Load Balancing With Max-Min of Summations
Ranjan Kumar Mondal1, Enakshmi Nandi2, Payel Ray3, Debabrata Sarddar4
1Ranjan Kumar Mondal, Computer Science & Engineering, University of Kalyani, W.B, India.
2Enakshmi Nandi, Computer Science & Engineering, University of Kalyani, W.B, India.
3Payel Ray, Computer Science & Engineering, University of Kalyani, W.B, India.
4Dr. Debabrata Sarddar, Computer Science & Engineering, University of Kalyani, W.B, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 5224-5226 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8652088619/2019©BEIESP | DOI: 10.35940/ijeat.B5552.088619
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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 a computing tool for human kind. In recent years, it is using to generate IT services, appliances for higher activities computing and outsourcing in a cost-efficient and flexible way. In modern times, a variety of types of bandwidth eater are growing speedily Cloud computing is growing phenomenal gradually to supply the different kinds of cloud services and applications to the internet-based customer. Cloud computing utilizes Internet applications to execute the large-scale jobs. The most important objective of cloud computing is to allocate and calculate different services transparently throughout a scalable network of machines. Load balancing is one of the significant issues in Cloud Computing. Loads should be divided as CPU load, the capacity of memory and system load which is the measurement of work that a computation system performs. Load balancing is a modern method where the load is being shared amongst several machines of a distributed system to enhance the utilization of various applications and response time of multiple tasks and prevent overloading situation and under loading situation. In or approach, we developed an algorithm, LBMMS, which combines all least completion time. For this study, LBMMS presents the proficient deployment of various resources in cloud computing.
Keywords: Cloud Computing, Load Balancing, Distributed System, Scheduling.