Resource Scheduling for Tasks of a Workflow in Cloud Environment

Karmakar, Kamalesh; Das, Rajib; Khatua, Sunirmal

doi:10.1007/978-3-030-36987-3_13

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…After specifying the tasks and hardware resources available for each task, automata and a bee is assigned, and their performance is such that the location of each subtask may vary depending on the selection power of the bee during the execution of the program, as shown in the Table 1. Whole task execution using cellular automata to divide training resources and tasks in this example took 48 time periods, i.e., 12…”

Section: Test Data Of the Problemmentioning

confidence: 99%

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Multi-objective tasks scheduling using artificial bee colony algorithm based on cellular automata in cloud computing environment

Babadi

Shiri

Goudarzi

et al. 2022

IJECE

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<span>Due to the development of communication device technology and the need to use up-to-date infrastructure ready to respond quickly and in a timely manner to computational needs, the competition for the use of processing resources is increasing nowadays. The scheduling tasks in the cloud computing environment have been remained a challenge to access a quick and efficient solution. In this paper, the aim is to present a new tactic for allocating the available processing resources based on the artificial bee colony (ABC) algorithm and cellular automata for solving the task scheduling problem in the cloud computing network. The results show the performance of the proposed method is better than its counterparts.</span>

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Section: Test Data Of the Problemmentioning

confidence: 99%

“…A single custom representation of the problem and a uniform intersection is built for the proposed algorithm. In [12], a task scheduler based on a genetic algorithm for the cloud computing system was invented that used the genetic algorithm [13] to minimize the time to complete tasks in the cloud computing environment.…”

Section: Introductionmentioning

confidence: 99%