Transition waste optimization for coded elastic computing

Son Hoang Dau, Ryan Gabrys, Yu Chih Huang, Chen Feng, Quang Hung Luu, Eidah J. Alzahrani, Zahir Tari

Research output: Contribution to journalArticleResearchpeer-review


Distributed computing, in which a resource-intensive task is divided into subtasks and distributed among different machines, plays a key role in solving large-scale problems. Coded computing is a recently emerging paradigm where redundancy for distributed computing is introduced to alleviate the impact of slow machines (stragglers) on the completion time. We investigate coded computing solutions over elastic resources, where the set of available machines may change in the middle of the computation. This is motivated by recently available services in the cloud computing industry (e.g., EC2 Spot, Azure Batch) where low-priority virtual machines are offered at a fraction of the price of the on-demand instances but can be preempted on short notice. Our contributions are three-fold. We first introduce a new concept called transition waste that quantifies the number of tasks existing machines must abandon or take over when a machine joins/leaves. We then develop an efficient method to minimize the transition waste for the cyclic task allocation scheme recently proposed in the literature (Yang et al. ISIT’19). Finally, we establish a novel solution based on finite geometry achieving zero transition wastes given that the number of active machines varies within a fixed range.

Original languageEnglish
Pages (from-to)4442-4465
Number of pages24
JournalIEEE Transactions on Information Theory
Issue number7
Publication statusPublished - Jul 2023


  • coded computing
  • Distributed computing
  • erasure coding
  • finite geometry
  • resource allocation

Cite this