Timely Coded Computing

Abstract

In modern distributed computing systems, unpredictable and unreliable infrastructures result in high variability of computing resources. Meanwhile, there is significantly increasing demand for timely and event-driven services with deadline constraints. Motivated by measurements over Amazon EC2 clusters, we consider a two-state Markov model for variability of computing speed in cloud networks. In this model, each worker can be either in a good state or a bad state in terms of the computation speed, and the transition between these states is modeled as a Markov chain which is unknown to the scheduler. We then consider a Coded Computing framework, in which the data is possibly encoded and stored at the worker nodes in order to provide robustness against nodes that may be in a bad state. Our goal is to design the optimal computation-load allocation strategy that maximizes the timely computation throughput (i.e, the average number of computation tasks accomplished before their deadline). Our main result is the development of a dynamic computation strategy called Estimate-and-Allocate (EA) strategy, which achieves the optimal timely computation throughput. Compared with the static allocation strategy, EA improves the timely computation throughput by 1.44 ×4.6 in experiments over Amazon EC2 clusters.