title: Prediction based proactive thermal virtual machine scheduling in green clouds.
abstract: Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM) scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs) before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated.
authors.affiliation: Department of Computer Science and Engineering, SGGS World University, Fatehgarh Sahib, Punjab, India.
authors.forename: Supriya
authors.initials: S
authors.lastname: Kinger
authors.affiliation: School of Mathematics and Computer Applications, Thapar University, Patiala, India.
authors.forename: Rajesh
authors.initials: R
authors.lastname: Kumar
authors.affiliation: School of Mathematics and Computer Applications, Thapar University, Patiala, India.
authors.forename: Anju
authors.initials: A
authors.lastname: Sharma
mesh_terms: D000389:Air Conditioning / Q000295:instrumentation* / Q000379:methods; D000465:Algorithms; D003246:Conservation of Energy Resources / Q000379:methods*; D004735:Energy Transfer; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005246:Feedback; D006358:Hot Temperature; D020407:Internet / Q000295:instrumentation*; D010363:Pattern Recognition, Automated / Q000379:methods*; D060388:Support Vector Machine