文章基本信息

标题：A Lightweight Approach Multiplexing Resource Allocation Scheme by Virtualization based on Time Series in SOC
本地全文：下载
作者：C.SARAVANAN ; C.ANURADHA
期刊名称：Oriental Journal of Computer Science and Technology
印刷版ISSN：0974-6471
出版年度：2014
卷号：7
期号：1
页码：155-160
出版社：Oriental Scientific Publishing Company
摘要：By leveraging virtual machine (VM) technology which provides performance and faultisolation, Cloud resources can be provisioned on demand in a fine-grained, multiplexed mannerrather than in monolithic pieces. By integrating volunteer computing into Cloud architectures, weenvision a gigantic Self-Organizing Cloud (SOC) being formed to reap the huge potential ofuntapped commodity computing power over the Internet. Towards this new architecture whereeach participant may autonomously act as both resource consumer and provider, we propose afully distributed, VM-multiplexing resource allocation scheme to manage decentralized resources.Our approach not only achieves maximized resource utilization using the proportional sharemodel (PSM), but also delivers provably and adaptively optimal execution efficiency. We alsodesign a novel multi-attribute range query protocol for locating qualified nodes. Contrary to existingsolutions which often generate bulky messages per request, our protocol produces only onelightweight query message per task on the Content Addressable Network (CAN). It works effectivelyto find for each task its qualified resources under a randomized policy that mitigates the contentionamong requesters. This paper extends our previous work and proposes a lightweight mathematicalmodel to estimate the energy cost of live migration of an idle virtual machine quantitatively. A seriesof experiments were conducted on KVM to profile the migration time and the power consumptionduring live migration. Based on these data we derived an energy cost model that predicts theenergy overhead of live migration of virtual machines with an accuracy of higher than 90%
关键词：Cloud Computing; VM-multiplexing Resource Allocation;Convex Optimization; P2P Multi-attribute Range-query; Lightweight Model