Resource Management in Cloud Computing with Optimal Pricing Policies

As a new computing paradigm, cloud computing has received much attention from research and economics fields in recent years. Cloud resources can be priced according to several pricing options in cloud markets. Usage-based and reserved pricing schemes are commonly adopted by leading cloud service providers (CSPs) such as Amazon and Google. With more and more CSPs entering cloud computing markets, the pricing of cloud resources is an important issue that they need to consider. In this paper, we study how to segment cloud resources using hybrid pricing schemes in order to obtain the maximum revenue by means of optimal pricing schemes in what is a largely monopolized cloud market. We first study how the revenue of a cloud provider can be maximised using an on-demand pricing scheme. We then turn to the study of revenue maximization with a reserved pricing scheme and, finally, we compare the revenues obtained from the two pricing schemes.     

Internet Service Pricing: Flat or Volume?

In this paper, we study the pricing of Internet services under monopoly and duopoly environments using an analytic model in which a service provider and users try to maximize their respective payoffs. We compare a few popular pricing schemes, including flat, volume-based, two-part, and nonlinear tariffs, with respect to revenue, social welfare, and user surplus. We perform a study of the sensitivity of these schemes to the estimation errors. In the duopoly situation, we formulate a simple normal form game between two service providers and study their equilibrium behaviors. Our main findings include: (1) the flat pricing generates higher revenue than the pure volume pricing when the elasticity of demand is low; (2) the volume-based pricing is better for society and users than the flat pricing regardless of the elasticity; (3) the market is segmented into two when one provider provides flat pricing and another provides volume based pricing.     

Predicting customer behavior in telecommunications

The wireless service subscriber calls a customer service representative to complain about dropped calls. During the conversation with the customer, the CSR views a display that shows this customer's probability of churn-switching from this service provider to another-as well as the most probable reasons to churn and the best strategy to retain this customer. The CSR then quickly responds to the subscriber according to the system's recommendation. This is an intelligent customer-care system designed to predict customer behavior. Predicting customer churn is a component in the decision framework for retaining customers and maximizing profitability. Companies can use these probability and revenue estimates in a decision-theoretic framework to determine a churn intervention strategy and a profitability optimization strategy. Predicting customer behavior helps service providers build customer loyalty and maximize profitability. For the success of a project, data preparation is often a critical part of the predictive algorithm.     

A novel dynamic pricing scheme for contributing peers in the VoD system

In this paper we have proposed a dynamic pricing scheme for the contributing peers in the Video on Demand (VoD) system. The scheme provides an effective mechanism to maximize the profit through the residual resources of the contributing peers. A utilization function is executed for each contributing peer to estimate the utility factor based on the parameters such as initial setup cost, holding cost, chaining cost and salvage cost. In this paper, we urge an effective dynamic pricing algorithm that efficiently utilizes a range of parameters with a varying degree of complexity. The key findings of the algorithm are (i) each contributing peers are benefitted by the monetary based on its resource contributions to the VoD system and (ii) a high degree of social optimum is established by proficiently aggregating the contributing peer’s resources with the overall resources of the VoD system. We validate our claim by simulating the proposed dynamic pricing scheme with other standard pricing schemes such as altruism, cost model and game theory perspective. The result of our dynamic pricing scheme shows the best utility factor than other standard pricing schemes.     

考虑客户满意度的网络零售配送时隙定价策略

为研究网络零售配送时隙的定价问题,结合客户满意度和收益双重目标,根据预期不确认理论和层次分析法给出了基于价格、交付期长度和宽度的客户满意度模型。基于收益管理理论,引入0-1变量,给出了考虑机会成本及额外成本的收益模型。根据零售商不同的收益偏好给出了双目标条件下的时隙定价模型。通过算例模拟分析了不同的零售商收益偏好对于时隙定价以及收益的影响;根据客户在线时隙选择随机性的特点,分析了在不同交付期长度及敏感度下的时隙定价策略,以及不同的客户到达率对于收益、客户满意度的影响。分析结果得出了考虑客户满意度和不同影响因素下的时隙定价策略,以及一系列比较有指导意义的性质和方法。     

