数据中心中软件定义的任务级截止日期感知抢占式流调度

Lili Liu, Dan Li, Jianping Wu
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引用次数: 2

摘要

许多数据中心应用都有截止日期需求,这就对网络传输中的截止日期意识提出了要求。在最后期限内完成是完成流程的必要要求。当前数据中心的传输协议试图公平地共享网络资源,并且与截止日期无关。最近,一些作品试图通过让尽可能多的流程在截止日期前完成来解决这个问题。但是,对于许多数据中心应用程序来说,任务直到最后一个流完成后才能完成,这意味着如果任务中的某些流无法满足截止日期,则已完成的流所消耗的带宽被浪费了。本文设计了一种任务级的截止日期感知抢占式流调度(TAPS),其目的是使更多的任务满足截止日期。我们利用软件定义网络(SDN)技术,将SDN从流级感知推广到任务级感知。调度算法在SDN控制器上运行,决定是否接受或丢弃流,预先分配传输时间片,并计算接受流的路由路径。广泛的流级模拟表明,TAPS在截止日期敏感数据中心环境中优于Varys、Bara at、PDQ(抢占式分布式快速流调度)、D3(截止日期驱动交付控制协议)和公平共享传输协议。在实际系统上的简单实现也证明了TAPS能够有效地利用数据中心的网络带宽。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TAPS: Software Defined Task-Level Deadline-Aware Preemptive Flow Scheduling in Data Centers
Many data center applications have deadline requirements, which pose a requirement of deadline-awareness in network transport. Completing within deadlines is a necessary requirement for flows to be completed. Transport protocols in current data centers try to share the network resources fairly and are deadline-agnostic. Recently several works try to address the problem by making as many flows meet deadlines as possible. However, for many data center applications, a task cannot be completed until the last flow finishes, which indicates the bandwidths consumed by completed flows are wasted if some flows in the task cannot meet deadlines. In this paper we design a task-level deadline-aware preemptive flow scheduling(TAPS), which aims to make more tasks meet deadlines. We leverage software defined networking (SDN) technology and generalize SDN from flow-level awareness to task-level awareness. The scheduling algorithm runs on the SDN controller, which decides whether a flow should be accepted or discarded, pre-allocates the transmission time slices and computes the routing paths for accepted flows. Extensive flow-level simulations demonstrate TAPS outperforms Varys, Bara at, PDQ (Preemptive Distributed Quick flow scheduling), D3 (Deadline-Driven Delivery control protocol) and Fair Sharing transport protocols in deadline sensitive data center environment. A simple implementation on real systems also proves that TAPS makes high effective utilization of the network bandwidth in data centers.
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