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Cloud-computing shares a common pool of resources across customers at a scalethat is orders of magnitude larger than traditional multi-user systems.Constituent physical compute servers are allocated multiple "virtual machines"(VM) to serve simultaneously. Each VM user should ideally be unaffected byothers' demand. Naturally, this environment produces new challenges for theservice providers in meeting customer expectations while extracting anefficient utilization from server resources. We study a new cloud servicemetric that measures prolonged latency or delay suffered by customers. We modelthe workload process of a cloud server and analyze the process as the customerpopulation grows. The capacity required to ensure that average workload doesnot exceed a threshold over long segments is characterized. This can be used bycloud operators to provide service guarantees on avoiding long durations oflatency. As part of the analysis, we provide a uniform large-deviationprinciple for collections of random variables that is of independent interest.

A strong law for the rate of growth of long latency periods in cloud computing service