An online auction framework for dynamic resource provisioning in cloud computing

Shi, Wei; Zhang, Linquan; Wu, Chuan; Li, Zongpeng; Lau, Francis C. M.

doi:10.1145/2591971.2591980

Cited by 96 publications

(59 citation statements)

References 24 publications

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“…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%

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: Definition 1 (Truthfulness)mentioning

confidence: 99%

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)

Self Cite

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“…To tackle these challenges, we leverage recent progress in mechanism design [5,6,16,23] and propose our Auc2Charge online auction framework for park-and-charge in this paper.…”

Section: Figure 1: An Overview Of Park-and-charge Operation Modementioning

confidence: 99%

“…This framework utilize the randomized mechanism design technique [5,6,16,23]. Its basic idea is to first transform the offline PNC problem into a series of one-shot social welfare maximization problems PNCone(t), one for each time slot t. During the transformation, the reported valuation b k j (t) for the kth bid of EV user j for time slot t is adjusted to a reduced value w k j (t) based on the budget limit Bj and the auction result for user j in last time slot t − 1.…”

Section: Auc2charge: An Online Auction Frameworkmentioning

confidence: 99%

“…To address these challenges, we propose Auc2Charge, a computationally efficient online auction framework, which leverages recent progress in mechanism design [5,6,16,23]. It involves a budget-based bid update process between any consecutive two time slots [6,23], which transforms the longterm total social welfare maximization problem(PNC) into a series of one-shot social welfare maximization problems PNCone(t).…”

Section: Introductionmentioning

confidence: 99%

“…It involves a budget-based bid update process between any consecutive two time slots [6,23], which transforms the longterm total social welfare maximization problem(PNC) into a series of one-shot social welfare maximization problems PNCone(t). For each PNCone(t) problem, we design a greedy α-approximation algorithm using the classic primaldual method [5], and translate it to a randomized one-shot auction mechanism, which is truthful and individual rational.…”

Section: Introductionmentioning

confidence: 99%

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Auc2Charge

Xiang

Kong

Xue

et al. 2015

Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems

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The increasing market share of electric vehicles (EVs) makes large-scale charging stations indispensable infrastructure for integrating EVs into the future smart grid. Thus their operation modes have drawn great attention from researchers. One promising mode called park-and-charge was recently proposed. It allows people to park their EVs at a parking lot, where EVs can get charged during the parking time. This mode has been experimented and demonstrated in small scale. However, the missing of an efficient market mechanism is an important gap preventing its large-scale deployment. Existing pricing policies, e.g., pay-by-use and flatrate pricing, would jeopardize the efficiency of electricity allocation and the corresponding social welfare in the parkand-charge mode, and thus are inapplicable. To find an efficient mechanism, this paper explores the feasibility and benefits of utilizing auction mechanism in the EV park-andcharge mode. The auction allows EV users to submit and update bids on their charging demand to the charging station, which makes corresponding electricity allocation and pricing decisions. To this end, we propose Auc2Charge, an online auction framework. Auc2Charge is truthful and individual rational. Running in polynomial time, it provides an efficient electricity allocation for EV users with a close-form approximation ratio on system social welfare. Through both theoretical analysis and numerical simulation, we demonstrate the efficacy of Auc2Charge in terms of social welfare and user satisfaction.

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SipaaS: Spot instance pricing as a Service framework and its implementation in OpenStack

Toosi

Khodadadi

Buyya

2015

Concurrency and Computation

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Designing dynamic pricing mechanisms that efficiently price resources in line with a provider's profit maximization goal is a key challenge in cloud computing environments. Despite the large volume of research published on this topic, there is no publicly available software system implementing dynamic pricing for Infrastructure as a Service cloud spot markets. This paper presents the implementation of a framework called Spot instance pricing as a Service (SipaaS) that supports an auction mechanism to price and allocate virtual machine instances. SipaaS is an open-source project offering a set of web services to price and sell virtual machine instances in a spot market resembling the Amazon EC2 spot instances. Cloud providers, who aim at utilizing SipaaS, should install add-ons in their existing platform to make use of the framework. As an instance, we provide an extension to the Horizon -the OpenStack dashboard project -to employ SipaaS web services and to add a spot market environment to OpenStack. To validate and evaluate the system, we conducted an experimental study with a group of 10 users utilizing the provided spot market in a real environment. Results show that the system performs reliably in a practical test environment.A. NADJARAN TOOSI, F. KHODADADI, AND R. BUYYA Spot market, since introduced by AWS, has been considered as one of the first steps towards a full-fledged market economy for computational resources [8]. In the spot market, customers communicate their bids for an instance-hour to AWS in order to acquire required number of instances. Subsequently, AWS reports a market-wide spot price at which VM instance usage is charged while terminating instances that are executing on a bid price lower than the market price (out-of-bid situation). Prices vary independently for each instance type and available data center (or availability zone in Amazon terminology).Amazon Web Services has revealed no detailed information regarding their pricing mechanism and the computation of the spot price. At present, the design of dynamic forms of pricing for cloud computing resources is an open research challenge and of great interest to both cloud providers and researchers. An auction mechanism is truthful, if for each bidder irrespective of any choice of bid by all other bidders, the dominant strategy is to report his/her true information. We presented a pricing mechanism called Online Extended Consensus Revenue Estimate (online Ex-CORE) auction that is truthful with high probability and generates a near optimal profit for the cloud provider in [9].In this paper, we describe an open source framework called Spot instance pricing as a Service (SipaaS). SipaaS § offers a set of web services that can be used by IaaS cloud providers to run a spot market resembling the AWS spot instances. It provides services to price VM instances using the internal pricing module that works based on the online Ex-CORE auction mechanism. The extensible architecture of the SipaaS framework allows for implementation of any new pricing mechan...

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An online auction framework for dynamic resource provisioning in cloud computing

Cited by 96 publications

References 24 publications

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

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

Auc2Charge

SipaaS: Spot instance pricing as a Service framework and its implementation in OpenStack

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