Parallel and Memory-limited Algorithms for Optimal Task Scheduling Using a Duplicate-Free State-Space

TL;DR

This paper explores parallel and memory-efficient algorithms for optimal task scheduling using a duplicate-free state-space model called Allocation-Ordering (AO), demonstrating improved scalability and performance over older models.

Contribution

It introduces algorithms leveraging the duplicate-free AO model for parallel and memory-limited search, enhancing scalability and efficiency in optimal task scheduling.

Findings

AO improves DFBnB performance and scalability.

AO enables more effective parallel search algorithms.

AO outperforms older Exhaustive List Scheduling (ELS) model in experiments.

Abstract

The problem of task scheduling with communication delays is strongly NP-hard. State-space search algorithms such as A* have been shown to be a promising approach to solving small to medium sized instances optimally. A recently proposed state-space model for task scheduling, known as Allocation-Ordering (AO), allows state-space search methods to be applied without the need for previously necessary duplicate avoidance mechanisms, and resulted in significantly improved A* performance. The property of a duplicate-free state space also holds particular promise for memory limited search algorithms, such as depth-first branch-and-bound (DFBnB), and parallel search algorithms. This paper investigates and proposes such algorithms for the AO model and, for comparison, the older Exhaustive List Scheduling (ELS) state-space model. Our extensive evaluation shows that AO gives a clear advantage to DFBnB and allows greater scalability for parallel search algorithms.