Abstract: Concerning the problems of resource allocation about uneven distribution, low efficiency, dislocation and so on, a new algorithm named Global Dominant Resource Fair (GDRF) allocation algorithm which adopts several rounds of allocation was proposed to meet the needs of different users, achieve multiple types of resource fairness, and get high resource utilization. First, a qualification queue was determined by allocated resource amount of the users, then the specific user was determined to allocate resource through the global dominant resource share and the global dominant resource weight. The matching condition of resources was took into account in allocation process and the progressive filling of Max-Min strategy was used. In addition, the universal fairness evaluation model of multi-resource allocation was applied to the specific algorithm. Comparison experiments were conducted based on a Google's cluster. Experimental results show that compared with maximizing multi-resource fairness based on dominant resource, the amount of allocated virtual machine is increased by 12%, the resource utilization is increased by 0.5 percentage points, and fairness evaluation value is increased by about 15%. The proposed algorithm has a high degree of adaptation of resources combination allocation, allowing the supply to better match users' demand.

Key words: cloud computing, resource allocation, fairness, fairness evaluation, progressive filling

摘要： 针对云数据中心资源分配不均、效率不高、资源错位等问题，为了满足不同用户的需求，达到多种资源分配的公平性，实现资源的高效利用，提出了全局优势资源公平（GDRF）分配算法。GDRF算法采用多轮分配方式，即先通过用户已分配资源量确定分配资格，每轮再通过全局优势资源共享比和全局优势资源权重来确定具体的分配用户，分配过程充分考虑了资源的匹配情况，采用了max-min fairness思想的渐进填充方式，并且将多资源分配公平性统一度量模型运用到了算法中。实验基于一个Google集群数据模型与基于占优资源的多资源联合公平分配算法作了比较。实验结果表明，GDRF算法分配的虚拟机总量提高了12%，资源总利用率提高了0.5个百分点，公平评估值提高了约15%，并且该算法的资源组合分配的适应度较高，使得用户需求和供给更匹配。

关键词: 云计算, 资源分配, 公平, 公平度量, 渐进填充