Authors: Anita Choudhary; Mahesh Chandra Govil; Girdhari Singh; Lalit K. Awasthi; Emmanuel S. Pilli
Addresses: Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur 302017, India ' National Institute of Technology, Sikkim 737139, India ' Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur 302017, India ' Dr. B.R. Ambedkar National Institute of Technology, Jalandhar 144011, India ' Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur 302017, India
Abstract: In cloud environments, the overload leads to performance degradation and Service Level Agreement (SLA) violation while underload results in inefficient utilisation of resources and needless energy consumption. Dynamic Virtual Machine (VM) consolidation is considered as an effective solution to deal with both overload and underload problems. However, dynamic VM consolidation is not a trivial solution as it can also lead to violation of negotiated SLA due to runtime overheads in VM migration. Further, dynamic VM consolidation approaches need to answer many questions such as (i) when to migrate a VM? (ii) which VM is to be migrated? and (iii) where to migrate the selected VM? In this work, efforts are made to develop a comprehensive approach to achieve better solution to above discussed problems. In the proposed approach, future forecasting methods for host overload detection are explored; a fuzzy logic based VM selection approach that enhances the performance of VM selection strategy is developed; and a VM placement algorithm based on destination CPU utilisation is also developed. The performance evaluation of the proposed approaches is carried out on CloudSim toolkit using PlanetLab data set. The simulation results have exhibited significant improvement in the number of VM migrations, energy consumption, and SLA violations.
Keywords: cloud computing; virtual machines; dynamic virtual machine consolidation; exponential smoothing; fuzzy logic.
International Journal of Grid and Utility Computing, 2019 Vol.10 No.4, pp.308 - 325
Received: 11 Feb 2017
Accepted: 18 Jun 2017
Published online: 25 Jun 2019 *