Carbon-Aware Workflow Scheduling with Fixed Mapping and Deadline Constraint

2025-07-11

AI Summary

To address high carbon emissions from DAG-based workflow scheduling in hybrid-energy data centers, this paper formulates carbon-aware scheduling under fixed task-to-processor mapping and given task ordering as an optimization problem—polynomially solvable for single processors but NP-hard for multi-processor settings. We propose CaWoSched, a general framework integrating greedy initialization with local search, and supporting exact solution via integer linear programming (ILP). Our approach explicitly models the time-varying availability of green energy and enforces task precedence constraints, maximizing green energy consumption while meeting workflow deadlines. Extensive experiments across diverse scenarios demonstrate that CaWoSched, evaluated over 16 strategy combinations, reduces carbon emissions by 23.7% on average compared to baseline algorithms. These results validate the effectiveness and robustness of carbon-aware scheduling under realistic, time-varying green energy supply profiles.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Combinatorial Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Schedule workflows to reduce carbon emissions
• Optimize task timing for green energy use
• Address NP-hard scheduling with multiple processors

Innovation

• Polynomial-time solution for uniprocessor scheduling
• Heuristic framework combining greedy and local search
• ILP-based exact solution for carbon-aware scheduling