Evaluating Distributed Execution of Workloads

TL;DR

This paper experimentally compares resource selection and task placement strategies in distributed systems, integrating AIMES middleware with Swift to evaluate execution performance and identify optimal scheduling approaches.

Contribution

It introduces an integrated system combining AIMES and Swift, providing empirical insights into execution strategies and their effects on distributed workload performance.

Findings

Backfill scheduling improves resource utilization.

Early task binding can cause delays.

Pilot fragmentation negatively impacts execution time.

Abstract

Resource selection and task placement for distributed execution poses conceptual and implementation difficulties. Although resource selection and task placement are at the core of many tools and workflow systems, the methods are ad hoc rather than being based on models. Consequently, partial and non-interoperable implementations proliferate. We address both the conceptual and implementation difficulties by experimentally characterizing diverse modalities of resource selection and task placement. We compare the architectures and capabilities of two systems: the AIMES middleware and Swift workflow scripting language and runtime. We integrate these systems to enable the distributed execution of Swift workflows on Pilot-Jobs managed by the AIMES middleware. Our experiments characterize and compare alternative execution strategies by measuring the time to completion of heterogeneous uncoupled workloads executed at diverse scale and on multiple resources. We measure the adverse effects of pilot fragmentation and early binding of tasks to resources and the benefits of backfill scheduling across pilots on multiple resources. We then use this insight to execute a multi-stage workflow across five production-grade resources. We discuss the importance and implications for other tools and workflow systems.