EdgeDR: An Online Mechanism Design for Demand Response in Edge Clouds

Shutong Chen, Lei Jiao, Fangming Liu*, Lin Wang

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

The computing frontier is moving from centralized mega datacenters towards distributed cloudlets at the network edge. We argue that cloudlets are well-suited for handling power demand response to help the grid maintain stability due to more flexible workload management attributed to their distributed nature. However, they also require computing demand response to avoid overload and maintain reliability. To this end, we propose a novel online market mechanism, EdgeDR, to achieve cost efficiency in edge demand response programs. At a high level, we observe that the cloudlet operator can dynamically switch on/off entire cloudlets to compensate for the energy reduction required by the power grid or provide enough computing resources to the edge service. We formulate a long-term social cost minimization problem and decompose it into a series of one-round procurement auctions. In each auction instance, we propose to let the cloudlet tenants bid with cost functions of their two-dimension service quality degradation tolerance, and let the cloudlet operator choose the service quality, manage the workload, and schedule the cloudlet activation status. In addition, we present a dynamic payment mechanism for the operator to balance the tradeoff between short-term profit and long-term benefit in more practical scenarios. Via rigorous analysis, we exhibit that our bidding policy is individually rational and truthful; our workload management algorithm has near-optimal performance in each auction; and our overall online algorithm achieves a provable competitive ratio. We further confirm the performance of our mechanism through extensive trace-driven simulations.

Original languageEnglish
Article number9448461
Pages (from-to)343-358
Number of pages16
JournalIEEE Transactions on Parallel and Distributed Systems
Volume33
Issue number2
DOIs
Publication statusE-pub ahead of print - 1 Feb 2022

Bibliographical note

Funding Information:
This work was supported in part by NSFC under Grants 61722206 and 61761136014 (and 392046569 for DFG), and 61520106005, in part by the National Key Research & Development (R&D) Plan under Grant 2017YFB1001703, in part by the Fundamental Research Funds for the Central Universities under Grants 2017KFKJXX009 and 3004210116, in part by the National Program for Support of Top-notch Young Professionals in National Program for Special Support of Eminent Professionals. The work of L. Jiao was supported in part by the Ripple Faculty Fellowship.

Publisher Copyright:
© 1990-2012 IEEE.

Keywords

  • cloudlet control
  • Edge demand response
  • energy saving
  • online mechanism

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