Carbon-Aware Mapping and Scheduling for Deadline-Constrained Workflows

2026-05-26

AI Summary

This study addresses the problem of scheduling precedence-constrained workflows in heterogeneous data centers with the dual objectives of meeting strict deadlines and minimizing carbon emissions. The authors formally prove that this problem is NP-hard and does not admit a constant-factor approximation algorithm. To tackle it, they propose a novel algorithm, Carbon-aware Workflow Mapping (CWM), which integrates dynamic programming with heuristic strategies to jointly optimize task mapping and scheduling. CWM explicitly models node-level energy heterogeneity and temporal fluctuations in renewable energy availability. Experimental results demonstrate that under a deadline constraint set at twice the baseline makespan, CWM reduces median carbon cost by 42% compared to the state-of-the-art CaWoSched algorithm, substantially improving carbon efficiency.

Abstract

As datacenters continue to grow in scale, their energy consumption and resulting carbon footprint have become pressing concerns. With the increasing share of renewable energy in a datacenter's mixed energy supply, shifting task execution to periods of high green-power availability is a promising strategy to reduce carbon emissions. However, in heterogeneous computing environments, the power consumption of compute nodes in a datacenter can also vary. In practice, workloads submitted to datacenters are often not isolated tasks, but entire workflows consisting of interdependent tasks with precedence constraints. A further challenge arises from the fact that carbon emission reductions must typically be achieved under strict workflow deadlines. In this work, we show that the problem posed by these challenges for the scheduler is NP-hard and admits no constant-factor approximation even for the uni-processor case. Motivated by this hardness, we present a novel algorithm CWM that combines carbon-aware mapping and scheduling to construct feasible solutions. Our approach integrates dynamic programming with efficient heuristics to exploit renewable energy availability and infrastructure heterogeneity. To assess the quality of the new algorithm, we evaluate it against the state-of-the-art approach CaWoSched and show that CWM achieves significant reductions in terms of carbon emissions in experiments. In particular, we are able to achieve a median carbon cost reduction of 42% over the best version of CaWoSched when the deadline is two times the makespan of a carbon-agnostic baseline. Note that CaWoSched itself already reduces the carbon-agnostic baseline by 36%.

Problem

• carbon-aware scheduling
• deadline-constrained workflows
• renewable energy
• heterogeneous computing
• datacenter carbon footprint

Innovation

• carbon-aware scheduling
• workflow mapping
• heterogeneous computing
• renewable energy
• deadline-constrained optimization