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Memory Overbooking and Dynamic Control of Xen Virtual Machines in Consolidated Environments

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Jin Heo, Xiaoyun Zhu, Pradeep Padala, Zhikui Wang
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IFIP/IEEE Symposium on Integrated Management (IM'09) mini-conference, Jun 2009. Abstract: The newly emergent cloud computing environments host hundreds to thousands of services on a shared resource pool. The sharing is enhanced by virtualization technologies allowing multiple services to run in different virtual machines (VMs) on a single physical node. Resource overbooking allows more services with time-varying demands to be consolidated reducing operational costs. In the past, researchers have studied dynamic control mechanisms for allocating CPU to virtual machines, when CPU is overbooked with respect to the sum of the peak demands from all the VMs. However, runtime re-allocation of memory among multiple VMs has not been widely studied, except on VMware platforms. In this paper, we present a case study where feedback control is used for dynamic memory allocation to Xen virtual machines in a consolidated environment. We illustrate how memory behaves differently from CPU in terms of its relationship to application-level performance, such as response times. We have built a prototype of a joint resource control system for allocating both CPU and memory resources to co-located VMs in real time. Experimental results show that our solution allows all the hosted applications to achieve the desired performance in spite of their time-varying CPU and memory demands, whereas a solution without memory control incurs significant service level violations.
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