A Truthful FPTAS Mechanism for Emergency Demand Response in Colocation Data Centers

TL;DR

This paper introduces MEDR, a truthful, efficient FPTAS mechanism for emergency demand response in colocation data centers, optimizing energy reduction while ensuring truthful participation and practical applicability.

Contribution

The paper formalizes the MEDR problem, designs a truthful FPTAS mechanism, and demonstrates its effectiveness through simulations on real smart grid data.

Findings

Mechanism is truthful and approximates within 1+ε

Achieves polynomial running time in number of tenants and 1/ε

Effective in reducing social cost and ensuring tenant utility

Abstract

Demand response (DR) is not only a crucial solution to the demand side management but also a vital means of electricity market in maintaining power grid reliability, sustainability and stability. DR can enable consumers (e.g. data centers) to reduce their electricity consumption when the supply of electricity is a shortage. The consumers will be rewarded in the case of DR if they reduce or shift some of their energy usage during peak hours. Aiming at solving the efficiency of DR, in this paper, we present MEDR, a mechanism on emergency DR in colocation data center. First, we formalize the MEDR problem and propose a dynamic programming to solve the optimization version of the problem. We then design a deterministic mechanism as a solution to solve the MEDR problem. We show that our proposed mechanism is truthful. Next, we prove that our mechanism is an FPTAS, i.e., it can be approximated within $1 + \epsilon$ for any given $\epsilon > 0$, while the running time of our mechanism is polynomial in $n$ and $1/\epsilon$, where $n$ is the number of tenants in the datacenter. Furthermore, we also give an auction system covering the efficient FPTAS algorithm as bidding decision program for DR in colocation datacenter. Finally, we choose a practical smart grid dataset to build a large number of datasets for simulation in performance evaluation. By evaluating metrics of the approximation ratio of our mechanism, the non-negative utility of tenants and social cost of colocation datacenter, the results demonstrate the effectiveness of our work.