云计算计费模型研究

分析并提出了一种基于用户业务相关的灵活定价策略和用户资源使用行为及对资源评价反馈的计费模型:首先,基于设定的业务类别资源定价策略关联模块,获取针对特定业务类别的资源单价策略,同时通过对云计算系统中用户的资源使用行为的分析,获得用户的资源使用行为影响因子;并依据SLA(服务等级协议)对资源提供者进行评价得到服务满意度,基于用户行为影响因子和服务满意度对计费结果进行调整。模型的实际应用表明,其具有良好的应用效果。     

基于Stackelberg博弈的WCDMA网络收益最大化计费的研究

该文研究了在WCDMA网络中如何选择价格来最大化网络收益.没有采用拥塞相关的计费,而是对每个用户有效传输的单位吞吐量收取固定的费用,但每个用户的传输速率是网络拥塞和单位带宽价格的函数,并在此基础上提出了用户净效用函数.利用Stackelberg博弈,建模网络与用户之间的交互,即一方面网络管理者设定价格,以便实现收益最大化,而用户通过自优化效用函数来寻找新的均衡点对此做出响应.本文提供了网络收益与接纳用户数目的定量关系,并研究了网络降低用户传输速率以增大网络容量和拥塞控制的经济动机.     

基于SDN框架的网络资源定价策略

针对网络中用户集中提出资源请求所造成资源负载过重和网络拥塞的问题,提出一种基于SDN架构的网络资源定价策略。首先,分析用户的需求和消费模式,确定资源交易模型。其次,结合现货消费模式和计划消费确定预约机制下资源动态定价策略模型。最后,根据所提出的资源定价策略,对资源价格进行实例仿真,结果表明,通过价格刺激用户提前预约资源,结合每个用户的信任度与消费习惯给出相应的优惠价格,可以有效地避免用户集中获取网络资源。     

无线视频通信中带宽分配策略

本文针对无线网络中多用户传输视频时带宽分配问题,提出了一种基于微观经济学中供需平衡的带宽分配策略。该方法充分考虑了运营商的收益,在运营商和用户收益同时最大且供需平衡时,所求得的频谱价格即为运营商的出售价格。同时给出了针对不同用户等级有不同的用户优先级因子,据运营商提出的带宽价格,彼此相互博弈,最终使得自身效用最大。仿真结果表明:本文提出的博弈论方法存在纳什均衡解和均衡单价;用户等级因子能按照用户的不同等级来分配带宽;系统传输质量相比以往方法有所提高。      

一种基于市场模型的网络带宽分配方法

价格手段最近被用来控制网络拥塞和管理网络资源的分配,而价格手段的一个重要特点是可以促进资源的合理分配和利用。本文提出一种基于市场模型的网络带宽分配方法,该方法根据用户的宽带需求和网络系统带宽的使用状况,计算出网络带宽的市场价格,并通过用户有的价格参数进行带宽资源的协凋与分配,分析表明,该方法可以达到优化系统效率和提高用户满意度的目标。     

基于服务质量的计费策略

先进的计费系统是提供优质服务的重要保证。采用适当的价格机制建立网络管理平台,引导用户行为,既能吸引更多用户使用网络,又能限制用户对网络资源的浪费和恶意使用,提高网络资源的利用率。在基于服务质量(QoS)的非线性计费策略中,网络资源的价格不是以单位资源统一定价,而是针对不同的业务类型,对单位资源采用不同的计费方案。文章分析了基于QoS计费策略的服务模型、计费原则以及实现该计费策略的参考模式。     

业务优先级感知的网络流量调度机制

针对网络中业务数据流过大、分布不均匀所造成的网络拥塞,提出一种优先级感知的动态网络流量调度机制.利用令牌桶算法,根据业务优先级的不同为不同业务分配不同速率的令牌,以实现业务优先级的划分;综合考虑业务的优先级及用户节点剩余缓存空间,对不同的业务采取不同的处理方式;同时,以流量到达因素、服务因素及节点缓存为指标定义了一种网络流量调度机制性能指标——分组丢失率.数值结果表明,所提机制可以对网络中业务优先级进行合理的划分,从而有效利用网络资源,预防网络拥塞,提升网络性能,为用户提供更加稳定可靠的网络服务.     

