SustainDC: Benchmarking for Sustainable Data Center Control
Avisek Naug, Antonio Guillen, Ricardo Luna, Vineet Gundecha, Desik, Rengarajan, Sahand Ghorbanpour, Sajad Mousavi, Ashwin Ramesh Babu, Dejan, Markovikj, Lekhapriya D Kashyap, Soumyendu Sarkar

TL;DR
SustainDC introduces a benchmarking platform with Python environments to evaluate multi-agent reinforcement learning algorithms for optimizing data center operations, aiming to enhance sustainability amid rising energy demands.
Contribution
This paper presents SustainDC, a novel benchmarking framework for MARL algorithms tailored to diverse data center control tasks and configurations.
Findings
MARL algorithms show varied performance across different data center scenarios
Significant potential exists for improving data center efficiency with MARL
SustainDC enables systematic evaluation of algorithms under real-world conditions
Abstract
Machine learning has driven an exponential increase in computational demand, leading to massive data centers that consume significant amounts of energy and contribute to climate change. This makes sustainable data center control a priority. In this paper, we introduce SustainDC, a set of Python environments for benchmarking multi-agent reinforcement learning (MARL) algorithms for data centers (DC). SustainDC supports custom DC configurations and tasks such as workload scheduling, cooling optimization, and auxiliary battery management, with multiple agents managing these operations while accounting for the effects of each other. We evaluate various MARL algorithms on SustainDC, showing their performance across diverse DC designs, locations, weather conditions, grid carbon intensity, and workload requirements. Our results highlight significant opportunities for improvement of data center…
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Code & Models
Videos
Taxonomy
TopicsParallel Computing and Optimization Techniques · Advanced Data Storage Technologies · Distributed and Parallel Computing Systems
MethodsSparse Evolutionary Training
