Deadline-Aware Coded Computation Across Homogeneous Workers

Distributed computing systems have been widely used in recent years to handle massive computations required by newly emerged machine learning algorithms and signal processing problems. In practice, a distributed computing system often receives multiple tasks each needs to be finished by a specific deadline. This necessitates use of a task scheduler which orders and prioritizes tasks executions. In this work, we consider task scheduling for a homogeneous distributed computing system with multiple matrix-vector multiplication jobs, and try to maximize the number of tasks completed before their deadlines. The main challenges in such a system are random task arrivals and random execution times due to the straggling effect. To address these challenges, we propose two task scheduling algorithms namely “simple greedy” and “farsighted greedy” and compare their performance with the ultimate upper bound, i.e., a genie-aided algorithm that knows the exact arrival and execution times of all tasks. Our simulation results demonstrate that the proposed algorithms can approach the performance of the genie-aided algorithm.