Improving the Revenue, Efficiency and Reliability in Data Center Spot Market: A Truthful Mechanism

Abstract

Data centers are typically over-provisioned, in order to meet certain service level agreements (SLAs) under worst-case scenarios (e.g., peak loads). Selling unused instances at discounted prices thus is a reasonable approach for data center providers to off-set the maintenance and operation costs. Spot market models are widely used for pricing and allocating unused instances. In this paper, we focus on mechanism design for a data center spot market (DCSM). Particularly, we propose a mechanism based on a repeated uniform price auction, and prove its truthfulness. In the mechanism, to achieve better quality of service, the flexibility of adjusting bids during job execution is provided, and a bidding adjustment model is also discussed. Four metrics are used to evaluate the mechanism: in addition to the commonly used metrics in auction theory, namely, revenue, efficiency, slowdown and waste are defined to capture the Quality of Service (QoS) provided by DCSMs. We prove that a uniform price action achieves optimal efficiency among all single-price auctions in DCSMs. We also conduct comprehensive simulations to explore the performance of the resulting DCSM. The result show that (1) the bidding adjustment model helps increase the revenue by an average of 5%, and decrease the slowdown and waste by average of 5% and 6%, respectively, (2) our model with repeated uniform price auction outperforms the current Amazon Spot Market by an average of 14% in revenue, 24% in efficiency, 13% in slowdown, and by 14% in waste. Parameter tuning studies are also performed to refine the performance of our mechanism.