ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2015, Vol. 52 ›› Issue (6): 1288-1302.doi: 10.7544/issn1000-1239.2015.20150168

所属专题: 2015面向应用领域需求的体系结构

• 系统结构 • 上一篇    下一篇

异构云计算体系结构及其多资源联合公平分配策略

王金海,黄传河,王晶,何凯,史姣丽,陈希   

  1. (武汉大学计算机学院 武汉 430072) (wangjinhai@whu.edu.cn)
  • 出版日期: 2015-06-01
  • 基金资助: 
    基金项目:国家自然科学基金项目(61373040,61173137);教育部博士点基金项目(20120141110073);新疆维吾尔自治区自然科学基金项目(2015211B030)

A Heterogeneous Cloud Computing Architecture and Multi-Resource-Joint Fairness Allocation Strategy

Wang Jinhai, Huang Chuanhe, Wang Jing, He Kai, Shi Jiaoli, Chen Xi   

  1. (Computer School, Wuhan University, Wuhan 430072)
  • Online: 2015-06-01

摘要: 资源分配策略是当前云计算研究领域中的一个重要研究热点,异构云计算体系结构下的复杂应用问题研究中,最基本的问题在于如何将总体有限的资源分配给多个租户或应用,以达到效率或收效最大化.但是,在经典的资源分配问题中,任务或者用户往往是“贪婪”的;因此,在总体资源有限的前提下,资源分配的公平性就显得尤为重要.为了满足不同的任务需求,达到多种资源分配的公平性,设计了一个虚拟化的异构云计算体系结构,提出了该体系结构下基于占优资源的多资源联合公平分配算法(maximizing multi-resource fairness based on dominant resource, MDRF),并且证明了算法的帕累托等相关属性;给出了占优资源熵(dominant resource entropy, DRE)和占优资源权重(dominant resource weight, DRW)的定义,占优资源熵更加精确地刻画了用户资源请求与任务所调度到的服务器资源之间的适应程度,使系统的自适应能力更强同时提高了资源利用率.占优资源权重保障了用户优先获取资源的优先次序,协同所采用保障公平性的Max-Min Fairness策略,使资源的分配更加有序.实验表明,我们的策略有更高的系统资源利用率,并且使需求与供给更加匹配,进而使用户的占优资源获取更多,提高了服务质量.

关键词: 多资源公平分配, 占优资源熵, 占优资源权重, 最大最小公平性, 异构云

Abstract: Resource allocation strategies are an important research hotspot about cloud computing at present. The most fundamental problem is how to fairly allocate the finite amount of resources to multiple users or applications in complex application under heterogeneous cloud computing architecture, at the same time, to achieve maximize resource utilization or efficiency. However, tasks or users are often greedy for classical resource allocation problems, therefore, under the condition of finite amount of resource, the fairness of resource allocation is particularly important. To meet different task requirements and achieve multiple types resource fairness, we design a heterogeneous cloud computing architecture and present an algorithm of maximizing multi-resource fairness based on dominant resource(MDRF). We further prove the related attributions of our algorithm such as Pareto efficiency, and give the definition of dominant resource entropy (DRE) and dominant resource weight (DRW). DRE accurately depicts the adaption degree between the resource requirement of user and the resource type of server allocated for user tasks, and makes the system more adaptive and improves the system resource utilization. DRW guarantees the priority of users obtaining resource when cooperating with the adopted Max-Min strategy guaranteeing fairness, and makes the system resource allocation more ordered. Experimental results demonstrate that our strategy has more higher resource utilization and makes resource requirements and resource provision more matching. Furthermore, our algorithm makes users achieve more dominant resource and improves the quality of service.

Key words: multi-resource fairness allocation, dominant resource entropy(DRE), dominant resource weight(DRW), Max-Min Fairness, heterogeneous cloud

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