利用马尔可夫服务速率策略改进多资源作业调度
Improving Multiresource Job Scheduling with Markovian Service Rate Policies
AI总结：
本文提出并分析了一类易于实现且易于分析的多资源作业调度策略——Markovian Service Rate (MSR) 策略，证明了其吞吐量最优性，推导了紧致的平均响应时间界限，并展示了如何根据系统参数选择合适的 MSR 策略。
摘要：
现代云计算工作负载由多资源作业组成，这些作业需要多种计算资源才能运行，例如 CPU 核心、内存、磁盘空间或硬件加速器。单个云服务器通常可以并行运行许多多资源作业，但前提是服务器有足够的资源来满足每个作业的需求。因此，调度策略必须选择并行运行的多资源作业集合，以最小化作业的平均响应时间——即从作业到达系统到完成所需的平均时间。遗憾的是，通过选择能够充分利用可用服务器资源的作业集合来实现低响应时间，已被证明是一个困难的问题。在本文中，我们开发并分析了一类新的多资源作业调度策略，称为 Markovian Service Rate (MSR) 策略。虽然先前的多资源作业调度策略要么分析起来高度复杂，要么难以实现，但我们的 MSR 策略实现简单且易于进行响应时间分析。我们证明了 MSR 策略类是吞吐量最优的，即只要系统有可能稳定，我们就可以使用 MSR 策略来稳定系统。我们还推导出了 MSR 算法下平均响应时间的界限，该界限在加性常数范围内是紧致的。这些界限可应用于具有不同抢占行为的系统，例如完全抢占式系统、非抢占式系统以及允许带设置时间抢占的系统。我们展示了如何利用我们的理论结果，根据系统到达率、作业服务需求、服务器的资源容量以及作业的资源需求来选择合适的 MSR 策略。
英文摘要：
Modern cloud computing workloads are composed of multiresource jobs that require a variety of computational resources in order to run, such as CPU cores, memory, disk space, or hardware accelerators. A single cloud server can typically run many multiresource jobs in parallel, but only if the server has sufficient resources to satisfy the demands of every job. A scheduling policy must therefore select sets of multiresource jobs to run in parallel in order to minimize the mean response time across jobs -- the average time from when a job arrives to the system until it is completed. Unfortunately, achieving low response times by selecting sets of jobs that fully utilize the available server resources has proven to be a difficult problem. In this paper, we develop and analyze a new class of policies for scheduling multiresource jobs, called Markovian Service Rate (MSR) policies. While prior scheduling policies for multiresource jobs are either highly complex to analyze or hard to implement, our MSR policies are simple to implement and are amenable to response time analysis. We show that the class of MSR policies is throughput-optimal in that we can use an MSR policy to stabilize the system whenever it is possible to do so. We also derive bounds on the mean response time under an MSR algorithm that are tight up to an additive constant. These bounds can be applied to systems with different preemption behaviors, such as fully preemptive systems, non-preemptive systems, and systems that allow preemption with setup times. We show how our theoretical results can be used to select a good MSR policy as a function of the system arrival rates, job service requirements, the server's resource capacities, and the resource demands of the jobs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
