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An Online Emergency Demand Response Mechanism for Cloud Computing

Published: 13 February 2018 Publication History

Abstract

This article studies emergency demand response (EDR) mechanisms from a data center perspective, where a cloud participates in a mandatory EDR program while receiving computing job bids from cloud users in an online fashion. We target a realistic EDR mechanism where (i) the cloud provider dynamically packs different types of resources on servers into requested VMs and computes job schedules to meet users’ requirements, (ii) the power consumption of servers in the cloud is limited by the grid through the EDR program, and (iii) the operation cost of the cloud is considered in the calculation of social welfare, measured by an electricity cost that consists of both volume charge and peak charge. We propose an online auction for dynamic cloud resource provisioning that is under the control of the EDR program, runs in polynomial time, achieves truthfulness, and close-to-optimal social welfare for the cloud ecosystem. In the design of the online auction, we first propose a new framework, compact exponential LPs, to handle job scheduling constraints in the time domain. We then develop a posted pricing auction framework toward the truthful online auction design, which leverages the classic primal-dual technique for approximation algorithm design. We evaluate our online auctions through both theoretical analysis and empirical studies driven by real-world traces.

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Published In

cover image ACM Transactions on Modeling and Performance Evaluation of Computing Systems
ACM Transactions on Modeling and Performance Evaluation of Computing Systems  Volume 3, Issue 1
March 2018
124 pages
ISSN:2376-3639
EISSN:2376-3647
DOI:10.1145/3186330
  • Editors:
  • Sem Borst,
  • Carey Williamson
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 February 2018
Accepted: 01 December 2017
Revised: 01 September 2017
Received: 01 June 2016
Published in TOMPECS Volume 3, Issue 1

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Author Tags

  1. Cloud computing
  2. approximation algorithms
  3. demand response
  4. mechanism design

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  • Research-article
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  • Refereed

Funding Sources

  • NSFC
  • Hubei Science Foundation
  • Research Grants Council of Hong Kong

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