CMS Workflow Execution using Intelligent Job Scheduling and Data Access Strategies

TL;DR

This paper presents an intelligent job scheduling and data access strategy for CMS workflows, significantly reducing latencies and improving overall execution time through a pilot job infrastructure with data reuse.

Contribution

It introduces a novel pilot job based infrastructure with intelligent data reuse and execution strategies to minimize various latencies in scientific workflows.

Findings

Significant reduction in workflow turnaround time.

Effective data reuse improves processing efficiency.

Validated with CMS Tier0 workflow and simulations.

Abstract

Complex scientific workflows can process large amounts of data using thousands of tasks. The turnaround times of these workflows are often affected by various latencies such as the resource discovery, scheduling and data access latencies for the individual workflow processes or actors. Minimizing these latencies will improve the overall execution time of a workflow and thus lead to a more efficient and robust processing environment. In this paper, we propose a pilot job based infrastructure that has intelligent data reuse and job execution strategies to minimize the scheduling, queuing, execution and data access latencies. The results have shown that significant improvements in the overall turnaround time of a workflow can be achieved with this approach. The proposed approach has been evaluated, first using the CMS Tier0 data processing workflow, and then simulating the workflows to evaluate its effectiveness in a controlled environment.