应用服务器集群能耗与性能平衡的在线实时优化
作者:
作者单位:

汕头大学

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中图分类号:

TP272

基金项目:

国家自然科学基金项目(61202366, 61702318);广东省自然科学基金项目(2018A030313438).


Online real-time optimization of power-performance tradeoff for application server clusters
Author:
Affiliation:

Shantou University

Fund Project:

National Natural Science Foundation of China (61202366, 61702318), Natural Science Foundation of Guangdong Province (2018A030313438).

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    摘要:

    如何根据负载状况实时优化应用服务器集群的部署,以在能耗与性能之间取得平衡是急需解决的重要问题. 对此,提出一种应用服务器集群能耗与性能平衡的在线实时优化策略,优化目标是最小化能耗与请求丢弃速率的加权值,优化内容包括各服务器的开关和CPU频率. 该策略包括小规模集群优化(SSCOpt)和大规模集群优化(LSCOpt)两种方案:前者定义大量的变量,将集群优化描述成线性混合整数规划问题,然后采用软件包求解;后者通过分析能耗和负载模型的特性定义很少的变量,将集群优化描述成非线性混合整数规划问题,并提出一种基于花朵授粉算法和变量融合的求解算法. 测试结果表明:当集群规模较小时, SSCOpt方案能快速求得全局最优部署;当集群规模较大时, LSCOpt方案能快速求得很好的次优部署.

    Abstract:

    How to dynamically optimize the deployment of an application server cluster according to the load condition to balance the power and performance is an important issue that must be urgently solved. This paper proposes an online real-time optimization strategy for power-performance tradeoff for application server clusters, which aims to minimize the weighted value of the power and request discarding rate of a cluster. The optimization content involves the on/off state and CPU frequency of each server. The strategy involves two methods: small-scale cluster optimization (SSCOpt) and large-scale cluster optimization (LSCOpt). The SSCOpt defines a large number of variables to describe cluster optimization as a linear mixed integer programming problem, and then uses a software package to solve the problem. By analyzing the traits of power and load models, the LSCOpt defines a small number of variables to describe cluster optimization as a nonlinear mixed integer programming problem, and then proposes an solution algorithm based on flower pollination algorithm and variable fusion. The experimental results show that, the SSCOpt can get the global optimal deployment rapidly when used in small-scale clusters, and the LSCOpt can find a good near-optimal deployment rapidly even when applied to large-scale clusters.

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历史
  • 收稿日期:2020-05-12
  • 最后修改日期:2021-07-17
  • 录用日期:2020-09-08
  • 在线发布日期: 2020-10-02
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