Carbon-Aware Computing for Data Centers with Probabilistic Performance Guarantees

TL;DR

This paper presents a probabilistic, load-shaping approach for data centers that optimizes job placement and scheduling to reduce carbon emissions and peak power usage while providing performance guarantees.

Contribution

It introduces a novel risk-aware, distributionally robust optimization framework for dynamic job scheduling in data centers with provable guarantees.

Findings

Significant reduction in carbon cost and peak power usage.

Effective real-time adaptation to grid signals.

Maintains computational efficiency and system constraints.

Abstract

Data centers are significant contributors to carbon emissions and can strain power systems due to their high electricity consumption. To mitigate this impact and to participate in demand response programs, cloud computing companies strive to balance and optimize operations across their global fleets by making strategic decisions about when and where to place compute jobs for execution. In this paper, we introduce a load shaping scheme which reacts to time-varying grid signals by leveraging both temporal and spatial flexibility of compute jobs to provide risk-aware management guidelines and job placement with provable performance guarantees based on distributionally robust optimization. Our approach divides the problem into two key components: (i) day-ahead planning, which generates an optimal scheduling strategy based on historical load data, and (ii) real-time job placement and (time) scheduling, which dynamically tracks the optimal strategy generated in (i). We validate our method in simulation using normalized load profiles from randomly selected Google clusters, incorporating time-varying grid signals. We can demonstrate significant reductions in carbon cost and peak power with our approach compared to myopic greedy policies, while maintaining computational efficiency and abiding to system and grid constraints.