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Article: An Online Emergency Demand Response Mechanism for Cloud Computing

TitleAn Online Emergency Demand Response Mechanism for Cloud Computing
Authors
KeywordsApproximation algorithms
Cloud computing
Demand response
Mechanism design
Issue Date2018
PublisherACM Special Interest Group. The Journal's web site is located at http://tompecs.acm.org/
Citation
ACM Transactions on Modeling and Performance Evaluation of Computing Systems, 2018, v. 3 n. 1, article no. 5 How to Cite?
AbstractThis 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.
Persistent Identifierhttp://hdl.handle.net/10722/259905
ISSN
2023 Impact Factor: 0.7
2023 SCImago Journal Rankings: 0.525
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhou, R-
dc.contributor.authorLi, Z-
dc.contributor.authorWu, C-
dc.date.accessioned2018-09-03T04:16:14Z-
dc.date.available2018-09-03T04:16:14Z-
dc.date.issued2018-
dc.identifier.citationACM Transactions on Modeling and Performance Evaluation of Computing Systems, 2018, v. 3 n. 1, article no. 5-
dc.identifier.issn2376-3639-
dc.identifier.urihttp://hdl.handle.net/10722/259905-
dc.description.abstractThis 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.-
dc.languageeng-
dc.publisherACM Special Interest Group. The Journal's web site is located at http://tompecs.acm.org/-
dc.relation.ispartofACM Transactions on Modeling and Performance Evaluation of Computing Systems-
dc.rightsACM Transactions on Modeling and Performance Evaluation of Computing Systems. Copyright © ACM Special Interest Group.-
dc.subjectApproximation algorithms-
dc.subjectCloud computing-
dc.subjectDemand response-
dc.subjectMechanism design-
dc.titleAn Online Emergency Demand Response Mechanism for Cloud Computing-
dc.typeArticle-
dc.identifier.emailWu, C: cwu@cs.hku.hk-
dc.identifier.authorityWu, C=rp01397-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1145/3177755-
dc.identifier.scopuseid_2-s2.0-85074675817-
dc.identifier.hkuros288747-
dc.identifier.volume3-
dc.identifier.issue1-
dc.identifier.spagearticle no. 5-
dc.identifier.epagearticle no. 5-
dc.identifier.isiWOS:000426879700005-
dc.publisher.placeUnited States-
dc.identifier.issnl2376-3639-

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