The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: WORKS 2025: 20th Workshop on Workflows in Support of Large-Scale Science
Authors: Jason Chamorro (Loyola Marymount University), Gabriel Twigg-Ho (Swinburne University of Technology), Jared Coleman (Loyola Marymount University), Tainã Coleman (San Diego Supercomputer Center (SDSC)), and Bhaskar Krishnamachari and Mohammadali Khodabandehlou (University of Southern California (USC))
Abstract: We present an automated framework for online task scheduling on heterogeneous distributed systems, building on a modular parametric scheduler that enables dynamic scheduling decisions based on evolving execution states. Inspired by classical list-scheduling strategies such as HEFT and CPoP, our online scheduler simulates real-time task scheduling using only partial task graph knowledge. We evaluate our online scheduler variants against both their traditional offline baselines and a naive online strategy using a large-scale benchmark suite of real-world scientific workflows. Experimental results across different estimation methods and compute-to-communication ratio (CCR) settings show that our adaptive online schedulers consistently outperform the naive approach, achieving performance within approximately 3-5% of an ideal offline scheduler that has full future knowledge (compared to the approximately 10% overhead for the naive baseline).
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