Hybrid Starling Social Spider Algorithm for Energy and Load Aware Task Scheduling in Cloud Computing
Arul Xavier V M1, Annadurai S2
1Arul Xavier V M, Assistant Professor, Department of Computer Science, Karunya Institute of Technology and Sciences, Coimbatore, India.
2Dr. Annadurai S, Professor, Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, India.
Manuscript received on 05 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 July 2019 | PP: 3135-3142 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8596078919/19©BEIESP | DOI: 10.35940/ijitee.I8596.078919
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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: The efficiency of the cloud-based systems is greatly relying on the task scheduling algorithm which affects the performance parameters such as makespan, response time, degree of imbalance and cost. In recent years, the energy efficiency is also considered as another challenging issue which affects the efficiency of cloud computing systems. This paper proposes a Hybrid Starling Social Spider Algorithm (Starling-SSA) for Energy and Load Aware Task Scheduling in cloud computing. The Starling-SSA is designed as a hybrid algorithm inspired by the intelligent behavior of social spider and the collective response behavior of starling birds. The foraging behavior of spider is implemented to identify the best VMs for the given task with minimum makespan and degree of imbalance. In addition to this, the distance factor is incorporated inspired by starling flock distance in order identify the closeness of VM pairs and avoids the VMs that are far away, thereby VMs can be limited during the searching process. This will greatly reduce energy consumption by taking only VMs that are belongs to the distance factor. The performance metrics such as makespan, degree of imbalance and energy efficiency are evaluated against the existing algorithms such as EATS, CBAT and HC-ACO. The results presents a significant improvements when comparing to the existing algorithms.
Keywords: Cloud Computing, Virtual Machine, Task Scheduling, Makespan, load Balancing, Energy Efficiency, Social Spider, Starling Bird’s Behavior, Degree of Imbalance
Scope of the Article: Cloud Computing