Carbon-Aware Workflow Scheduling with Fixed Mapping and Deadline Constraint

TL;DR

This paper addresses the challenge of scheduling interdependent workflows in data centers to minimize carbon emissions by leveraging green energy availability, proposing algorithms and heuristics for different processing scenarios.

Contribution

It formalizes the carbon-aware workflow scheduling problem with fixed task mapping and deadlines, and introduces a heuristic framework for NP-hard cases.

Findings

Heuristics significantly reduce carbon emissions compared to baseline.

Polynomial-time solution for uniprocessor case.

Heuristics outperform baseline algorithms in experiments.

Abstract

Large data and computing centers consume a significant share of the world's energy consumption. A prominent subset of the workloads in such centers are workflows with interdependent tasks, usually represented as directed acyclic graphs (DAGs). To reduce the carbon emissions resulting from executing such workflows in centers with a mixed (renewable and non-renewable) energy supply, it is advisable to move task executions to time intervals with sufficient green energy when possible. To this end, we formalize the above problem as a scheduling problem with a given mapping and ordering of the tasks. We show that this problem can be solved in polynomial time in the uniprocessor case. For at least two processors, however, the problem becomes NP-hard. Hence, we propose a heuristic framework called CaWoSched that combines several greedy approaches with local search. To assess the 16 heuristics resulting from different combinations, we also devise a simple baseline algorithm and an exact ILP-based solution. Our experimental results show that our heuristics provide significant savings in carbon emissions compared to the baseline.