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Dynamic resource demand prediction and allocation in multi-tenant service clouds - 2016

Research Area:  Cloud Computing

Abstract:

Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and resource allocation methodology that can provision resources in advance, thereby minimizing the virtual machine downtime required for resource provisioning. In this paper, we present a dynamic resource demand prediction and allocation framework in multi-tenant service clouds. The novel contribution of our proposed framework is that it classifies the service tenants as per whether their resource requirements would increase or not; based on this classification, our framework prioritizes prediction for those service tenants in which resource demand would increase, thereby minimizing the time needed for prediction. Furthermore, our approach adds the service tenants to matched virtual machines and allocates the virtual machines to physical host machines using a best-fit heuristic approach. Performance results demonstrate how our best-fit heuristic approach could efficiently allocate virtual machines to hosts so that the hosts are utilized to their fullest capacity. Copyright © 2016 John Wiley & Sons, Ltd.

Author(s) Name:  Manish Verma, G. R. Gangadharan, Nanjangud C. Narendra,Ravi Vadlamani , Vidyadhar Inamdar, Lakshmi Ramachandran and Rodrigo N. Calheiros

Journal name:  Concurrency and Computation: Practice & Experience

Conferrence name:  

Publisher name:  ACM

DOI:  10.1002/cpe.3767

Volume Information:  Volume 28,Issue 17,December 2016, pp 4429–4442