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.

On the Viability of a Cloud Virtual Service Provider

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.

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Dynamic Scheduling and Pricing in Wireless Cloud Computing

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.

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Dynamic pricing and profit maximization for the cloud with geo-distributed data centers

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.

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Exploring the profit-reliability trade-off in Amazon's spot instance market: A better pricing mechanism

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.

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Pricing data center demand response

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.

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Tradeoffs Between Profit and Customer Satisfaction for Service Provisioning in the Cloud

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.

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How to Bid the Cloud

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.

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We study the infinite horizon dynamic pricing problem for an infrastructure cloud provider in the emerging cloud computing paradigm. The cloud provider, such as Amazon, provides computing capacity in the form of virtual instances and charges customers a time-varying price for the period they use the instances. The provider's problem is then to find an optimal pricing policy, in face of stochastic demand arrivals and departures, so that the average expected revenue is maximized in the long run. We adopt a revenue management framework to tackle the problem. Optimality conditions and structural results are obtained for our stochastic formulation, which yield insights on the optimal pricing strategy. Numerical results verify our analysis and reveal additional properties of optimal pricing policies for the infinite horizon case.

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Present or future: Optimal pricing for spot instances

P. Wang, Y. Qi, D. Hui, L. Rao and X. Liu  Proceedings of IEEE ICDCS, 2013

The recent years witnessed rapid emergence and proliferation of cloud computing. To fully utilize the compute resources, some cloud operators provide spot resources. Spot resources allow customers to bid on unused capacity. However, pricing policy of spot resources should be carefully designed and the impact on both present and future should be considered. For the present, the cloud provider can set a higher price to gain extra revenue. For the future, higher price will shift more requests with lower prices to later time and reduce the revenue of future. Meanwhile, the quality of service should be considered either since bad QoS will incur loss of potential users. In this paper, we present a demand curve to model the impact of pricing on the present and future revenue. Then we formulate the revenue maximization problem as a time-average optimization problem. Next, since this basic model fails to provide information of service delay, we extend it to a more generalized one that ensures the worst-case delay of user requests. While the future knowledge of arrival requests is unknown, it is necessary to design online algorithms for the optimization problems. We apply Lyapunov optimization framework and design an efficient online algorithm which dose not require any future knowledge of requests arrival. Evaluations based on real-life datacenter workload and Amazon EC2 Spot Price illustrate efficiency of our algorithms.

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Resource pricing game in geo-distributed clouds

H. Roh, C. Jung, Lee and D.-Z. Du  Proceedings of IEEE INFOCOM, 2013

Cloud computing enables larger classes of application service providers to distribute their services to world-wide users in multiple regions without their own private data centers. Heterogeneity and resource limitation of geo-graphically distributed cloud data centers impose application service providers to have incentives to optimize their computing resource usage while guaranteeing some level of quality of service. Recent studies proposed various techniques for optimization of computing resource usage from cloud users (or application service providers) perspective with little consideration of competition. In addition, optimization efforts of application service providers motivate cloud service providers owning multiple geo-distributed clouds to decide their computing resource prices considering their efforts. In this context, we formulate this problem for cloud service providers as a game of resource pricing in geo-distributed clouds. One of the main challenges in this problem is how to model the best responses of application service providers, given resource price information of clouds in non-overlapped regions. We propose a novel concave game to describe the quantity competition among application service providers reducing payment while guaranteeing fair service delay to end users. Furthermore, we optimize the prices of computing resources to converge to the equilibrium. In addition, we show several characteristics of the equilibrium point and discuss their implications to design computing resource markets for geo-distributed clouds.

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A framework for truthful online auctions in cloud computing with heterogeneous user demands

H. Zhang, B. Li, H. Jiang, F. Liu, A. V. Vasilakos and J. Liu  Proceedings of IEEE INFOCOM, 2013

The paradigm of cloud computing has spontaneously prompted a wide interest in market-based resource allocation mechanisms by which a cloud provider aims at efficiently allocating cloud resources among potential users. Among these mechanisms, auction-style pricing policies, as they can effectively reflect the underlying trends in demand and supply for the computing resources, have attracted a research interest recently. This paper conducts the first work on a framework for truthful online cloud auctions where users with heterogeneous demands could come and leave on the fly. Our framework desirably supports a variety of design requirements, including (1) dynamic design for timely reflecting fluctuation of supply-demand relations, (2) joint design for supporting the heterogeneous user demands, and (3) truthful design for discouraging bidders from cheating behaviors. Concretely speaking, we first design a novel bidding language, wherein users' heterogeneous demands are generalized to regulated and consistent forms. Besides, building on top of our bidding language we propose COCA, an incentive-Compatible (truthful) Online Cloud Auction mechanism based on two proposed guidelines. Our theoretical analysis shows that the worst-case performance of COCA can be well-bounded. Further, in simulations the performance of COCA is seen to be comparable to the well-known off-line Vickrey-Clarke-Groves (VCG) mechanism [11].

