# Energy and Network Aware Workload Management for Geographically Distributed Data Centers

arXiv: https://arxiv.org/abs/2106.00066 · HTML: https://mlsanity.com/paper/2106.00066/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2021.

## Built on by (most cited first, 6 of 6)

- [Sustainable Carbon-Aware and Water-Efficient LLM Scheduling in Geo-Distributed Cloud Datacenters](https://mlsanity.com/paper/2505.23554/) (2025)
- [SHIELD: Sustainable Hybrid Evolutionary Learning Framework for Carbon, Wastewater, and Energy-Aware Data Center Management](https://mlsanity.com/paper/2308.13086/) (2023)
- [Game-Theoretic Deep Reinforcement Learning to Minimize Carbon Emissions and Energy Costs for AI Inference Workloads in Geo-Distributed Data Centers](https://mlsanity.com/paper/2404.01459/) (2024)
- [A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management](https://mlsanity.com/paper/2205.08072/) (2022)
- [MARLIN: Multi-Agent Game-Theoretic Reinforcement Learning for Sustainable LLM Inference in Cloud Datacenters](https://mlsanity.com/paper/2605.13496/) (2026)
- [Ethical Design of Computers: From Semiconductors to IoT and Artificial Intelligence](https://mlsanity.com/paper/2212.12508/) (2022)

## Abstract (arXiv)

Cloud service providers are distributing data centers geographically to minimize energy costs through intelligent workload distribution. With increasing data volumes in emerging cloud workloads, it is critical to factor in the network costs for transferring workloads across data centers. For geo-distributed data centers, many researchers have been exploring strategies for energy cost minimization and intelligent inter-data-center workload distribution separately. However, prior work does not comprehensively and simultaneously consider data center energy costs, data transfer costs, and data center queueing delay. In this paper, we propose a novel game theory-based workload management framework that takes a holistic approach to the cloud operating cost minimization problem by making intelligent scheduling decisions aware of data transfer costs and the data center queueing delay. Our framework performs intelligent workload management that considers heterogeneity in data center compute capability, cooling power, interference effects from task co-location in servers, time-of-use electricity pricing, renewable energy, net metering, peak demand pricing distribution, and network pricing. Our simulations show that the proposed game-theoretic technique can minimize the cloud operating cost more effectively than existing approaches.