Most data generated by modern applications is stored in the cloud, and there is an exponential growth in the volume of jobs to access these data and perform computations using them. The volume of data access or computing jobs can be heterogeneous across different job types and can unpredictably change over time. Cloud service providers cope with this demand heterogeneity and unpredictability by over-provisioning the number of servers hosting each job type. In this paper, we propose the addition of erasure-coded servers that can flexibly serve multiple job types without additional storage cost. We analyze the service capacity region and the response time of such erasure-coded systems and compare them with standard uncoded replication-based systems currently used in the cloud. We show that coding expands the service capacity region, thus enabling the system to handle variability in demand for different data types. Moreover, we characterize the response time of the coded system in various arrival rate regimes. This analysis reveals that adding even a small number of coded servers can significantly reduce the mean response time, with a drastic reduction in regimes where the demand is skewed across different job types.
翻译:现代应用产生的大多数数据都储存在云层中,而且用于获取这些数据和进行使用这些数据的计算的工作量也呈指数增长。数据访问或计算工作的数量可能在不同的工作类型中各异,而且难以预测地随着时间的变化而变化。云端服务供应商通过过多地提供每个工作类型的托管服务器来应对这种需求差异和不可预测性。在本文件中,我们提议增加可灵活服务于多种工作类型的删除编码服务器,而无需额外储存费用。我们分析了服务能力区域和这种删除编码系统的反应时间,并将其与云层中目前使用的标准的未编码的复制系统进行比较。我们表明,编码扩大了服务能力区域,从而使该系统能够处理不同数据类型需求的变异性。此外,我们在各种抵达率制度中对编码系统的响应时间进行了描述。这一分析显示,即使增加少量编码服务器也能大大减少平均反应时间,同时急剧减少不同工作类型的需求。