SDP research papers can be roughly categorized by the different types of pricing that they investigate. We consider 15 such topics here. If you know of a paper we missed, let us know.
Pricing cloud services (21)
One major source of the recent growth in data usage is cloud computing, in which users send computational “jobs” to be completed at datacenters in the cloud. The pricing of cloud services bears some resemblance to data pricing, but cloud services are unique in requiring not just network capacity but also CPU and memory resources. Thus, effective pricing schemes must account for competition on not just one but multiple resources.
L. Zheng, C. Joe-Wong, C. G. Brinton, C. W. Tan, S. Ha and M. Chiang ACM SIGMETRICS 2016
Cloud service providers (CSPs) often face highly dynamic user demands for their resources, which can make it difficult for them to maintain consistent quality-of-service. Some CSPs try to stabilize user demands by offering sustained-use discounts to jobs that consume more instance-hours per month. These discounts present an opportunity for users to pool their usage together into a single "job." In this paper, we examine the viability of a middleman, the cloud virtual service provider (CVSP), that rents cloud resources from a CSP and then resells them to users. We show that the CVSP's business model is only viable if the average job runtimes and thresholds for sustained-use discounts are sufficiently small; otherwise, the CVSP cannot simultaneously maintain low job waiting times while qualifying for a sustained-use discount. We quantify these viability conditions by modeling the CVSP's job scheduling and then use this model to derive users' utility-maximizing demands and the CVSP's profit-maximizing price, as well as the optimal number of instances that the CVSP should rent from the CSP. We verify our results on a one-month trace from Google's production compute cluster, through which we first validate our assumptions on the job arrival and runtime distributions, and then show that the CVSP is viable under these workload traces. Indeed, the CVSP can earn a positive profit without significantly impacting the CSP's revenue, indicating that the CSP and CVSP can coexist in the cloud market.
S. Ren, and M. van der Schaar IEEE Transactions on Mobile Computing, 13(10), 2283 - 2292, 2013
In this paper, we consider a wireless cloud computing system in which the service provider operates a data center and provides cloud services to its subscribers at dynamic prices. We propose a joint optimization of scheduling and pricing decisions for delay-tolerant batch services to maximize the service provider's long-term profit. Unlike the existing research on jointly scheduling and pricing that focuses on static or asymptotic analysis, we focus on a dynamic setting and develop a provably-efficient Dynamic Scheduling and Pricing (Dyn-SP) algorithm which, without the necessity of predicting the future information, can be applied to an arbitrarily random environment that may follow an arbitrary trajectory overtime. We prove that, compared to the optimal offline algorithm with future information, Dyn-SP produces a close-to-optimal average profit while bounding the job queue length in the data center. We perform a trace-based simulation study to validate Dyn-SP. In particular, we show both analytically and numerically that a desired tradeoff between the profit and queueing delay can be obtained by appropriately tuning the control parameter. Our results also indicate that, compared to the existing algorithms which neglect demand-side management, cooling system energy consumption, and/or the queue length information, Dyn-SP achieves a higher average profit while incurring (almost) the same average queueing delay.
J. Zhao, H. Li, C. Wu, Z. Li, Z. Zhang, and F. C. M. Lau Proceedings of IEEE INFOCOM, 2014
Cloud providers often choose to operate datacenters over a large geographic span, in order that users may be served by resources in their proximity. Due to time and spatial diversities in utility prices and operational costs, different datacenters typically have disparate charges for the same services. Cloud users are free to choose the datacenters to run their jobs, based on a joint consideration of monetary charges and quality of service. A fundamental problem with significant economic implications is how the cloud should price its datacenter resources at different locations, such that its overall profit is maximized. The challenge escalates when dynamic resource pricing is allowed and long-term profit maximization is pursued. We design an efficient online algorithm for dynamic pricing of VM resources across datacenters in a geo-distributed cloud, together with job scheduling and server provisioning in each datacenter, to maximize the profit of the cloud provider over a long run. Theoretical analysis shows that our algorithm can schedule jobs within their respective deadlines, while achieving a time-average overall profit closely approaching the offline maximum, which is computed by assuming that perfect information on future job arrivals are freely available. Empirical studies further verify the efficacy of our online profit maximizing algorithm.
