Optimization Algorithms based Task Scheduling Method for Cloud Computing Environment
Khairunnisa*, Assistant Professor, Department of Computer Science, Jamal Mohammed College (Autonomous), Tiruchirappalli 620 020, Tamilnadu, India.
Manuscript received on 4 August 2019. | Revised Manuscript received on 9 August 2019. | Manuscript published on 30 September 2019. | PP: 3608-3613 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5318098319/2019©BEIESP | DOI: 10.35940/ijrte.C5318.098319
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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: There are various enhancements in the world of technology. Among that Cloud computing delivers numerous amenities over the Internet. It employs data centers which comprise hardware and software provision for loading, servers, and systems. The primary reason for the popularity of Cloud computing is consistent performance, economical operation, prompt accessibility, rapid scaling and much more. The chief cause for concern in cloud computing are the errors that happen either in the software or the hardware and energy consumption on a large scale. The clients pay only for resources utilized by them and assets which are accessible during the computing in a cloud setting. In the environment of cloud computing, Task scheduling is significant concepts which can be used to minimize the energy and time spent. The algorithms in Task scheduling might employ various measures toward dispense preference to subtasks that may generate many schedules to the divergent computing structure. Moreover, consumption of energy could be dissimilar for every source which is allocated to a job. This present research explores that the PSO-CA based energy aware task scheduling method can predict with the aim to enhance the resource distribution.
Keywords: Cloud Computing, Particle Swarm Optimization, Cultural Algorithm, Task Scheduling, Energy-Aware.
Scope of the Article: Cloud Computing