Genetic Algorithm-Based Task Scheduling in Cloud Computing Using MapReduce Framework

Zhihao Peng; Poria Pirozmand; Masoumeh MotevalliAli EsmaeiliAli Asghar Rahmani Hosseinabadi

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Genetic Algorithm-Based Task Scheduling in Cloud Computing Using MapReduce Framework

机译：Genetic Algorithm-Based Task Scheduling in Cloud Computing Using MapReduce Framework

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Task scheduling is an essential component of any distributed system because it routes tasks to appropriate resources for execution, such as grids, clouds, and peer-to-peer networks. Common scheduling algorithms include downsides, such as high temporal complexity, non-simultaneous processing of input tasks, and longer program execution times. Exploration-based scheduling algorithms prioritize tasks using a variety of methods, resulting in long execution times on heterogeneous distributed computing systems. As a result, task prioritization becomes a bottleneck in such systems. It is appropriate to prioritize tasks with the shortest execution time using faster algorithms. The genetic algorithm (GA) is one of the evolutionary approaches used to solve complex problems quickly. This paper proposes a parallel GA with a MapReduce architecture for scheduling jobs on cloud computing with various priority queues. The fundamental aim of this study is to employ a MapReduce architecture to minimize the total execution time of the task scheduling process in the cloud computing environment. The proposed method accomplishes task scheduling in two stages: first, the GA was used in conjunction with heuristic techniques to assign tasks to processors, and then the GA was used in conjunction with the MapReduce framework to assign jobs to processors. In our experiments, we consider heterogeneous resources that differ in their ability to execute various tasks, as well as running a job on different resources with varying execution durations. The results show that the proposed method outperforms other algorithms such as particle swarm optimization, whale optimization algorithm, moth-flame optimization, and intelligent water drops.

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《Mathematical Problems in Engineering: Theory, Methods and Applications》 |2022年第52期|1.1-1.11|共11页
作者
Zhihao Peng; Poria Pirozmand; Masoumeh MotevalliAli EsmaeiliAli Asghar Rahmani Hosseinabadi;
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EIT Data Science and Communication College Zhejiang Yuexiu University Shaoxing;

Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding;

Department of Computer Engineering Karaj Branch Islamic Azad University KarajFaculty of Technical Engineering Islamic Azad University, North Tehran Branch Tehran;

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Genetic Algorithm-Based Task Scheduling in Cloud Computing Using MapReduce Framework

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