Energy-Aware Workflow Execution: An Overview of Techniques for Saving Energy and Emissions in Scientific Compute Clusters

TL;DR

This paper reviews techniques for reducing energy consumption and carbon emissions in scientific workflow execution on compute clusters, highlighting methods to make scientific computing more environmentally sustainable.

Contribution

It provides an overview of energy-saving techniques for scientific workflows, including real-world carbon footprint estimation and various optimization strategies.

Findings

Estimated carbon footprints of real scientific workflows.

Techniques like energy-efficient architectures and workload scheduling.

Potential reductions in energy use and emissions.

Abstract

Scientific research in many fields routinely requires the analysis of large datasets, and scientists often employ workflow systems to leverage clusters of computers for their data analysis. However, due to their size and scale, these workflow applications can have a considerable environmental footprint in terms of compute resource use, energy consumption, and carbon emissions. Mitigating this is critical in light of climate change and the urgent need to reduce carbon emissions. In this chapter, we exemplify the problem by estimating the carbon footprint of three real-world scientific workflows from different scientific domains. We then describe techniques for reducing the energy consumption and, thereby, carbon footprint of individual workflow tasks and entire workflow applications, such as using energy-efficient heterogeneous architectures, generating optimised code, scaling processor voltages and frequencies, consolidating workloads on shared cluster nodes, and scheduling workloads for optimised energy efficiency.