Load Balancing Strategies to Solve Flowshop Scheduling on Parallel Computing

TL;DR

This paper introduces a novel load balancing strategy for parallel flowshop scheduling that improves computational efficiency by dynamically assigning loads based on node performance, validated on a supercomputer.

Contribution

The paper proposes the Proportional Fairness Strategy (PFS) for load balancing in parallel flowshop scheduling, enhancing efficiency over existing methods.

Findings

PFS outperforms existing strategies in reducing computation time.

Combining PFS with optimized data transfer yields 13-15% efficiency gains.

Validated on an 86-CPU supercomputer using MPI.

Abstract

This paper first presents a parallel solution for the Flowshop Scheduling Problem in parallel environment, and then proposes a novel load balancing strategy. The proposed Proportional Fairness Strategy (PFS) takes computational performance of computing process sets into account, and assigns additional load to computing nodes proportionally to their evaluated performance. In order to efficiently utilize the power of parallel resource, we also discuss the data structure used in communications among computational nodes and design an optimized data transfer strategy. This data transfer strategy combined with the proposed load balancing strategy have been implemented and tested on a super computer consisted of 86 CPUs using MPI as the middleware. The results show that the proposed PFS achieves better performance in terms of computing time than the existing Adaptive Contracting Within Neighborhood Strategy. We also show that the combination of both the Proportional Fairness Strategy and the proposed data transferring strategy achieves additional 13~15% improvement in efficiency of parallelism.