A truthful incentive mechanism for emergency demand response in colocation data centers

Zhang, Linquan; Ren, Shaolei; Wu, Chuan; Li, Zongpeng

doi:10.1109/infocom.2015.7218654

Cited by 90 publications

(66 citation statements)

References 21 publications

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“…We compare our mechanism with the optimal mechanism and Truth-DR [11]. The optimal mechanism, denoted by OPT, achieves the maximal social cost saving by deciding the global optimal allocation with information of all reduction signals.…”

Section: B Resultsmentioning

confidence: 99%

“…Such fairness is desirable to avoid significant reward differentiation among tenants. Nonetheless, fairness is rarely ensured in existing mechanism designs, which typically determine each winner's reward based on other tenants' bids in order to guarantee truthfulness and, consequently, provide different prices to different bidders even though they ask for the same allocation [11], [13]. By contrast, our mechanism addresses the less-studied reward fairness throughout the EDR event, in addition to truthfulness and social cost that are more common in the existing literature.…”

Section: Definition 1 (Truthfulness)mentioning

confidence: 99%

“…To the best of our knowledge, this represents the first efficient mechanism to handle online EDR signal arrivals with long-term performance guarantees. We compare FairDR with the existing best-known algorithm for colocation EDR, i.e., Truth-DR [11]. We show that FairDR achieves almost perfect fairness in rewarding yet incurs little cost increase compared to Truth-DR.…”

Section: Introductionmentioning

confidence: 99%

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Fair rewarding in colocation data centers: Truthful mechanism for emergency demand response

Sun

Ren

et al. 2015

2015 IEEE 23rd International Symposium on Quality of Service (IWQoS)

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Reducing servers' power usage in data centers upon utility's request has been emerging as a valuable demand response resource for enhancing power grid's efficiency and reliability, especially during emergency events (e.g., extreme weather) that result in electricity production shortage and put the grid in jeopardy. Nonetheless, for demand response in multitenant colocation data centers, operators may have to leverage expensive and environmentally-unfriendly diesel generation, because individual tenants manage their own servers' power usage without coordination and are typically charged by data center operators based on fixed power contracts that provide no incentives for demand response. This paper focuses on emergency demand response (EDR) and proposes an auction-based incentive mechanism, called FairDR, that incentivizes and coordinates tenants' energy reduction through financial rewards for enabling cost-effective and low-carbon EDR in colocation data center. FairDR decides tenants' energy reduction online without knowing a priori the future energy reduction requirements. It is proved that FairDR ensures tenants' truthfulness in the auction process, attains a bounded overall cost saving compared to the offline optimum which knows all the demands, and guarantees fairness (i.e., similar rewards are offered if tenants reduce the same amount of energy) that is largely absent in the existing auction mechanisms. Finally, trace-driven simulations are performed to validate our analysis and demonstrate that FairDR outperforms the existing mechanisms by improving fairness and achieving a good cost saving that is comparable to the offline optimum.

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Section: B Resultsmentioning

confidence: 99%

Section: Definition 1 (Truthfulness)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fair rewarding in colocation data centers: Truthful mechanism for emergency demand response

Sun

Ren

et al. 2015

2015 IEEE 23rd International Symposium on Quality of Service (IWQoS)

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“…A few recent studies [24]- [26] have begun to examine multi-tenant colocation demand response, but they all assume that tenants can proactively propose to reduce loads by submitting their bids. This may be subject to tenants' cheating behaviors in certain scenarios [24] (which was later resolved by [25]), and also requires tenants to be "intelligent" enough to calculate their optimal bids. By contrast, this paper takes a different approach centering around the colocation operator: Contract-DR is easier for implementation in practice since it transfers the computational burden to the colocation operator such that tenants only need to say "yes or no" to the offered contracts rather than proposing their own bids.…”

Section: Related Workmentioning

confidence: 99%

“…By contrast, this paper takes a different approach centering around the colocation operator: Contract-DR is easier for implementation in practice since it transfers the computational burden to the colocation operator such that tenants only need to say "yes or no" to the offered contracts rather than proposing their own bids. Moreover, we focus on minimizing the operator's cost, rather than the "social cost" considered in [25] that does not necessarily translate into a lower cost for colocation operator.…”

Section: Related Workmentioning

confidence: 99%

A Contract design approach for colocation data center demand response

Ahmed¹,

Islam²,

Ren³

2015

2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

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Demand response programs maintain transmission stability in power grid through reducing electricity use during peak period, making grid more eff cient and robust. While numerous demand response programs are currently being deployed by utility companies, we focus on emergency demand response program, which is critical to ensure reliability during emergency situations. As a key participant in such program, we consider a critical type of data center: multi-tenant colocation data center (or colocation), where multiple tenants mange their own servers in shared space but typically lack incentives to reduce energy for demand response. To enable multi-tenant data center demand response, we propose a contract-based mechanism, called Contract-DR, which offers f nancial incentives to tenants to shed energy during emergency situations, reducing the usage of costineffective and environmentally-unfriendly diesel generation. We conduct theoretical analysis to prove the optimality of Contract-DR and also validate it through a trace-based study.

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