High-performance computing in a cloud environment may require massive data transfer among some of the virtual machines (VMs). These VMs are deployed in physical machines (hosts) of a data center. The data transfer among the communicating VMs may use the same shared communication links of the data center. Hence, it is important to have efficient bandwidth allocation policies for different data transfer requests (DTRs) which result in better utilization of bandwidth and fair allocation among the DTRs. In this paper, a few bandwidth allocation policies are proposed and their performances are analyzed. While designing these policies, the objective is the maximization of throughput and bandwidth utilization while minimizing the service time and turnaround time. Some of the policies are based on integer linear programming (ILP) which runs in exponential time while others are based on polynomial-time heuristics. Experimental results show that the performances of heuristic-based policies are comparable to those given by ILP-based exponential time policies.

中文翻译：

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云数据中心虚拟机通信带宽分配

云环境中的高性能计算可能需要在某些虚拟机 (VM) 之间进行大量数据传输。这些虚拟机部署在数据中心的物理机（主机）中。通信VM之间的数据传输可以使用数据中心的相同共享通信链路。因此，重要的是为不同的数据传输请求 (DTR) 制定有效的带宽分配策略，从而更好地利用带宽并在 DTR 之间公平分配。在本文中，提出了几种带宽分配策略并分析了它们的性能。在设计这些策略时，目标是最大化吞吐量和带宽利用率，同时最小化服务时间和周转时间。一些策略基于以指数时间运行的整数线性规划 (ILP)，而其他策略基于多项式时间启发式算法。实验结果表明，基于启发式策略的性能与基于 ILP 的指数时间策略的性能相当。