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首页> 外文期刊>Future generation computer systems >Developing resource consolidation frameworks for moldable virtual machines in clouds
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Developing resource consolidation frameworks for moldable virtual machines in clouds

机译：为云中可塑造的虚拟机开发资源整合框架

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摘要

This paper considers the scenario where multiple clusters of Virtual Machines (i.e., termed Virtual Clusters) are hosted in a Cloud system consisting of a cluster of physical nodes. Multiple Virtual Clusters (VCs) cohabit in the physical cluster, with each VC offering a particular type of service for the incoming requests. In this context, VM consolidation, which strives to use a minimal number of nodes to accommodate all VMs in the system, plays an important role in saving resource consumption. Most existing consolidation methods proposed in the literature regard VMs as "rigid" during consolidation, i.e., VMs' resource capacities remain unchanged. In VC environments, QoS is usually delivered by a VC as a single entity. Therefore, there is no reason why VMs' resource capacity cannot be adjusted as long as the whole VC is still able to maintain the desired QoS. Treating VMs as "moldable" during consolidation may be able to further consolidate VMs into an even fewer number of nodes. This paper investigates this issue and develops a Genetic Algorithm (GA) to consolidate moldable VMs. The GA is able to evolve an optimized system state, which represents the VM-to-node mapping and the resource capacity allocated to each VM. After the new system state is calculated by the GA, the Cloud will transit from the current system state to the new one. The transition time represents overhead and should be minimized. In this paper, a cost model is formalized to capture the transition overhead, and a reconfiguration algorithm is developed to transit the Cloud to the optimized system state with low transition overhead. Experiments have been conducted to evaluate the performance of the GA and the reconfiguration algorithm.
机译：本文考虑了在由物理节点群集组成的云系统中托管多个虚拟机群集（即称为虚拟群集）的方案。多个虚拟集群（VC）共存于物理集群中，每个VC为传入的请求提供特定类型的服务。在这种情况下，VM整合努力使用最少数量的节点来容纳系统中的所有VM，在节省资源消耗方面起着重要作用。文献中提出的大多数现有合并方法都将VM视为合并期间的“刚性”，即VM的资源容量保持不变。在VC环境中，QoS通常由VC作为单个实体提供。因此，只要整个VC仍然能够维持所需的QoS，就没有理由无法调整VM的资源容量。在合并过程中将VM视为“可塑型”可能能够将VM进一步整合到更少的节点中。本文对此问题进行了调查，并开发了一种遗传算法（GA）来整合可成型VM。 GA能够演化出优化的系统状态，该状态表示VM到节点的映射以及分配给每个VM的资源容量。遗传算法计算出新的系统状态后，云将从当前的系统状态过渡到新的状态。过渡时间代表开销，应将其最小化。本文将成本模型形式化以捕获过渡开销，并开发了一种重新配置算法，以将云过渡到具有低过渡开销的优化系统状态。已经进行实验以评估GA和重新配置算法的性能。

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  • 来源
    《Future generation computer systems》 |2014年第3期|69-81|共13页
  • 作者

    Ligang He; Deqing Zou; Zhang Zhang; Chao Chen; Hai Jin; Stephen A. Jarvis;

  • 作者单位

    Department of Computer Science, University of Warwick, Coventry, CV4 7AL, United Kingdom;

    School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China;

    School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China;

    Department of Computer Science, University of Warwick, Coventry, CV4 7AL, United Kingdom;

    School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China;

    Department of Computer Science, University of Warwick, Coventry, CV4 7AL, United Kingdom;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Virtualization; Cluster; Cloud;

    机译：虚拟化;簇;云;

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