无线mesh网络中基于效用最优的覆盖多播策略

支持多播通信是无线mesh网络(WMN)的一个重要应用.采用基于效用的定价机制,通过拉格朗日(Lagrange)对偶分解法获得一个基于价格的分布式算法.以该分布式算法为核心作为无线mesh网络中数据流速率和功率调整的优化策略,每条链路根据自己转发流所消耗功率状况合理地定价,通过价格机制来调节数据流的速率,从而调整节点接收数据流的速率,使网络净效用最大化.实验结果表明该算法是有效可行的.     

流量经营的相关技术及策略

数据流量连续高速增长,移动公司面临巨大的用户需求以及运营收入的低增长双重问题,网络建设成本居高不下。必须对用户的数据流量进行有效管控,避免无效的用户数据流量占据大量的带宽。分等级为不同用户提供不同的服务能力,体现差异化服务。这些都涉及如何进行流量经营的问题,文章试图在对移动公司现网情况进行分析的基础上,提出流量经营的思路及方案。     

用户公平的活动队列管理

用户公平活动队列管理算法UFQ(User Fair Queuing)的目标是在各种网络环境中都能为所有的用户提供满意度一致的服务.UFQ采用在网络边缘标记用户所属数据报的期望服务满意度u,在网络核心根据数据报的满意度高低,结合当前数据报流经节点的拥塞程度,来决定数据报的丢弃或标记(使用ECN),从而获得不同用户一致满意的服务.UFQ不要求接纳控制和信令.它仅在网络边缘保持数据流的状态信息;只维护一个先进先出队列,通过拥塞时丢弃或标记较高满意度的数据报,在不同的用户之间公平地分配网络带宽,从而有效地控制、减轻拥塞.通过TCP/IP网络的模拟,证实了算法能够按照用户期望满意度公平地分配网络带宽,提高网络的服务质量.     

Dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in smart cities

The integration of the Internet of Vehicles (IoV) in future smart cities could help solve many traffic-related challenges, such as reducing traffic congestion and traffic accidents. Various congestion pricing and electric vehicle charging policies have been introduced in recent years. Nonetheless, the majority of these schemes emphasize penalizing the vehicles that opt to take the congested roads or charge in the crowded charging station and do not reward the vehicles that cooperate with the traffic management system. In this paper, we propose a novel dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in an urban smart city environment. The proposed system rewards the drivers that opt to take alternative congested-free ways and congested-free charging stations. We propose a token management system that serves as a virtual currency, where the vehicles earn these tokens if they take alternative non-congested ways and charging stations and use the tokens to pay for the charging fees. The proposed system is designed for Vehicular Ad-hoc Networks (VANETs) in the context of a smart city environment without the need to set up any expensive toll collection stations. Through large-scale traffic simulation in different smart city scenarios, it is proved that the system can reduce the traffic congestion and the total charging time at the charging stations.     

新型移动互联网流量差异化集中计费架构及技术研究

引入新型移动互联网流量差异化集中计费方式,采用网络旁路方式,对用户上网内容采集、进行差异化标识,并集中在一点批价计费,实现基于特定应用、特定APN及特定时间段等流量差异化集中计费,为用户提供更透明、简单和易于理解的计费方式,给运营商带来更灵活的流量经营策略,为内容提供商创造有益的竞争环境。同时,通过集中计费处理方式,有效降低投资和维护成本。     

对3GPP在线计费配额分配流程的一种优化

介绍了3GPP在线计费系统架构和在线计费场景。在业务平滑终结场景下,为了进一步简化信令流程、改善用户体验,提出了OCS主动推送配额流程,即用户充值后OCS可主动推送配额给CTF,以进一步简化信令交互流程,缩短用户充值后获得服务的时延。     

