Transition Waste Optimization for Coded Elastic Computing

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

研究成果: Article同行評審


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. <italic>Coded computing</italic> 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 <italic>transition waste</italic> 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 <italic>et al</italic>. ISIT&#x2019;19). Finally, we establish a novel solution based on finite geometry achieving <italic>zero</italic> transition wastes given that the number of active machines varies within a fixed range.

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期刊IEEE Transactions on Information Theory
出版狀態Accepted/In press - 2023


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