DCcluster-Opt: Benchmarking Dynamic Multi-Objective Optimization for Geo-Distributed Data Center Workloads

AI summary

Overview

Research area: Sustainable computing, reinforcement learning for resource management, and multi-objective optimization applied to geo-distributed data center operations.

Technical level: Intermediate. The paper is readable without deep domain expertise, but familiarity with reinforcement learning environments (specifically the Gymnasium API) and basic data center concepts helps.

Scope in one sentence: DCcluster-Opt is an open-source simulation benchmark that lets researchers test scheduling algorithms for assigning AI workloads across many geographically distributed data centers while balancing carbon emissions, energy costs, service-level agreements, and water consumption.

What This Paper Is About

Large-scale AI training and inference consume enormous amounts of electricity, and where that computation runs matters: electricity grids differ in carbon intensity, prices fluctuate by hour and region, weather affects cooling load, and moving data between sites costs both money and time. Researchers want to build schedulers that shift workloads to cleaner, cheaper places at the right moments, but there has been no shared, realistic testbed that captures all of these interacting factors at once. This paper introduces DCcluster-Opt, a high-fidelity, open-source simulation benchmark that fills that gap.

Key Contributions

1. A high-fidelity geo-temporal simulation environment. DCcluster-Opt fuses curated real-world datasets — AI workload traces, grid carbon intensity, electricity market prices, weather for 20 global regions, cloud transmission costs, and empirical network delay parameters — with physics-informed models of data center hardware and cooling.

2. A formal multi-objective scheduling problem. A top-level coordinating agent must dynamically reassign or defer incoming tasks (each with its own resource and service-level agreement requirements) across a configurable cluster of data centers, while trading off carbon emissions, energy cost, SLA compliance, and water use.

3. Detailed physical modeling, including heat recovery. The environment represents CPUs, GPUs, memory, HVAC energy consumption, and advanced components such as waste-heat recovery, rather than treating data centers as abstract compute boxes.

4. A modular reward system plus reproducible baselines. Objectives can be reweighted or swapped, and the environment ships with a Gymnasium API and baseline controllers (both reinforcement learning and rule-based) so that diverse algorithms can be compared fairly.

Main Findings

• No single benchmark previously captured the full stack: the authors argue that existing work is fragmented — studies typically model either environmental signals (carbon, price, weather) or data center physics or network dynamics, but rarely all three together, which limits how credible scheduling results are.

• Realism comes from real data, not synthetic assumptions: carbon intensity, prices, weather, latency, and transmission cost are drawn from actual measurements across 20 regions, making results more transferable to practice.

• The problem is genuinely hard: the coordinating agent faces a dynamic, multi-objective decision problem with deferrable tasks and hard service-level constraints, which makes it a demanding testbed rather than a toy environment.

• Trade-offs are explicit and tunable: because the reward function is modular, researchers can directly investigate tensions such as lower carbon emissions versus higher cost, or better SLA compliance versus higher water consumption.

• Reproducibility is a first-class design goal: the Gymnasium interface and included baselines are intended to make algorithm comparisons consistent across research groups.

Methodology in Plain English

The authors built a simulator rather than running experiments on real hardware. They gathered publicly available datasets describing what actually happens in the world — how dirty the electricity grid is hour by hour in different regions, what power costs, what the weather is like, how long data takes to travel between sites, and how much bandwidth costs — and combined them with equations describing how servers and cooling systems consume energy. On top of this, they defined a task-scheduling problem: jobs arrive with requirements and deadlines, and a single coordinating agent decides whether each job runs now, runs somewhere else, or waits. Because the objectives conflict, the reward system is broken into separate parts that can be weighted differently. Finally, they wrapped everything in the standard Gymnasium reinforcement learning interface and provided baseline policies, so anyone can plug in their own algorithm and get comparable numbers.

Why This Matters

Impact on research: Sustainable computing has suffered from a lack of shared evaluation standards, meaning results from different papers are often not comparable. A common, realistic benchmark lets the field measure genuine progress on carbon-aware scheduling instead of comparing numbers produced under incompatible assumptions.

Real-world applications:

• Carbon-aware workload placement for cloud providers and AI labs that want to route training or inference jobs toward regions with cleaner electricity at a given hour.
• Electricity cost reduction by exploiting regional and temporal price differences, including participation in demand-response programs.
• Water stewardship, since data center cooling consumes significant water and siting decisions affect water-stressed communities.
• Network-aware scheduling, where latency and data-transfer costs constrain which jobs can realistically be moved between sites.

Industry relevance: Hyperscalers and colocation operators face mounting pressure from regulators, investors, and customers to report and reduce emissions, cost, and water use simultaneously. A configurable simulator lets them test scheduling policies at scale before risking production workloads on live infrastructure.

Future Directions

• Closing the simulation-to-reality gap: validating that policies learned in DCcluster-Opt still perform well when deployed on real hardware, and quantifying where the simulator diverges from reality.
• Handling uncertainty: extending the environment to include forecasting errors for weather, carbon intensity, and prices, since real schedulers must decide with imperfect predictions rather than perfect foresight.
• Expanding the modeled infrastructure: adding on-site renewable generation, battery storage, carbon capture, and embodied carbon of hardware, all of which change the optimal scheduling strategy.
• Scaling and decentralizing control: moving beyond a single coordinating agent to hierarchical or multi-agent schemes that can manage hundreds or thousands of facilities, and studying how coordination overhead affects achieved objectives.

Target Audience

Reinforcement learning researchers looking for a realistic, multi-objective benchmark; sustainable computing and "green AI" researchers; data center and cloud infrastructure engineers evaluating scheduling strategies; energy and grid researchers interested in demand-side flexibility; and graduate students or practitioners who want a hands-on environment for studying trade-offs between environmental impact, cost, and performance.

Authors’ abstract

The increasing energy demands and carbon footprint of large-scale AI require intelligent workload management in globally distributed data centers. Yet progress is limited by the absence of benchmarks that realistically capture the interplay of time-varying environmental factors (grid carbon intensity, electricity prices, weather), detailed data center physics (CPUs, GPUs, memory, HVAC energy), and geo-distributed network dynamics (latency and transmission costs). To bridge this gap, we present DCcluster-Opt: an open-source, high-fidelity simulation benchmark for sustainable, geo-temporal task scheduling. DCcluster-Opt combines curated real-world datasets, including AI workload traces, grid carbon intensity, electricity markets, weather across 20 global regions, cloud transmission costs, and empirical network delay parameters with physics-informed models of data center operations, enabling rigorous and reproducible research in sustainable computing. It presents a challenging scheduling problem where a top-level coordinating agent must dynamically reassign or defer tasks that arrive with resource and service-level agreement requirements across a configurable cluster of data centers to optimize multiple objectives. The environment also models advanced components such as heat recovery. A modular reward system enables an explicit study of trade-offs among carbon emissions, energy costs, service level agreements, and water use. It provides a Gymnasium API with baseline controllers, including reinforcement learning and rule-based strategies, to support reproducible ML research and a fair comparison of diverse algorithms. By offering a realistic, configurable, and accessible testbed, DCcluster-Opt accelerates the development and validation of next-generation sustainable computing solutions for geo-distributed data centers.