Semi-online task partitioning and communication between local and remote processors

Abstract

We study the scheduling of computational tasks on one local processor and one remote processor with communication delay. This problem has important application in cloud computing. Although the communication time to transmit a task can be inferred from the known data size of the task and the transmission bandwidth, the processing time of the task is generally unknown until it is processed to completion. Given a set of independent tasks with unknown processing times, we propose a Semi-online Partitioning and Communication (SPaC) algorithm, to jointly select the subset of tasks to be offloaded to the remote processor and their order of transmission, with an aim to minimize the overall makespan to process all tasks. Even though the offline version of this problem, with a priori known processing times, is NP-hard, we show that the proposed semi-online algorithm achieves a small competitive ratio when the communication times of tasks are smaller than their processing times at the remote processor. For general communication times, we use simulation to demonstrate that SPaC outperforms online list scheduling and performs comparably well with known offline heuristics.