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Dynamic pricing and profit maximization for the cloud with geo-distributed data centers

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.

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Pricing and resource allocation in a cloud computing market

L. Du  Proceedings of IEEE/ACM CCGrid, 2012

The problem is identified as a pricing driven virtual machine (VM) revenue maximization problem with Markovian traffics in a cloud market composed of hybrid cloud and public cloud. A pooled resource allocation model is built followed by numerical tests. The results indicate that hybrid cloud performs the best and the resource allocation model helps optimally allocate the pooled resources.

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Dynamic resource provisioning in cloud computing: A randomized auction approach

L. Zhang, Z. Li and C. Wu  Proceedings of IEEE INFOCOM, 2014

This work studies resource allocation in a cloud market through the auction of Virtual Machine (VM) instances. It generalizes the existing literature by introducing combinatorial auctions of heterogeneous VMs, and models dynamic VM provisioning. Social welfare maximization under dynamic resource provisioning is proven NP-hard, and modeled with a linear integer program. An efficient α-approximation algorithm is designed, with α ~ 2.72 in typical scenarios. We then employ this algorithm as a building block for designing a randomized combinatorial auction that is computationally efficient, truthful in expectation, and guarantees the same social welfare approximation factor α. A key technique in the design is to utilize a pair of tailored primal and dual LPs for exploiting the underlying packing structure of the social welfare maximization problem, to decompose its fractional solution into a convex combination of integral solutions. Empirical studies driven by Google Cluster traces verify the efficacy of the randomized auction.

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Revenue maximization using adaptive resource provisioning in cloud computing environments

G. Feng, S. Garg, R. Buyya and W. Li  Proceedings of the ACM/IEEE 13th International Conference on Grid Computing, 2012

Compared with the traditional computing models such as grid computing and cluster computing, a key advantage of Cloud computing is that it provides a practical business model for customers to use remote resources. However, it is challenging for Cloud providers to allocate the pooled computing resources dynamically among the differentiated customers so as to maximize their revenue. It is not an easy task to transform the customer-oriented service metrics into operating level metrics, and control the Cloud resources adaptively based on Service Level Agreement (SLA). This paper addresses the problem of maximizing the provider's revenue through SLA-based dynamic resource allocation as SLA plays a vital role in Cloud computing to bridge service providers and customers. We formalize the resource allocation problem using Queuing Theory and propose optimal solutions for the problem considering various Quality of Service (QoS) parameters such as pricing mechanisms, arrival rates, service rates and available resources. The experimental results, both with the synthetic dataset and with traced dadataset, show that our algorithms outperform related work.

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Socially optimal pricing of cloud computing resources

I. Menache, A. Ozdaglar and N. Shimkin  Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools, 2011

The cloud computing paradigm offers easily accessible computing resources of variable size and capabilities. We consider a cloud-computing facility that provides simultaneous service to a heterogeneous, time-varying population of users, each associated with a distinct job. Both the completion time, as well as the user's utility, may depend on the amount of computing resources applied to the job. In this paper, we focus on the objective of maximizing the long-term social surplus, which comprises of the aggregate utility of executed jobs minus load-dependent operating expenses. Our problem formulation relies on basic notions of welfare economics, augmented by relevant queueing aspects. We first analyze the centralized setting, where an omniscient controller may regulate admission and resource allocation to each arriving job based on its individual type. Under appropriate convexity assumptions on the operating costs and individual utilities, we establish existence and uniqueness of the social optimum. We proceed to show that the social optimum may be induced by a single per-unit price, which charges a fixed amount per unit time and resource from all users.

