Scalable Hierarchical Scheduling for Malleable Parallel Jobs on Multiprocessor-based Systems

TL;DR

This paper introduces AC-DS, a scalable hierarchical scheduling algorithm for malleable parallel jobs on multiprocessor systems, demonstrating theoretical optimality and superior performance through experiments.

Contribution

The paper presents a novel hierarchical scheduling algorithm, AC-DS, with proven scalability and competitiveness, tailored for malleable parallel jobs in multi-level computing environments.

Findings

AC-DS achieves $O(1)$-competitiveness in makespan.

AC-DS outperforms existing strategies across various workloads.

The algorithm is scalable regardless of hierarchical levels.

Abstract

The proliferation of multi-core and multiprocessor-based computer systems has led to explosive development of parallel applications and hence the need for efficient schedulers. In this paper, we study hierarchical scheduling for malleable parallel jobs on multiprocessor-based systems, which appears in many distributed and multilayered computing environments. We propose a hierarchical scheduling algorithm, named AC-DS, that consists of a feedback-driven adaptive scheduler, a desire aggregation scheme and an efficient resource allocation policy. From theoretical perspective, we show that AC-DS has scalable performance regardless of the number of hierarchical levels. In particular, we prove that AC-DS achieves $O(1)$-competitiveness with respect to the overall completion time of the jobs, or the makespan. A detailed malleable job model is developed to experimentally evaluate the effectiveness of the proposed scheduling algorithm. The results verify the scalability of AC-DS and demonstrate that AC-DS outperforms other strategies for a wide range of parallel workloads.