Explicit allocation of best-effort packet delivery service

This paper presents the “allocated-capacity” framework for providing different levels of best-effort service in times of network congestion. The “allocated-capacity” framework-extensions to the Internet protocols and algorithms-can allocate bandwidth to different users in a controlled and predictable way during network congestion. The framework supports two complementary ways of controlling the bandwidth allocation: sender-based and receiver-based. In today's heterogeneous and commercial Internet the framework can serve as a basis for charging for usage and for more efficiently utilizing the network resources. We focus on algorithms for essential components of the framework: a differential dropping algorithm for network routers and a tagging algorithm for profile meters at the edge of the network for bulk-data transfers. We present simulation results to illustrate the effectiveness of the combined algorithms in controlling transmission control protocol (TCP) traffic to achieve certain targeted sending rates     

Pricing in multiservice loss networks: static pricing, asymptoticoptimality and demand substitution effects

We consider a communication network with fixed routing that can accommodate multiple service classes, differing in bandwidth requirements, demand pattern, call duration and routing. The network charges a fee per call which can depend on the current congestion level and which affects user's demand. Building on the single-node results of I.Ch. Paschalidis and J.N. Tsitsiklis (see IEEE/ACM Trans. Networking, vol.8, p.171-84, 2000), we consider both problems of revenue and of welfare maximization, and show that static pricing is asymptotically optimal in a regime of many, relatively small, users. In particular, the performance of an optimal (dynamic) pricing strategy is closely matched by a suitably chosen class-dependent static price, which does not depend on instantaneous congestion. This result holds even when we incorporate demand substitution effects into the demand model. More specifically, we model the situation where price increases for a class of service might lead users to use another class as an imperfect substitute. For both revenue and welfare maximization objectives we characterize the structure of the asymptotically optimal static prices, expressing them as a function of a parsimonious number of parameters. We employ a simulation-based approach to tune those parameters and to compute efficiently an effective policy away from the limiting regime. Our approach can handle large, realistic, instances of the problem     

Impacts of subsidy regulation on the mobile market in Korea : Major provider’s diversification of handset quality

In the mobile service market, operators competitively engage in price discrimination of a bundle of handset and service over customers who have their switching costs and are locked-in under a long-term contract in return for subsidizing the handset. This paper is motivated by a recent legislation enacted in Korea (Mobile Device Distribution Improvement Act) that bans price discrimination with handset subsidy. We study the impacts of subsidy regulation on equilibrium prices, profit, and average revenue per user (ARPU) in a game approach. We set up an integrated model of price and quality competition under a duopoly structure. By comparative static and dynamic analysis, we configure the equilibrium of price and quality competition when two MNOs are differentiated in terms of handset quality, marginal cost, and market share. Our results are as follows. When two operators are randomly differentiated in quality and market share, the regulation induces only the minor operator to lower its incumbent price with adjusting quality and cost (dis)advantages. Although the regulation can lead to a drop in ARPU as the regulator wishes, it is achieved at the cost of the minor operators loss of profit if the market structure is significantly asymmetric. If two operators can choose their quality of service in the long run and if marginal costs are dependent on the quality level, quality differentiation is more likely to happen with the regulation. As subsidies are banned, the major operator now targets customers who are less sensitive to quality by degrading its quality, thus charging a lower price. When the marginal cost is independent of quality level, both operators go for the top quality. Our findings propose that the market structure, financial status of operators, channel and cost of distributing handsets before the subsidy regulation is introduced be considered fully.     

Software‐defined network‐enabled opportunistic offloading and charging scheme in multi‐unmanned aerial vehicle ecosystem

