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Network-Aware Re-Scheduling: Towards Improving Network Performance of Virtual Machines in a Data Center

机译:网络感知的重新调度:旨在提高数据中心中虚拟机的网络性能

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An effective virtual machine allocation and scheduling algorithm can improve the utilization of physical servers, lower energy cost and improve the overall performance of datacenters. Current virtual machine scheduling algorithms mainly focus on the initial allocation of VMs based on the CPU, memory and network bandwidth requirements. However, Caused by finish of jobs or expiration of lease, related virtual machines would be shut down and leave the system which generate plenty of resource fragments. Such fragments lead to unbalanced resource utilization and the communication performance may decline significantly. This paper studied the network influence on some typical applications in datacenters and proposed a self-adaptive network-aware virtual machine re-scheduling algorithm to maintain an optimal system-wide status. Our algorithm had two stages. In the first stage, we checked whether re-scheduling was necessary and in the second stage perform a heuristic re-scheduling to lower communication cost among VMs. We use two benchmarks in a real environment to examine network influence on different tasks. To evaluate the advantages of the proposed algorithm, we also build a cloud computing testbed. Real workload trace-driven simulations and testbed-based experiments show that, our algorithm greatly shortens the average finish time of map-reduce tasks and reduced time delay of web applications. Simulation results showed that our algorithm considerably reduced the amount of high-delay jobs, lowered the average traffic passed through high-level switches and improved the communication ability among virtual machines.
机译:有效的虚拟机分配和调度算法可以提高物理服务器的利用率,降低能源成本并提高数据中心的整体性能。当前的虚拟机调度算法主要专注于基于CPU,内存和网络带宽需求的VM初始分配。但是,由于工作完成或租赁期满,相关的虚拟机将被关闭并离开系统,从而产生大量的资源碎片。这样的碎片会导致资源利用不平衡,并且通信性能可能会大大下降。本文研究了网络对数据中心一些典型应用的影响,并提出了一种自适应网络感知的虚拟机重新调度算法,以维持最佳的系统范围状态。我们的算法有两个阶段。在第一阶段,我们检查是否需要重新调度,在第二阶段,我们执行启发式重新调度以降低VM之间的通信成本。我们在实际环境中使用两个基准来检查网络对不同任务的影响。为了评估该算法的优势,我们还构建了一个云计算测试平台。实际工作量跟踪驱动的仿真和基于测试平台的实验表明,我们的算法大大缩短了Map-reduce任务的平均完成时间,并减少了Web应用程序的时间延迟。仿真结果表明,我们的算法大大减少了高延迟作业的数量,降低了通过高层交换机的平均流量,并提高了虚拟机之间的通信能力。

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