IaaS云服务定价机制的仿真研究

定价机制是影响云计算用户利益和云服务提供商收益的关键因素。本文以IaaS云服务定价机制为研究对象,首先将IaaS云服务定价机制分为固定定价和动态定价2大类,其中固定定价细分为即用即付费和预订定价2种,从理论上分析2类定价机制对IaaS云服务提供商收益的影响;然后,分别建立即用即付费和预订定价机制下IaaS云服务提供商的收益模型,并用启发式算法构建动态定价机制下IaaS云服务提供商的收益模型;最后,基于Repast Simphony建立多主体仿真模型,比较分析3种不同定价机制下IaaS云服务提供商的收益。研究结果表明,随着服务时长和顾客数量的增加,动态定价机制更能为IaaS云服务提供商带来更多收益,同时IaaS云服务提供商可以通过改变服务等级和资源价格等相关参数,改变顾客行为,使顾客数量增加,从而提高收益。     

SLA-based QoS pricing in DiffServ networks

The availability of high-speed transmission media and networking equipment in contemporary networks, as well as the evolution of quality-demanding applications has focused research interest on the provision of advanced qualitative services in addition to the traditional best-effort model of the Internet. A number of alternatives for service differentiation and QoS provisioning have been proposed and standardized, but in the case of backbone, transport networks the DiffServ architecture has prevailed, due to its scalability and deployment feasibility. The provisioning of services according to the DiffServ framework has in turn raised the requirements for interdependent, controlled resource allocation and service pricing, with particular needs for pricing mechanisms that preserve the potential and flexibility of DiffServ. At the same time, such mechanisms should reflect resource usage, allocate resources efficiently, reimburse costs or maximize service provision profits and lead customers to requesting services that will maximize their revenue. In this work, after reviewing related research, the principles that a pricing scheme for DiffServ-based services should follow are presented, stressing the differences form traditional Internet pricing. Based on these principles, an analytical approach to pricing a particular class of DiffServ-based services and a methodology for applying this approach in a real network are proposed and evaluated.     

Pricing strategies for differentiated services content delivery networks

In order to reduce the user latency, the web sites disseminate some of their information to surrogate caches located closer to the users. We discuss a simple differentiated service type architecture for content delivery networks. A pricing scheme is next proposed to complement this architecture and provide fair service to the subscribed publishers. Then, we show that the suggested pricing scheme is also the optimal (revenue maximizing) pricing scheme for a monopolistic surrogate. We investigate the performance of the pricing scheme in a duopoly and show that under certain conditions the competition results in peering of the surrogates. Finally, we suggest methods to determine the optimal number of service classes offered to the users.     

Double-sided auctions applied to vertical handover for mobility management in wireless networks

In the next generation Internet, we have seen the convergence of multimedia services and Internet with the mobility of users. Vertical handover decision (VHD) algorithm are essential components of the mobility management architecture in mobile wireless networks. VHD algorithms help mobile users to choose the best mobile network to connect among available candidates. It also can help the network manager to optimize easily the limited resources shared among the network providers and the users. In this article, we formulate VHD algorithm as a resource allocation problem for down-link transmission power in multiple W-CDMA networks and show how combinatorial double-sided auctions can be applied to this specific problem. The proposed pricing schemes make use of the signal interference to noise ratio, achievable data rates, power allocation at mobile networks, and monetary cost as decision criteria and our model differentiate between new calls and on-going communications to take into account that the last category has somewhat more importance. Several combinatorial double-sided auction are proposed to maximize the social welfare and /or to provide incentives for mobile users and mobile operators to be truth-telling in terms of valuation or cost. Finally, the economic properties of the different proposed pricing schemes are also studied by means of simulations.     

基于改进粒子群算法的网络广告配置优化

针对网络广告的特点,提出了一个基于混合定价策略的网络广告资源配置优化模型,将其建模为一个约束优化问题,最大化网站的总收益。通过罚函数法进行约束处理,提出一种改进的粒子群算法进行求解。仿真结果表明了该算法的有效性。     

Two pricing mechanisms for a service provider when customers’ delay costs are value-related

In this paper, we study two pricing mechanisms for a provider that serves delay-sensitive customers, one is the uniform pricing and the other is the priority auction. The expected delay cost of a customer is assumed to depend on his value for the service (i.e. the unit delay cost is a strictly increasing function of his value) and the expected sojourn time caused by the number of customers in the system (and his payment-based position in the queue if auction is adopted). Hence, each customer reacts to the service provider’s pricing mechanism by deciding whether or not to enter the service system and how much he pays. This resulting problem is a Stackelberg game. When auction is adopted, by using of adverse selection, we derive a feasible scheme in which customers with higher value would like to pay more. We further compare the performance of these two pricing mechanisms. Our numerical examples show that auction performs better not only in terms of revenue making but also in terms of social welfare improvement. Interestingly, auction can also render more customer surplus in most instances, which differs from the common techniques in revenue management field.     