Unmanned aerial vehicles (UAVs) are autonomous fliers, which can play different roles in modern day applications. In one of the important role, UAVs can act as aerial data forwarding nodes for communication range enhancement in remote areas. UAVs form a web of drones, which can be geo‐distributed across a large area to serve various applications. However, the two major contradicting challenges with respect to multi‐UAV networks are channel congestion and flight time enhancement. The use of effective data transmission techniques to handle congestion can lead to higher battery dissipation, which in turn end up in the reduction in flight time. However, it is utmost necessity to provide an effective framework, which can provide a viable solution for handling congestion in multi‐UAV networks while enhancing the flight time of UAVs. To handle these issues, software‐defined network (SDN)–enabled opportunistic offloading and charging scheme (SOOCS) in multi‐UAV ecosystem is designed in this paper. In this scheme, an opportunistic offloading scheme is proposed, which uses an SDN‐based control model to handle congestion issues. Apart from this, an opportunistic energy‐charging scheme is designed, wherein the UAVS can either replenish their batteries using solar plates or they can wirelessly charge energy from charging points deployed at various geo‐distributed locations. The proposed scheme is evaluated using a simulation‐based study over the realistic deployment of charging points in Chandigarh City, India. The results obtained show the superiority of SOOCS over other variants of its category in terms of end‐to‐end delay, throughput, and hand‐over latency.     

Pricing network resources for adaptive applications

The Differentiated Services framework (DiffServ) has been proposed to provide multiple Quality of Service (QoS) classes over IP networks. A network supporting multiple classes of service also requires a differentiated pricing structure. In this work, we propose a pricing algorithm in a DiffServ environment based on the cost of providing different levels of services, and on long-term average user resource demand of a service class. We integrate the proposed service-dependent pricing scheme with a dynamic pricing and service negotiation environment by considering a dynamic and congestion-sensitive pricing component. Pricing network services dynamically based on the level of service, usage, and congestion allows a more competitive price to be offered, allows the network to be used more efficiently, and provides a natural and equitable incentive for applications to adapt their service requests according to network conditions. We also develop the demand behavior of adaptive users based on a physically reasonable user utility function. Simulation results show that a congestion-sensitive pricing policy coupled with user rate adaptation is able to control congestion and allows a service class to meet its performance assurances under large or bursty offered loads, even without explicit admission control. Users are able to maintain a stable expenditure, and allowing users to migrate between service classes in response to price increases further stabilizes the individual service prices. When admission control is enforced, congestion-sensitive pricing still provides an advantage in terms of a much lower connection blocking rate at high loads.     

Pricing strategies and categories for LTE networks

Long Term Evolution (LTE) systems will provide a large number of users with various high quality wireless Internet services including but not limited to voice over IP, real-time gaming, multimedia streaming and several others. A suitable pricing policy is an important component in order to bring benefits to both the operators and the customers. In fact, through this, the operator can efficiently manage the radio resources of cellular networks. For different types of services, the operator can maintain user Quality of Service and through which, the revenue can be optimized. This article analyzes various possible LTE pricing schemes, including the one proposed, based on different criteria: network load and congestion, operator revenue, traffic differentiation and user categorization. We provide comparative graphs to highlight the pros and cons of the studied pricing strategies. We highlight the importance for the operator to move from the often used flat-rate style policies towards more dynamic pricing strategies taking into account the user and service classes.     

Learning dynamic prices in MultiSeller electronic retail markets with price sensitive customers, stochastic demands, and inventory replenishments

In this paper, we use reinforcement learning (RL) as a tool to study price dynamics in an electronic retail market consisting of two competing sellers, and price sensitive and lead time sensitive customers. Sellers, offering identical products, compete on price to satisfy stochastically arriving demands (customers), and follow standard inventory control and replenishment policies to manage their inventories. In such a generalized setting, RL techniques have not previously been applied. We consider two representative cases: 1) no information case, were none of the sellers has any information about customer queue levels, inventory levels, or prices at the competitors; and 2) partial information case, where every seller has information about the customer queue levels and inventory levels of the competitors. Sellers employ automated pricing agents, or pricebots, which use RL-based pricing algorithms to reset the prices at random intervals based on factors such as number of back orders, inventory levels, and replenishment lead times, with the objective of maximizing discounted cumulative profit. In the no information case, we show that a seller who uses Q-learning outperforms a seller who uses derivative following (DF). In the partial information case, we model the problem as a Markovian game and use actor-critic based RL to learn dynamic prices. We believe our approach to solving these problems is a new and promising way of setting dynamic prices in multiseller environments with stochastic demands, price sensitive customers, and inventory replenishments.