Research Area: Cloud Computing
Cloud resource management becomes more important with the increasing usage of cloud resources. With various cloud options available, cloud provider may have different priority in managing the resource through resource scheduling and provisioning. Dynamic VM (Virtual Machine) consolidation algorithm is one of the techniques which can be used to reduce energy consumption through VM migration. Higher VM migration may lead to lower energy consumption and higher SLA violation. Although previous research has successfully decreased energy consumption and SLA violation, cloud providers may need to manage trade-offs between energy and SLA violation through availability of priority in the system. This paper proposes neural network-based adaptive selection of VM consolidation algorithms which adaptively chooses appropriate algorithm according to cloud providers goal priority and environment parameters. Dataset generation and performance evaluation using simulations on real-world PlanetLab VMs workload trace showed that adaptive selector produced better average performance score than independent methods on various evaluation priority.
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Author(s) Name: Joseph NathanaelWitanto,Hyotaek Lim and Mohammed Atiquzzaman
Journal name: Future Generation Computer Systems
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Publisher name: ELSEVIER
DOI: 10.1016/j.future.2018.04.075
Volume Information: Volume 87, October 2018, Pages 35-42
