Genetic Algorithm with Descendants Idea for Scheduling Tasks Graph in the Multi-Processor Architecture

Mahi, Mostafa; Kodaz, Halife

doi:10.7763/jacn.2014.v2.72

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…The GA has proposed to schedule this graph of the task that can be obtained effectively with low time. The results obtained from the study stated that the graph algorithm without the cost of communication could work quickly compared with others [14].…”

Section: Related Workmentioning

confidence: 91%

Task Scheduling Optimization in Cloud Computing by Rao Algorithm

Younes¹,

Elnahary²,

Alkinani³

et al. 2022

Computers, Materials &Amp; Continua

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Cloud computing is currently dominated within the space of highperformance distributed computing and it provides resource polling and ondemand services through the web. So, task scheduling problem becomes a very important analysis space within the field of a cloud computing environment as a result of user's services demand modification dynamically. The main purpose of task scheduling is to assign tasks to available processors to produce minimum schedule length without violating precedence restrictions. In heterogeneous multiprocessor systems, task assignments and schedules have a significant impact on system operation. Within the heuristic-based task scheduling algorithm, the different processes will lead to a different task execution time (makespan) on a heterogeneous computing system. Thus, a good scheduling algorithm should be able to set precedence efficiently for every subtask depending on the resources required to reduce (makespan). In this paper, we propose a new efficient task scheduling algorithm in cloud computing systems based on RAO algorithm to solve an important task and schedule a heterogeneous multiple processing problem. The basic idea of this process is to exploit the advantages of heuristic-based algorithms to reduce space search and time to get the best solution. We evaluate our algorithm's performance by applying it to three examples with a different number of tasks and processors. The experimental results show that the proposed approach significantly succeeded in finding the optimal solutions than others in terms of the time of task implementation.

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Section: Related Workmentioning

confidence: 91%