Self-Stabilizing Balls and Bins in Batches

Berenbrink, Petra; Friedetzky, Tom; Kling, Peter; Mallmann-Trenn, Frederik; Nagel, Lars; Wastell, Chris

Abstract

A fundamental problem in distributed computing is the distribution of requests to a set of uniform servers without a centralized controller. Classically, such problems are modeled as static balls into bins processes, where m balls (tasks) are to be distributed among n bins (servers). In a seminal work, Azar et al. (SIAM J Comput 29(1):180–200, 1999. https://doi.org/10.1137/S0097539795288490) proposed the sequential strategy GREEDY[d] for n=m . Each ball queries the load of d random bins and is allocated to a least loaded of them. Azar et al. (1999) showed that d=2 yields an exponential improvement compared to d=1 . Berenbrink et al. (SIAM J Comput 35(6):1350–1385, 2006. https://doi.org/10.1137/S009753970444435X) extended this to m≫n , showing that for d=2 the maximal load difference is independent of m (in contrast to the d=1 case). We propose a new variant of an infinite balls-into-bins process. In each round an expected number of λn new balls arrive and are distributed (in parallel) to the bins. Subsequently, each non-empty bin deletes one of its balls. This setting models a set of servers processing incoming requests, where clients can query a server’s current load but receive no information about parallel requests. We study the GREEDY[d] distribution scheme in this setting and show a strong self-stabilizing property: for any arrival rate λ=λ(n)<1 , the system load is time-invariant. Moreover, for any (even super-exponential) round t, the maximum system load is (w.h.p.) Open image in new window for d=1 and Open image in new window for d=2 . In particular, GREEDY[2] has an exponentially smaller system load for high arrival rates.

Citation

Berenbrink, P., Friedetzky, T., Kling, P., Mallmann-Trenn, F., Nagel, L., & Wastell, C. (2018). Self-Stabilizing Balls and Bins in Batches. Algorithmica, 80(12), 3673-3703. https://doi.org/10.1007/s00453-018-0411-z

• Journal Article Type: Article
• Acceptance Date: Jan 10, 2018
• Online Publication Date: Feb 15, 2018
• Publication Date: Dec 1, 2018
• Deposit Date: Mar 16, 2018
• Publicly Available Date: Feb 15, 2019
• Journal: Algorithmica
• Print ISSN: 0178-4617
• Electronic ISSN: 1432-0541
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 80
• Issue: 12
• Pages: 3673-3703
• DOI: https://doi.org/10.1007/s00453-018-0411-z
• Public URL: https://durham-repository.worktribe.com/output/1337769
• Related Public URLs: https://arxiv.org/abs/1603.02188ll