Peak Cost Analysis of Distributed Systems (Author's version)

Abstract

We present a novel static analysis to infer the peak cost of distributed systems. The different locations of a distributed system communicate and coordinate their actions by posting tasks among them. Thus, the amount of work that each location has to perform can greatly vary along the execution depending on: (1) the amount of tasks posted to its queue, (2) their respective costs, and (3) the fact that they may be posted in parallel and thus be pending to execute simultaneously. The peak cost of a distributed location refers to the maximum cost that it needs to carry out along its execution. Inferring the peak cost is challenging because it increases and decreases along the execution, unlike the standard notion of total cost which is cumulative. Our key contribution is the novel notion of quantified queue configuration which captures the worst-case cost of the tasks that may be simultaneously pending to execute at each location along the execution. A prototype implementation demonstrates the accuracy and feasibility of the proposed peak cost analysis.