A Hybrid Algorithm for Scheduling Scientific Workflows in Cloud Computing

Sardaraz, Muhammad; Tahir, Muhammad

doi:10.1109/access.2019.2961106

Cited by 37 publications

(24 citation statements)

References 43 publications

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“…where MS is the makespan and FT refers to the completion time of a task. 38 Monetary cost is calculated using equation (7) 31…”

Section: Methodsmentioning

confidence: 99%

“…where MC refers to the monetary cost of a workflow and TEC and TTC are, respectively, calculated using equations (8) and (9) 38…”

Section: Methodsmentioning

confidence: 99%

“…The fitness of multi-objectives is calculated using equation (11) based on the maximum and minimum values of makespan, cost, and load balance. Sardaraz and Tahir 38 calculate the fitness on the basis of makespan and cost including a parameter w for the user to set a weightage to either the makespan or the cost. In the evaluation of the proposed algorithm, an equal weightage was used for all parameters…”

Section: Methodsmentioning

confidence: 99%

“…Blank cells mean that the desired parameter is not shown in the article. 13 Yes Yes ---Yes Zhu et al 16 Yes Yes ----Priyadarsini and Arockiam 22 Yes Yes ---Verma and Kaushal 26 -Yes Yes Yes Yes -Aryan and Delavar 27 Yes -----Chen et al 28 -Y e s ---Y e s Barrett et al 29 Yes Yes ---Chen et al 30 Yes Yes -Yes --Casas et al 31 Yes Yes ---Manasrah and Ba Ali 33 Yes Yes Yes ---Choudhary et al 34 Yes Yes ----Xiumin et al 35 Yes Yes ----Junlong et al 36 Yes Yes -Yes --Shojafar and Javanmardi 37 Yes Yes Yes ---Sardaraz and Tahir 38 Yes Yes Yes ---MS: makespan; LB: load balance; SLA: service-level agreement. Blank cells mean that the desired parameter is not targeted in the article.…”

Section: Sardaraz and Tahir 38 Scientific Cloudsim Dynamicmentioning

confidence: 99%

“…The experimental results are compared with other methods to validate the performance of the proposed algorithm. Sardaraz and Tahir 38 present a hybrid algorithm for scheduling workflows in a cloud computing environment. The algorithm uses PSO with the pre-processing phase to achieve better scheduling.…”

Section: Related Workmentioning

confidence: 99%

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A parallel multi-objective genetic algorithm for scheduling scientific workflows in cloud computing

Sardaraz

Tahir

2020

International Journal of Distributed Sensor Networks

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Recent developments in cloud computing have made it a powerful solution for executing large-scale scientific problems. The complexity of scientific workflows demands efficient utilization of cloud resources to satisfy user requirements. Scheduling of scientific workflows in a cloud environment is a challenge for researchers. The problem is considered as NP-hard. Some constraints such as a heterogeneous environment, dependencies between tasks, quality of service and user deadlines make it difficult for the scheduler to fully utilize available resources. The problem has been extensively studied in the literature. Different researchers have targeted different parameters. This article presents a multi-objective scheduling algorithm for scheduling scientific workflows in cloud computing. The solution is based on genetic algorithm that targets makespan, monetary cost, and load balance. The proposed algorithm first finds the best solution for each parameter. Based on these solutions, the algorithm finds the superbest solution for all parameters. The proposed algorithm is evaluated with benchmark datasets and comparative results with the standard genetic algorithm, particle swarm optimization, and specialized scheduler are presented. The results show that the proposed algorithm achieves an improvement in makespan and reduces the cost with a well load balanced system.

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“…where MS is the makespan and FT refers to the completion time of a task. 38 Monetary cost is calculated using equation (7) 31…”

Section: Methodsmentioning

confidence: 99%

“…where MC refers to the monetary cost of a workflow and TEC and TTC are, respectively, calculated using equations (8) and (9) 38…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Sardaraz and Tahir 38 Scientific Cloudsim Dynamicmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

A parallel multi-objective genetic algorithm for scheduling scientific workflows in cloud computing

Sardaraz

Tahir

2020

International Journal of Distributed Sensor Networks

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Multi‐objective task scheduling in cloud computing

Malti

Hakem

Benmammar

2022

Concurrency and Computation

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Summary Cloud computing services are used to fulfill user requests, often expressed in the form of tasks and their execution in such environments requires efficient scheduling strategies that take into account both algorithmic and architectural characteristics. Unfortunately, this problem is known to be NP‐hard in its general form. Despite the fact that several studies have been published in the literature, there are still interesting and relevant questions to be addressed. Indeed, most of the previous studies focus on a single objective and in the case where they deal with a set of objectives, they use a simple compromise function and do not consider how each of the parameters might influence the others. To this end, we propose an efficient task scheduling algorithm which is based on the pollination behavior of flowers and makes use of both Pareto optimality principle and TOPSIS technique to improve the quality of the obtained solutions. Both single and multiobjective optimization variants are investigated. In the latter case, three optimization criteria are considered, namely, minimizing the time makespan or schedule length, the execution cost, and maximizing the overall reliability of the task mapping. Different test‐bed scenarios and QoS metrics were considered and the obtained results corroborate the merits of the proposed algorithm.

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