Logistic Regression and HostPower for Host Machine OverLoad and UnderLoad Detection Algorithm in cloud datacenter

  • Mr. Sreenivasa B.L, Dr. S Sathyanarayana
Keywords: Energy Consumed by physical machine (EC), Service Level Agreement Violation (SLAV), performance deprivation because of migration (PDM), Energy Service Level Agreement Violation (ESV), migration of Virtual Machine and Service Level Agreement Violation Time per active host (SLATAH).

Abstract

 In present generation the popularity of cloud computing technology is growing. Also number of user’s who are using the cloud resource are increasing day by day. To process the user demands the number of active host machines in datacenter consume large amount of energy. Because of it there is vast amount of carbon emission released to the environment.  Energy efficient algorithm and techniques are required to decrease the carbon emissions. In this paper the logistic regression for Host machine Overload detection and HostPower for Host machine Underload detection is proposed. The core objective is to rise the utilization of physical machine and reduce energy consumption by efficiently detecting the overloaded or underloaded host machine, to avoid the performance degradation and wastage of energy consumption. To test this algorithm CloudSim Toolkit is been used. The factor such as EC, SLAV, PDM, ESV, VM migration and SLATAH is used as a measure to assess the effectiveness of an Algorithm.

Published
2021-08-18
How to Cite
Dr. S Sathyanarayana, M. S. B. (2021). Logistic Regression and HostPower for Host Machine OverLoad and UnderLoad Detection Algorithm in cloud datacenter. Design Engineering, 9250- 9268. Retrieved from http://thedesignengineering.com/index.php/DE/article/view/3496
Section
Articles