Multi-objective Prediction based Task Scheduling Method in Cloud Computing
Swapnil M Parikh1, Saurabh A Shah2, Narendra M Patel3
1Swapnil M Parikh*, Department of Computer Science and Engineering, Babaria Institute of Technology, Vadodara, Gujarat, India.
2Saurabh A Shah, Department of Computer Engineering, C U Shah University, Wadhwan, Surendranagar, Gujarat, India.
3Narendra M Patel, Department of Computer Engineering, Birla Vishwakarma Mahavidydyalaya, V V Nagar, Gujarat, India.
Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9388-9394 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9702118419/2019©BEIESP | DOI: 10.35940/ijrte.D9702.118419
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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: Cloud Computing is Internet based computing where one can store and access their personal resources from any computer through Internet. Cloud Computing is a simple pay-per-utilize consumer-provider service model. Cloud is nothing but large pool of easily accessible and usable virtual resources. Task (Job) scheduling is always a noteworthy issue in any computing paradigm. Due to the availability of finite resources and time variant nature of incoming tasks it is very challenging to schedule a new task accurately and assign requested resources to cloud user. Traditional task scheduling techniques are improper for cloud computing as cloud computing is based on virtualization technology with disseminated nature. Cloud computing brings in new challenges for task scheduling due to heterogeneity in hardware capabilities, on-demand service model, pay-per-utilize model and guarantee to meet Quality of Service (QoS). This has motivated us to generate multi-objective methods for task scheduling. In this research paper we have presented multi-objective prediction based task scheduling method in cloud computing to improve load balancing in order to satisfy cloud consumers dynamically changing needs and also to benefit cloud providers for effective resource management. Basically our method gives low probability value for not capable and overloaded nodes. To achieve the same we have used sigmoid function and Euclidean distance. Our major goal is to predict optimal node for task scheduling which satisfies objectives like resource utilization and load balancing with accuracy.
Keywords: Cloud Computing, Task Scheduling, Multi-objective, Resource Management.
Scope of the Article: Cloud Computing.