Volume 18, No. 6, 2021

Regression Using Logistic Method For Physical Machine Overload Finding And Host Power Method For Physical Machine Under Load Finding Algorithm In Cloud Datacenter

Sreenivasa B.L , Dr. S Sathyanarayana


Cloud computing is becoming increasingly prevalent in today's generation. In addition, the number of users who use cloud resources is growing by the day. The number of active host machines in a datacenter consumes a lot of energy to execute user requests. As a result, a significant amount of carbon dioxide is emitted into the atmosphere. To reduce carbon emissions, energy-efficient algorithms and approaches are necessary. The logistic regression for Host machine Overload detection and Host Power for Host machine Underload detection are discussed in this paper. The main goal is to increase physical machine utilization while lowering energy consumption by efficiently recognizing overloaded or underloaded host machines to avoid performance degradation and energy waste. Cloud Sim Toolkit is used to test this technique. The efficiency of an algorithm is measured using factors such as EC, SLAV, PDM, ESV, VM migration, and SLATAH.

Pages: 5835-5850

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 Ti

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