Optimization towards Efficiency and Stateful of dispel4py

TL;DR

This paper introduces a hybrid optimization approach for dispel4py, enhancing efficiency and support for stateful workflows through Redis mapping and auto-scaling, significantly reducing runtime and resource usage.

Contribution

It presents a novel hybrid method for stateful workflow optimization in dispel4py, integrating Redis mapping and auto-scaling to improve performance and resource efficiency.

Findings

Auto-scaling reduces runtime to 87% of baseline

Resource usage drops to 76% with auto-scaling

Stateful dispel4py achieves 32% of previous runtime

Abstract

Scientific workflows bridge scientific challenges with computational resources. While dispel4py, a stream-based workflow system, offers mappings to parallel enactment engines like MPI or Multiprocessing, its optimization primarily focuses on dynamic process-to-task allocation for improved performance. An efficiency gap persists, particularly with the growing emphasis on conserving computing resources. Moreover, the existing dynamic optimization lacks support for stateful applications and grouping operations. To address these issues, our work introduces a novel hybrid approach for handling stateful operations and groupings within workflows, leveraging a new Redis mapping. We also propose an auto-scaling mechanism integrated into dispel4py's dynamic optimization. Our experiments showcase the effectiveness of auto-scaling optimization, achieving efficiency while upholding performance. In the best case, auto-scaling reduces dispel4py's runtime to 87% compared to the baseline, using only 76% of process resources. Importantly, our optimized stateful dispel4py demonstrates a remarkable speedup, utilizing just 32% of the runtime compared to the contender.