Randomization Improving Online Time-Sensitive Revenue Maximization for Green Data Centers

TL;DR

This paper introduces a randomized online algorithm for green data centers that improves profit maximization by effectively managing uncertain job requests and green energy supplies, outperforming previous deterministic methods.

Contribution

It proposes a novel randomized online scheduling algorithm for green data centers, with competitive analysis and real trace validation, advancing beyond existing deterministic approaches.

Findings

The randomized algorithm outperforms deterministic algorithms in many scenarios.

The approach is theoretically sound with proven competitive ratios.

Real trace experiments confirm improved profit outcomes.

Abstract

Green data centers have become more and more popular recently due to their sustainability. The resource management module within a green data center, which is in charge of dispatching jobs and scheduling energy, becomes especially critical as it directly affects a center's profit and sustainability. The thrust of managing a green data center's machine and energy resources lies at the uncertainty of incoming job requests and future showing-up green energy supplies. Thus, the decision of scheduling resources has to be made in an online manner. Some heuristic deterministic online algorithms have been proposed in recent literature. In this paper, we consider online algorithms for green data centers and introduce a randomized solution with the objective of maximizing net profit. Competitive analysis is employed to measure online algorithms' theoretical performance. Our algorithm is theoretical-sound and it outperforms the previously known deterministic algorithms in many settings using real traces. To complement our study, optimal offline algorithms are also designed.