HAMM: A Hybrid Algorithm of Min-Min and Max-Min Task Scheduling Algorithms in Cloud Computing
Ibrahim A. Thiyeb1, Sharaf A. Alhomdy2
1Ibrahim A. Thiyeb*, Postgraduate Student IT Department, FCIT, Sana’a University, Sana‟a, Yemen.
2Dr. Sharaf A. Alhomdy*, Associate Prof., IT Department, FCIT, Sana’a University, Sana‟a, Yemen.
Manuscript received on October 06, 2020. | Revised Manuscript received on October 25, 2020. | Manuscript published on November 30, 2020. | PP: 209-218 | Volume-9 Issue-4, November 2020. | Retrieval Number: 100.1/ijrte.D4874119420 | DOI: 10.35940/ijrte.D4874.119420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Nowadays, with the huge development of information and computing technologies, the cloud computing is becoming the highly scalable and widely computing technology used in the world that bases on pay-per-use, remotely access, Internet-based and on-demand concepts in which providing customers with a shared of configurable resources. But, with the highly incoming user’s requests, the task scheduling and resource allocation are becoming major requirements for efficient and effective load balancing of a workload among cloud resources to enhance the overall cloud system performance. For these reasons, various types of task scheduling algorithms are introduced such as traditional, heuristic, and meta-heuristic. A heuristic task scheduling algorithms like MET, MCT, Min-Min, and Max-Min are playing an important role for solving the task scheduling problem. This paper proposes a new hybrid algorithm in cloud computing environment that based on two heuristic algorithms; Min-Min and Max-Min algorithms. To evaluate this algorithm, the Cloudsim simulator has been used with different optimization parameters; makespan, average of resource utilization, load balancing, average of waiting time and concurrent execution between small length tasks and long size tasks. The results show that the proposed algorithm is better than the two algorithms Min-Min and Max-Min for those parameters.
Keywords: Task Scheduling, Load Balancing, Heuristic Algorithms, Makespan, Max-Min, Min-Min, Resource Utilization.