# An Online Algorithm for Combined Computing Workload and Energy Coordination Within A Regional Data Center Cluster

## TL;DR

This paper introduces a prediction-free, Lyapunov optimization-based online algorithm for coordinating workload and energy in regional data center clusters, ensuring physical limits and near-optimal performance.

## Contribution

It develops a novel online algorithm with performance guarantees and a distributed implementation method for energy and workload coordination in data centers.

## Key findings

- The algorithm guarantees workload and energy levels within physical limits.
- It provides a theoretical bound on the performance gap compared to offline solutions.
- Case studies demonstrate improved efficiency over existing methods.

## Abstract

Regional data center clusters have flourished in recent years to serve customers in a major city with low latency. The optimal coordination of data centers in a regional cluster has become a pressing issue because of its rising energy consumption. In this paper, a Lyapunov optimization-based online algorithm is developed for the combined computing workload and energy coordination of data centers in a regional cluster. The proposed online algorithm is prediction-free and easy to implement. We prove that the workload queues and battery energy level will be within their physical limits, though their related time-coupling constraints are not considered explicitly in the proposed algorithm. The previous online algorithms do not have such a guarantee. A theoretical upper bound on the optimality gap between the online and offline results is derived to provide a performance guarantee for the proposed algorithm. To enable distributed implementation, an accelerated ADMM algorithm is developed with iteration truncation and follow-up well-designed adjustments, whereby a nearly optimal solution is attained with much enhanced computational efficiency. Case studies show the effectiveness of the proposed method and its advantages over the existing methods.