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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >TRACON: Interference-Aware Schedulingfor Data-Intensive Applicationsin Virtualized Environments
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TRACON: Interference-Aware Schedulingfor Data-Intensive Applicationsin Virtualized Environments

机译：TRACKON：虚拟环境中数据密集型应用程序的干扰感知调度

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摘要

Large-scale data centers leverage virtualization technology to achieve excellent resource utilization, scalability, and high availability. Ideally, the performance of an application running inside a virtual machine (VM) shall be independent of co-located applications and VMs that share the physical machine. However, adverse interference effects exist and are especially severe for data-intensive applications in such virtualized environments. In this work, we present TRACON, a novel Task and Resource Allocation CONtrol framework that mitigates the interference effects from concurrent data-intensive applications and greatly improves the overall system performance. TRACON utilizes modeling and control techniques from statistical machine learning and consists of three major components: the interference prediction model that infers application performance from resource consumption observed from different VMs, the interference-aware scheduler that is designed to utilize the model for effective resource management, and the task and resource monitor that collects application characteristics at the runtime for model adaption. We implement and validate TRACON with a variety of cloud applications. The evaluation results show that TRACON can achieve up to 25 percent improvement on application throughput on virtualized servers.
机译：大型数据中心利用虚拟化技术来实现出色的资源利用率，可伸缩性和高可用性。理想情况下，在虚拟机（VM）中运行的应用程序的性能应独立于位于同一位置的应用程序和共享物理机的VM。然而，存在不利的干扰影响，并且对于这种虚拟化环境中的数据密集型应用尤其严重。在这项工作中，我们提出了TRACON，这是一种新颖的任务和资源分配控制框架，可减轻并发数据密集型应用程序的干扰影响，并大大提高整体系统性能。 TRACON利用统计机器学习中的建模和控制技术，包括三个主要组成部分：从不同VM观察到的资源消耗中推断应用程序性能的干扰预测模型，旨在利用该模型进行有效资源管理的干扰感知调度程序，任务和资源监视器在运行时收集应用程序特征以进行模型调整。我们使用各种云应用程序来实施和验证TRACON。评估结果表明，TRACON可以将虚拟服务器上的应用程序吞吐量提高多达25％。

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