Optimal Scheduling and Placement of Internet Banner Advertisements

The increasing popularity of the World Wide Web has made it an attractive medium for advertisers. As more advertisers place Internet advertisements (hereafter also called "ads"), it has become important for Web site owners to maximize revenue through the optimal selection and placement of these ads. Unlike most previous research, we consider a hybrid pricing model, where the price advertisers pay is a function of 1) the number of exposures of the ad and 2) the number of clicks on the ad. The problem is finding an ad schedule to maximize the Web site revenue under a hybrid pricing model. We formulate two versions of the problem - static and dynamic - and propose a variety of efficient solution techniques that provide near-optimal solutions. In the dynamic version, the schedule of ads is changed based on individual user click behavior. We show by using a theoretical proof under special circumstances and an experimental demonstration under general conditions that a schedule that adapts to the user click behavior consistently outperforms one that does not. We also demonstrate that to benefit from observing the user click behavior, the associated probability parameter need not be estimated accurately. For both of these versions, we examine the sensitivity of the revenue with respect to the model parameters.     

云计算资源分配动态定价方法

云市场用户的资源需求往往会随着时间而波动变化,在资源分配与定价时若不充分考虑供需双方的内在激励,将难以获得理想的结果。基于市场策略,设计一个组合拍卖机制来平滑用户需求,以提高资源管理效率及服务收益;所提机制以动态定价的方式向用户分配资源,实现了无妒与可信两种属性,无妒属性保证了机制运行的稳定性,而可信属性可以使得服务收益最大化;此外所提方法具有较低的计算复杂度,易于实现。实验结果表明,在短缺与饱和市场下,所提方法均可获得近似最优的收益及相对较高的社会福利。     

Detecting tax evasion: a co-evolutionary approach

We present an algorithm that can anticipate tax evasion by modeling the co-evolution of tax schemes with auditing policies. Malicious tax non-compliance, or evasion, accounts for billions of lost revenue each year. Unfortunately when tax administrators change the tax laws or auditing procedures to eliminate known fraudulent schemes another potentially more profitable scheme takes it place. Modeling both the tax schemes and auditing policies within a single framework can therefore provide major advantages. In particular we can explore the likely forms of tax schemes in response to changes in audit policies. This can serve as an early warning system to help focus enforcement efforts. In addition, the audit policies can be fine tuned to help improve tax scheme detection. We demonstrate our approach using the iBOB tax scheme and show it can capture the co-evolution between tax evasion and audit policy. Our experiments shows the expected oscillatory behavior of a biological co-evolving system.     

Utilizing unreliable public resources for higher profit and better SLA compliance in computing utilities

Computing utilities are emerging as an important part of the infrastructure for outsourcing computer services. Fundamental to outsourcing is the notion of quality of service, which is defined by service level agreements (SLAs) between the computing utilities and clients. One of the major objectives of computing utilities is to maximize their net profit while maintaining customer loyalty. To achieve this objective, the computing utilities should meet or exceed their SLA constraints most of the time. Defining the SLAs conservatively might be one way of easily achieving these goals. However, by tuning the SLA parameters conservatively the computing utility might under utilize its resources with a resultant loss of revenue. Therefore, we can see two main issues with SLA management: designing SLAs competitively so that expected revenue for the computing utility is maximized and maintaining the operating conditions such that SLAs are satisfied with very high probability. In this paper, we show that inducting unreliable public resources into a computing utility enables more competitive SLAs while maintaining higher levels of run time compliances as well as maximizing profit. Our scheduling algorithms assume that idle cycles from public resources are available in plenty, therefore, the performance gains do not incur any additional financial cost. However, there is communication overhead when public resources from a wide area network is included. This overhead is kept to the minimum by enabling the scheduler work without any monitoring on the public resources.     