K. Song, Y. Yao, and L. Golubchik Proceedings of IEEE/ACM IWQoS, 2013
In Amazon's spot instance (SI) market, the volatility of the two important parameters, namely spot price (SP) and inter-price time (IPT), affects not only the market's profit, but also its service reliability. Thus, it is important for the cloud service provider to understand how SP and IPT impact the profit-reliability trade-off in the SI market. To the best of our knowledge, such a trade-off has not been studied in existing liter-ature. In this paper, we model Amazon's SI market as a modified repeated single-price auction and study the profit maximization problem using a graph model. We prove the NP-Completeness of the corresponding offline decision problem. Moreover, we propose an order-statistics based online pricing (OSOP) algorithm that can effectively evaluate and tune the profit-reliability trade-off by on-the-fly adapting SP and IPT. In our approach, SP and IPT are determined in real time based on the order statistics of the latest historical bids and the profit-reliability trade-off desired by the service providers. Our experiments show that the proposed OSOP mechanism (on average) achieves as high as ≈19% profit gain as compared to the current algorithm, with negligible reliability loss. Moreover, the mechanism also achieves a favorable trade-off between profit and service reliability, at which point our mechanism (on average) achieves ≈ 12% profit gain and ≈ 8% reduction in unexpected service interruption penalty as compared to the current algorithm.
Z. Liu, I. Liu, S. Low, and A. Wierman Proceedings of ACM SIGMETRICS, 2014
Demand response is crucial for the incorporation of renewable energy into the grid. In this paper, we focus on a particularly promising industry for demand response: data centers. We use simulations to show that, not only are data centers large loads, but they can provide as much (or possibly more) flexibility as large-scale storage if given the proper incentives. However, due to the market power most data centers maintain, it is difficult to design programs that are efficient for data center demand response. To that end, we propose that prediction-based pricing is an appealing market design, and show that it outperforms more traditional supply function bidding mechanisms in situations where market power is an issue. However, prediction-based pricing may be inefficient when predictions are inaccurate, and so we provide analytic, worst-case bounds on the impact of prediction error on the efficiency of prediction-based pricing. These bounds hold even when network constraints are considered, and highlight that prediction-based pricing is surprisingly robust to prediction error.
J. Chen, C. Wang, B. B. Zhou, L. Sun, Y. C. Lee, and A. Y. Zomaya Proceedings of HPDC'11
The recent cloud computing paradigm represents a trend of moving business applications to platforms run by parties located in different administrative domains. A cloud platform is often highly scalable and cost-effective through its pay-as-you-go pricing model. However, being shared by a large number of users, the running of applications in the platform faces higher performance uncertainty compared to a dedicated platform. Existing Service Level Agreements (SLAs) cannot sufficiently address the performance variation issue. In this paper, we use utility theory leveraged from economics and develop a new utility model for measuring customer satisfaction in the cloud. Based on the utility model, we design a mechanism to support utility-based SLAs in order to balance the performance of applications and the cost of running them. We consider an infrastructure-as-a-service type cloud platform (e.g., Amazon EC2), where a business service provider leases virtual machine (VM) instances with spot prices from the cloud and gains revenue by serving its customers. Particularly, we investigate the interaction of service profit and customer satisfaction. In addition, we present two scheduling algorithms that can effectively bid for different types of VM instances to make tradeoffs between profit and customer satisfaction. We conduct extensive simulations based on the performance data of different types of Amazon EC2 instances and their price history. Our experimental results demonstrate that the algorithms perform well across the metrics of profit, customer satisfaction and instance utilization.
L. Zheng, C. Joe-Wong, C. W. Tan, M. Chiang, and X. Wang Proceedings of ACM SIGCOMM, 2015
Amazon's Elastic Compute Cloud (EC2) uses auction-based spot pricing to sell spare capacity, allowing users to bid for cloud resources at a highly reduced rate. Amazon sets the spot price dynamically and accepts user bids above this price. Jobs with lower bids (including those already running) are interrupted and must wait for a lower spot price before resuming. Spot pricing thus raises two basic questions: how might the provider set the price, and what prices should users bid? Computing users' bidding strategies is particularly challenging: higher bid prices reduce the probability of, and thus extra time to recover from, interruptions, but may increase users' cost. We address these questions in three steps: (1) modeling the cloud provider's setting of the spot price and matching the model to historically offered prices, (2) deriving optimal bidding strategies for different job requirements and interruption overheads, and (3) adapting these strategies to MapReduce jobs with master and slave nodes having different interruption overheads. We run our strategies on EC2 for a variety of job sizes and instance types, showing that spot pricing reduces user cost by 90% with a modest increase in completion time compared to on-demand pricing.