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arXiv 1804.07571cs.DCcs.GTcs.PF
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云计算的集群接纳问题研究

On the cluster admission problem for cloud computing

Ludwig Dierks, Ian A. Kash, Sven Seuken

摘要:

云计算提供商面临着将异构客户工作负载与为他们服务的资源相匹配的问题。如果已经在集群上运行作业的客户随着时间的推移而增加或减少其资源使用量,则这尤其具有挑战性。因此,提供商必须不断决定是否可以向给定的集群添加额外的工作负载,或者这样做是否会影响现有工作负载的扩展能力。目前,这通常使用简单的阈值策略来保留每个集群的大部分,这导致低效率(即,集群平均利用率低)。我们提出了更复杂的政策,控制准入集群,并证明他们显着提高集群利用率。我们首先介绍了集群接纳问题,并将其形式化为一个受约束的部分可观测马尔可夫决策过程(POMDP)。由于这是不可行的,以解决POMDP最优,然后系统地设计接纳政策,估计每个工作负载的分布的时刻,未来的资源使用。通过基于Microsoft Azure跟踪的广泛模拟,我们表明我们的准入政策比简单的阈值政策有了实质性的改进。然后,我们表明,大量的进一步收益是可能的,如果高质量的信息是关于到达的工作负载。在此基础上,我们提出了一个信息启发的方法来激励用户提供这些信息,并模拟其效果。

英文摘要:

Cloud computing providers face the problem of matching heterogeneous customer workloads to resources that will serve them. This is particularly challenging if customers, who are already running a job on a cluster, scale their resource usage up and down over time. The provider therefore has to continuously decide whether she can add additional workloads to a given cluster or if doing so would impact existing workloads' ability to scale. Currently, this is often done using simple threshold policies to reserve large parts of each cluster, which leads to low efficiency (i.e., low average utilization of the cluster). We propose more sophisticated policies for controlling admission to a cluster and demonstrate that they significantly increase cluster utilization. We first introduce the cluster admission problem and formalize it as a constrained Partially Observable Markov Decision Process (POMDP). As it is infeasible to solve the POMDP optimally, we then systematically design admission policies that estimate moments of each workload's distribution of future resource usage. Via extensive simulations grounded in a trace from Microsoft Azure, we show that our admission policies lead to a substantial improvement over the simple threshold policy. We then show that substantial further gains are possible if high-quality information is available about arriving workloads. Based on this, we propose an information elicitation approach to incentivize users to provide this information and simulate its effects.

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