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Review of pricing models for grid and cloud computing

P. Samini and A. Patel  Proceedings of the IEEE Symposium on Computers and Informatics, 2011

Distributed system resources have become prevalent in ICT departments to lessen the burden of huge expenses incurred by very expensive storage computer systems. Add to this the continuous introduction and ever-growing evolution of simple to complex applications, the demand to access huge quantities of data, intensive computations, powerful simulations, maintaining and offering system resources and middleware infrastructure services the need to do all of this at an affordable and reasonable price is crucial. Distributed grid and cloud computing resources are currently considered to be one of the best technology options to provide this. They have many similar features and functions, and both of them are classed as distributed systems. They are capable of offering unaffordable resources and services at a reasonable price in a mass marketplace. The big question is: what is a reasonable price? How is pricing modeled and on what kind of economic principles is it based? Much of the issues surrounding these questions are very complex in themselves. This paper provides a comparative review of grid and cloud computing economic and pricing models from which appropriate tariffs and charging models can be chosen to meet particular business objectives. The actual choice depends on many other factors like enterprise regulations, tax laws, service level agreements and return on investments, are very important but outside the scope of this paper. In this paper we give the basic core principles and a comparative review of the latest and most appropriate economic and pricing models applicable to grid and cloud computing in order to propose better models for the future.

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Price comparison for Infrastructure-as-a-Service

E. K. Siham, C. Schlereth and B. Skiera  Proceedings of ECIS, 2012

Today's pricing of infrastructure-as-a-service is not transparent because some providers, such as Google, charge separately for each service characteristic (e.g., $50 per CPU or $15 per GB of memory per month) and let customers freely configure the service. In contrast, competitors like Amazon, Microsoft, and IBM only offer predefined bundles (e.g., 4 GB of memory, 400 GB of storage, and 2 CPUs for $140 per month). These different types of pricing plans make price comparisons very difficult. The aim of this study is to increase price transparency among providers by proposing two price comparison methods, which provide a detailed overview of the billing situation in the infrastructure-as-a-service market. The first method is "hedonic pricing", which decomposes each provider's billing into the contributing values of the product´s characteristics. The second is a new method, called PriCo ("Pricing plan Comparison"), which in addition considers offers from competitive providers. We employ the two methods in an empirical study, which compares the pricing of the infrastructure-as-a-service providers Google, Microsoft, Amazon, IBM, and Terremark. The insights gained allow customers to better identify the best provider for their needs. The proposed methods also help providers to better understand and position their pricing in the market.

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Resource pricing and equilibrium allocation policy in cloud computing

F. Teng and F. Magoules  Proceedings of the 10th IEEE International Conference on Computer and Information Technology, 2010

Cloud computing is a new emerging computing paradigm that advocates supplying users everything as a service. Compared with grid computing, the focus of resource management problem is transformed to resource virtualization and allocation rather than job decomposition and scheduling. It is more urgent to find better solutions for cloud resource allocation than ever before. Although there have been some research efforts in grid computing, most of them aim at maximizing utility of system and lack of analysis for competition between different users. Some researches consider competition analysis, but they assume that common knowledge is certain and known for every user, which is difficult to be applied in a global distributed cloud environment. In this paper, we hereby propose a new resource pricing and allocation policy where users can predict the future resource price as well as satisfy budget and deadline constraints. Experimental results prove that resource price can gradually converge to an equilibrium state by dynamic games and that cloud users can receive Nash equilibrium allocation proportion without other competitors' bidding information.

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Dynamic resource pricing on federated clouds

M. Mihailescu and Y. M. Teo  Proceedings of IEEE/ACM CCGrid, 2010

Current large distributed systems allow users to share and trade resources. In cloud computing, users purchase different types of resources from one or more resource providers using a fixed pricing scheme. Federated clouds, a topic of recent interest, allows different cloud providers to share resources for increased scalability and reliability. However, users and providers of cloud resources are rational and maximize their own interest when consuming and contributing shared resources. In this paper, we present a dyanmic pricing scheme suitable for rational users requests containing multiple resource types. Using simulations, we compare the efficiency of our proposed strategy-proof dynamic scheme with fixed pricing, and show that user welfare and the percentage of successful requests is increased by using dynamic pricing.

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Distributed systems meet economics: Pricing in the cloud

H. Wang, Q. Jing, R. Chen, B. He, Z. Qian and L. Zhou  Proceedings of USENIX HotCloud, 2010

Cloud computing allows users to perform computation in a public cloud with a pricing scheme typically based on incurred resource consumption. While cloud comput- ing is often considered as merely a new application for classic distributed systems, we argue that, by decoupling users from cloud providers with a pricing scheme as the bridge, cloud computing has fundamentally changed the landscape of system design and optimization. Our pre- liminary studies on Amazon EC2 cloud service and on a local cloud computing testbed, have revealed an interest- ing interplay between distributed systems and economics related to pricing. We believe that this new angle of look- ing at distributed systems potentially fosters new insights into cloud computing.

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