2012/02/14 by Bogdan Alexandru Caprarescu, Caprarescu, Bogdan Alexandru, Eva Kaslik +4
Computer Science · #Advanced Database Systems and Queries #Data Management and Algorithms #Distributed #FOS: Computer and information sciences #Parallel #Peer-to-Peer Network Technologies #and Cluster Computing (cs.DC) #cs.DC
paper · pdf · doi:10.48550/arxiv.1202.2981
Submitted to Journal of Computer and System Sciences
arxiv created 2012/02/14 · openalex publication_date 2012/02/14 · arxiv updated 2012/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A major impediment towards the industrial adoption of decentralized distributed systems comes from the difficulty to theoretically prove that these systems exhibit the required behavior. In this paper, we use probability theory to analyze a decentralized auto-scaling algorithm in which each node probabilistically decides to scale in or out. We prove that, in the context of dynamic workloads, the average load of the system is maintained within a variation interval with a given probability, provided that the number of nodes and the variation interval length are higher than certain bounds. The paper also proposes numerical algorithms for approximating these minimum bounds.