheduling model based on whale-Gaussian cloud. In the second layer of the model, a whale optimization strategy based on the Gaussian cloud model (GCWOAS2) is used for multiobjective task scheduling in a cloud computing which is to minimize the completion time of the task via effectively utilizing the virtual machine resources and to keep the load balancing of each virtual machine, reducing the operating cost of the system. In the GCWOAS2 strategy, an opposition-based learning mechanism is first used to initialize the scheduling strategy to generate the optimal scheduling scheme. Then, an adaptive mobility factor is proposed to dynamically expand the search range. The whale optimization algorithm based on the Gaussian cloud model is proposed to enhance the randomness of search. Finally, a multiobjective task scheduling algorithm based on Gaussian whale-cloud optimization (GCWOA) is presented, so that the entire scheduling strategy can not only expand the search range but also jump out of the local maximum and obtain the global optimal scheduling strategy. Experimental results show that compared with other existing metaheuristic algorithms, our strategy can not only shorten the task completion time but also balance the load of virtual machine resources, and at the same time, it also has a better performance in resource utilization.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Multiobjective Task Scheduling Algorithm-gcwoa Algorithm Based On Gaussian Whale-cloud Optimization Inmentioning

confidence: 99%

GCWOAS2: Multiobjective Task Scheduling Strategy Based on Gaussian Cloud‐Whale Optimization in Cloud Computing

Sun

et al. 2021

Computational Intelligence and Neuroscience

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“…The proposed framework involves developing a task scheduling model based on evaluation factors employed in the multiobjective scheduling approaches for optimizing multiple objectives. As per survey conducted by [3], cost, energy consumption, task completion time, task waiting time, flow time, failure rate, profit, carbon emission, makespan and reliability are the most commonly used evaluation factors in multi objective scheduling approaches. In this work, we focused on the three most common metrics, energy usage, cost and makespan as evaluation factors in the proposed framework using task estimation module.…”

Section: B Task Estimation Modulementioning

confidence: 99%

“…Cloud computing service providers offer software and hardware resources through cloud data centres. In order to meet the increasing demands of cloud computing resources, data centres are equipped with more powerful servers and other related hardware resources [3]. It has been observed that the significant portion of energy consumption is caused by servers in the cloud data centre as depicted in Fig.…”

Section: Introductionmentioning

confidence: 99%

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A Metaheuristic Framework for Dynamic Virtual Machine Allocation With Optimized Task Scheduling in Cloud Data Centers

Alsadie

2021

IEEE Access

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Optimal allocation of virtual machines in a cloud computing environment for user-submitted tasks is a challenging task. Finding an optimal task scheduling solution is considered as NP-hard problem specifically for large task sizes in the cloud environment. The best solution involves scheduling the tasks to virtual machines data centre while minimizing the essential, influential and cost effective parameters such as energy usage, makespan and cost. In this direction, this work presents a metaheuristic framework called MDVMA for dynamic virtual machine allocation with optimized task scheduling in a cloud computing environment. The MDVMA focuses on developing a multi-objective scheduling method using non dominated sorting genetic algorithm (NSGA)-II algorithm-based metaheuristic algorithm for optimizing task scheduling with the aim of minimizing energy usage, makespan and cost simultaneously to provide tradeoff to the cloud service providers as per their requirements. To evaluate the performance of the MDVMA approach, we compared the performances of two different scenarios of benchmark real-world workload data sets using the existing approaches, namely, Artificial Bee Colony (ABC) algorithm, Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) algorithm. Simulation results demonstrate that optimizing task scheduling leads to better overall results in terms of minimizing energy usage, makespan and cost of the cloud data center. Finally, the paper concludes metaheuristic algorithms as a promising method for task scheduling in a cloud computing environment.INDEX TERMS Cloud computing, energy consumption, task scheduling, meta-heuristics algorithm, Optimization.

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An efficient fault tolerance scheme based enhanced firefly optimization for virtual machine placement in cloud computing

Sheeba

Maheswari

2023

Concurrency and Computation

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The virtual machine placement for the highly reliable cloud application is considered as one of the challenging and critical issues. To tackle such an issue, this article proposes the enhanced firefly algorithm based virtual machine placement model. But the migration time of the virtual machine placement is high and to reduce the migration time of the virtual machine placement, this article utilizes the K-means clustering algorithm. In addition, to obtain the optimal cluster for the virtual machine placement, the adaptive particle swarm optimization with the coyote optimization algorithm is employed. The experimental results are conducted for the proposed approach using various measures such as transmission overhead, total execution time, packet size, parallel applications numbers, and virtual machine numbers. The results demonstrate that the proposed method offers improved performance and an optimal virtual machine placement scheme with respect to the various constraint factors. The evaluation exposes that the proposed method offers less execution time when compared to other methods.

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Multi-Objective Task and Workflow Scheduling Approaches in Cloud Computing: a Comprehensive Review

Cited by 63 publications

References 90 publications

GCWOAS2: Multiobjective Task Scheduling Strategy Based on Gaussian Cloud‐Whale Optimization in Cloud Computing

GCWOAS2: Multiobjective Task Scheduling Strategy Based on Gaussian Cloud‐Whale Optimization in Cloud Computing

A Metaheuristic Framework for Dynamic Virtual Machine Allocation With Optimized Task Scheduling in Cloud Data Centers

An efficient fault tolerance scheme based enhanced firefly optimization for virtual machine placement in cloud computing

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