Task Scheduling based on Modified Grey Wolf Optimizer in Cloud Computing Environment

Alzaqebah, Abdullah; Al-Sayyed, Rizik; Masadeh, Raja

doi:10.1109/ictcs.2019.8923071

Cited by 32 publications

(23 citation statements)

References 29 publications

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“…This study compared the performance of the SLnO algorithm with the performance of WOA, GWO, VWOA and RR scheduling algorithms over various independent tasks (100-500) with different number of VMs (8, 16 and 32), which were selected in previous studies (Alzaqebah et al, 2019;. The results are illustrated in Figures 4-7.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The implementing time of long sized tasks is more time-consuming compared with that when the task is short or medium. EET and ETT are expressed as equations ( 8) and (Alzaqebah et al, 2019), respectively (Awad et al, 2015).…”

Section: Task Scheduling On Cloud Computingmentioning

confidence: 99%

“…Metaheuristic algorithms were defined as powerful set of procedures that are used to solve complex optimization problems with optimal solution or near to optimal solution (Almezeini and Hafez, 2017;Masadeh et al, 2017;Alzaqebah et al, 2019). Most of these algorithms are nature-inspired algorithms such as evolutionary algorithms and swarm intelligence.…”

Section: Introductionmentioning

confidence: 99%

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Task scheduling on cloud computing based on sea lion optimization algorithm

Masadeh

Alsharman

Sharieh

et al. 2021

IJWIS

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Purpose Sea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons, it is considered a global optimizer. The scheduling operation is completed by imitating the hunting behavior of sea lions. Design/methodology/approach Cloud computing (CC) is a type of distributed computing, contributory in a massive number of available resources and demands, and its goal is sharing the resources as services over the internet. Because of the optimal using of these services is everlasting challenge, the issue of task scheduling in CC is significant. In this paper, a task scheduling technique for CC based on SLnO and multiple-objective model are proposed. It enables decreasing in overall completion time, cost and power consumption; and maximizes the resources utilization. The simulation results on the tested data illustrated that the SLnO scheduler performed better performance than other state-of-the-art schedulers in terms of makespan, cost, energy consumption, resources utilization and degree of imbalance. Findings The performance of the SLnO, Vocalization of Whale Optimization Algorithm (VWOA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Round Robin (RR) algorithms for 100, 200, 300, 400 and 500 independent cloud tasks on 8, 16 and 32 VMs was evaluated. The results show that SLnO algorithm has better performance than VWOA, WOA, GWO and RR in terms of makespan and imbalance degree. In addition, SLnO exhausts less power than VWOA, WOA, GWO and RR. More precisely, SLnO conserves 5.6, 21.96, 22.7 and 73.98% energy compared to VWOA, WOA, GWO and RR mechanisms, respectively. On the other hand, SLnO algorithm shows better performance than the VWOA and other algorithms. The SLnO algorithm's overall execution cost of scheduling the cloud tasks is minimized by 20.62, 39.9, 42.44 and 46.9% compared with VWOA, WOA, GWO and RR algorithms, respectively. Finally, the SLnO algorithm's average resource utilization is increased by 6, 10, 11.8 and 31.8% compared with those of VWOA, WOA, GWO and RR mechanisms, respectively. Originality/value To the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Task Scheduling On Cloud Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Task scheduling on cloud computing based on sea lion optimization algorithm

Masadeh

Alsharman

Sharieh

et al. 2021

IJWIS

Self Cite

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“…For example, an MO-GWO approach was suggested to optimize both makespan and energy [53]. Modified versions of GWO, named MGWO and mean GWO, were proposed, which resulted in better performance over the baseline algorithms [54,55]. In another research work, Elaziz et al in [56] have combined the efficient local searching feature of the DE algorithm in the MS algorithm to improve the scheduling solution.…”

Section: Related Workmentioning

confidence: 99%

Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

et al. 2022

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Bag-of-Tasks (BoT) scheduling over cloud computing resources called Cloud Bag-of-Tasks Scheduling (CBS) problem, which is a well-known NP-hard optimization problem. Whale Optimization Algorithm (WOA) is an effective method for CBS problems, which still requires further improvement in exploration ability, solution diversity, convergence speed, and ensuring adequate exploration–exploitation tradeoff to produce superior scheduling solutions. In order to remove WOA limitations, a hybrid oppositional differential evolution-enabled WOA (called h-DEWOA) approach is introduced to tackle CBS problems to minimize workload makespan and energy consumption. The proposed h-DEWOA incorporates chaotic maps, opposition-based learning (OBL), differential evolution (DE), and a fitness-based balancing mechanism into the standard WOA method, resulting in enhanced exploration, faster convergence, and adequate exploration–exploitation tradeoff throughout the algorithm execution. Besides this, an efficient allocation heuristic is added to the h-DEWOA method to improve resource assignment. CEA-Curie and HPC2N real cloud workloads are used for performance evaluation of scheduling algorithms using the CloudSim simulator. Two series of experiments have been conducted for performance comparison: one with WOA-based heuristics and another with non-WOA-based metaheuristics. Experimental results of the first series of experiments reveal that the h-DEWOA approach results in makespan improvement in the range of 5.79–13.38% (for CEA-Curie workloads), 5.03–13.80% (for HPC2N workloads), and energy consumption in the range of 3.21–14.70% (for CEA-Curie workloads) and 10.84–19.30% (for HPC2N workloads) over well-known WOA-based metaheuristics. Similarly, h-DEWOA also resulted in significant performance in comparison with recent state-of-the-art non-WOA-based metaheuristics in the second series of experiments. Statistical tests and box plots also revealed the robustness of the proposed h-DEWOA algorithm.

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“…In [41], Bansal and Singh proposed enhancement to some issues related to GWO algorithm, low exploration and slow convergence rate using explorative equation and opposition-based learning (OBL), where results show better effectiveness than other metaheuristic algorithms; 23 standard benchmark test problems are used to validate the proposed enhancement. In [42], a modified GWO (MGWO) was proposed to schedule jobs on virtual machines and enhance performance. Outcomes revealed that the algorithm performed better than WOA and GWO in terms of cost and makespan.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem

Ababneh

2021

Mathematical Problems in Engineering

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In the context of cloud computing, one problem that is frequently encountered is task scheduling. This problem has two primary implications, which are the planning of tasks on virtual machines and the attenuation of performance. In order to address the problem of task scheduling in cloud computing, requisite nontraditional optimization attitudes to attain the optima of the problem, the present paper puts forth a hybrid multiple-objective approach called hybrid grey wolf and whale optimization (HGWWO) algorithms, that integrates two algorithms, namely, the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA), with the purpose of conjoining the advantages of each algorithm for minimizing costs, energy consumption, and total execution time needed for task implementation, beside that improving the use of resources. Assessment of the aims of the proposed approach is carried out with the help of the tool known as CloudSim. As pointed out by the results of the experimental work undertaken, the proposed approach has the capability of performing at a superior level by comparison to the original algorithms GWO and WOA on their own with regard to costs, energy consumption, makespan, use of resources, and degree of imbalance.

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Task Scheduling based on Modified Grey Wolf Optimizer in Cloud Computing Environment

Cited by 32 publications

References 29 publications

Task scheduling on cloud computing based on sea lion optimization algorithm

Task scheduling on cloud computing based on sea lion optimization algorithm

Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem

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