Abstract
Dynamic workloads in cloud computing can be managed through live migration of virtual machines from overloaded or underloaded hosts to other hosts to save energy and/or mitigate performance-related Service Level Agreement (SLA) violations. The challenging issue is how to detect when a host is overloaded to initiate live migration actions in time. In this paper, a new approach to make long-term predictions of resource demands of virtual machines for host overload detection is presented. To take into account the uncertainty of long-term predictions, a probability distribution model of the prediction error is built. Based on the probability distribution of the prediction error, a decision-theoretic approach is proposed to make live migration decision that take into account live migration overheads. Experimental results using the CloudSim simulator and PlanetLab workloads show that the proposed approach achieves better performance and higher stability compared to other approaches that do not take into account the uncertainty of long-term predictions and the live migration overhead.
Highlights
Cloud computing is a promising approach in which resources are provided as services that can be leased and released by users through the Internet in an on-demand fashion [1]
One of the widely used cloud computing service models is Infrastructure as a Service (IaaS) [2] where raw computing resources are provided in the form of Virtual Machines (VMs) to cloud consumers charged for the resources consumed
The second one called Short-Term Detection (SHT-D) detects an overload state if the actual and the predicted CPU usage values of the two time intervals in the future are above the overload threshold
Summary
Cloud computing is a promising approach in which resources are provided as services that can be leased and released by users through the Internet in an on-demand fashion [1]. One of the widely used cloud computing service models is Infrastructure as a Service (IaaS) [2] where raw computing resources are provided in the form of Virtual Machines (VMs) to cloud consumers charged for the resources consumed. Virtualization approaches such as Xen [3] and VMware [4] allow infrastructure resources to be shared in an effective manner. VMs make it possible to allocate resources dynamically according to varying demands, providing opportunities for the efficient use of computing resources, as well as the optimization of application performance and energy consumption.
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