2018
DOI: 10.1016/j.icte.2017.08.001
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A hybrid particle swarm optimization and hill climbing algorithm for task scheduling in the cloud environments

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Cited by 66 publications
(36 citation statements)
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“…The makespan is the total time that is required for all tasks to be finished. Therefore, the researcher desire to gain algorithms which should explore the best mapping of a set of jobs onto resources while the makespan is minimized . In this section, we will review the previous work on the task scheduling issue in a fog environment.…”
Section: Related Workmentioning
confidence: 99%
“…The makespan is the total time that is required for all tasks to be finished. Therefore, the researcher desire to gain algorithms which should explore the best mapping of a set of jobs onto resources while the makespan is minimized . In this section, we will review the previous work on the task scheduling issue in a fog environment.…”
Section: Related Workmentioning
confidence: 99%
“…In the first scenario, which is denoted as S1, the number of all tasks (n) and the number of all resources (m) are assigned (12,2). For better analysis, the efficiency of the proposed method, six scenarios with different scales were provided.…”
Section: Data Setmentioning
confidence: 99%
“…For better analysis, the efficiency of the proposed method, six scenarios with different scales were provided. In the first scenario, which is denoted as S1, the number of all tasks (n) and the number of all resources (m) are assigned (12,2). In the second scenario, which is denoted as S2, the number of all tasks (n) and the number of all resources (m) are assigned (20,3).…”
Section: Data Setmentioning
confidence: 99%
“…To overcome this problem, researchers are focused on implementation of hybridization of any two of the evolutionary algorithms. To find a solution to the problems of cloud computing in task scheduling, Dordaie & Navimipour [24] proposed a hybrid particle swarm optimization and hill climbing algorithm which was properly scheduled, but it takes more time for task completion. Liu et al [25] described a task scheduling algorithm with the help of hybridised genetic ant colony systems.…”
Section: Fig1 Flow Process Of Task Scheduling In Cloud Environmentmentioning
confidence: 99%