A pricing strategy for job allocation in mobile grids using a non-cooperative bargaining theory framework

Due to their inherent limitations in computational and battery power, storage and available bandwidth, mobile devices have not yet been widely integrated into grid computing platforms. However, millions of laptops, PDAs and other portable devices remain unused most of the time, and this huge repository of resources can be potentially utilized, leading to what is called a mobile grid environment. In this paper, we propose a game theoretic pricing strategy for efficient job allocation in mobile grids. By drawing upon the Nash bargaining solution, we show how to derive a unified framework for addressing such issues as network efficiency, fairness, utility maximization, and pricing. In particular, we characterize a two-player, non-cooperative, alternating-offer bargaining game between the Wireless Access Point Server and the mobile devices to determine a fair pricing strategy which is then used to effectively allocate jobs to the mobile devices with a goal to maximize the revenue for the grid users. Simulation results show that the proposed job allocation strategy is comparable to other task allocation schemes in terms of the overall system response time.     

Congestion prevention in broadband wireless access systems: An economic approach

While the demand for mobile broadband wireless services continues to increase, radio resources remain scarce. Even with the substantial increase in the supported bandwidth in the next generation broadband wireless access systems (BWASs), it is expected that these systems will severely suffer from congestion, due to the rapid increase in demand of bandwidth-intensive multimedia services. Without efficient bandwidth management and congestion control schemes, network operators may not be able to meet the increasing demand of users for multimedia services, and hence they may suffer an immense revenue loss. In this paper, we propose an admission-level bandwidth management scheme consisting of call admission control (CAC) and dynamic pricing. The main aim of our proposed scheme is to provide monetary incentives to users to use the wireless resources efficiently and rationally, hence, allowing efficient bandwidth management at the admission level. By dynamically determining the prices of units of bandwidth, the proposed scheme can guarantee that the number of connection requests to the system are less than or equal to certain optimal values computed dynamically, hence, ensuring a congestion-free system. The proposed scheme is general and can accommodate different objective functions for the admission control as well as different pricing functions. Comprehensive simulation results with accurate and inaccurate demand modeling are provided to show the effectiveness and strengths of our proposed approach.     

An enhanced ABC algorithm for single machine order acceptance and scheduling with class setups

This paper considers the order acceptance and scheduling problem in a single machine environment where each customer order is characterized by a known processing time, due date, revenue, and class setup time. The objective is to maximize the total revenue. Since the problem is computationally intractable, we first conduct a preliminary study of applying the basic artificial bee colony algorithm to address the problem under study. Specifically, we design appropriate neighborhood operators with respect to the problem. Based on the results of the preliminary study and the problem characteristics, an enhanced artificial bee colony algorithm is developed with a series of modifications. The extensive experimental results indicate that the enhanced artificial bee colony algorithm is both computationally efficient and effective for large sized problem instances.     

An incentive-based electronic payment scheme for digital content transactions over the Internet

Digital content transactions through e-commerce will grow tremendous in the coming years. Well-designed electronic payment schemes and high-quality digital contents are two critical successful factors. This paper proposes an incentive-based electronic payment scheme, which can ensure both important properties of fair exchange and customer anonymity in e-commerce transactions and enhance authors’ motivation to create digital contents. The proposed payment scheme is based on cryptographic techniques. Besides, it adopts a mechanism called “the apportionment contract of sales revenue”, which records payees’ apportionment amount. The bank can immediately apportion the sales revenue according to this contract when customers complete payments. By scrupulous analyses, the proposed payment scheme can achieve fair exchange, customer anonymity, and payment security. This paper also compares the proposed payment scheme to related schemes. The comparison result shows that the proposed payment scheme has the best characteristics on the following viewpoints: incentive-based payment, fair exchange, customer anonymity, the denomination of payment token, and the number of payment token for a transaction.     

Towards real-time dynamic spectrum auctions

In this paper, we propose a low-complexity auction framework to distribute spectrum in real-time among a large number of wireless users with dynamic traffic. Our design consists of a compact and highly expressive bidding format, two pricing models to control tradeoffs between revenue and fairness, and fast auction clearing algorithms to achieve conflict-free spectrum allocations that maximize auction revenue. We develop analytical bounds on algorithm performance and complexity to verify the efficiency of the proposed approach. We also use both simulated and real deployment traces to evaluate the auction framework. We conclude that pricing models and bidding behaviors have significant impact on auction outcomes and spectrum utilization. Any efficient spectrum auction system must consider demand and spectrum availability in local regions to maximize system-wide revenue and spectrum utilization.