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Analysis of Frequently Failing Tasks and Rescheduling Strategy in the Cloud System

机译:云系统中常见任务失败和调度策略分析

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摘要

To better understand task failures in cloud computing systems, the authors analyze failure frequency of tasks based on Google cluster dataset, and find some frequently failing tasks that suffer from long-term failures and repeated rescheduling, which are called killer tasks as they can be a big concern of cloud systems. Hence there is a need to analyze killer tasks thoroughly and recognize them precisely. In this article, the authors first investigate resource usage pattern of killer tasks and analyze rescheduling strategies of killer tasks in Google cluster to find that repeated rescheduling causes large amount of resource wasting. Based on the above observations, they then propose an online killer task recognition service to recognize killer tasks at the very early stage of their occurrence so as to avoid unnecessary resource wasting. The experiment results show that the proposed service performs a 93.6% accuracy in recognizing killer tasks with an 87% timing advance and 86.6% resource saving for the cloud system averagely.
机译:为了更好地了解云计算系统中的任务失败,作者基于Google集群数据集分析了任务的失败频率,并发现了一些长期失败和重复重新安排的频繁失败的任务,它们被称为致命任务,因为它们可能是致命的。云系统备受关注。因此,有必要彻底分析杀手级任务并准确识别它们。在本文中,作者首先研究了杀手任务的资源使用模式,并分析了Google集群中的杀手任务的重新安排策略,以发现重复的重新安排会导致大量资源浪费。基于上述观察,他们随后提出了在线杀手任务识别服务,以便在杀手任务发生的早期就对其进行识别,从而避免不必要的资源浪费。实验结果表明,所提出的服务在识别杀手级任务方面的准确度为93.6%,定时提前量为87%,云系统平均节省了86.6%的资源。

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