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Optimization of Caching Update and pricing algorithm Based on Stochastic Geometry Theory in Video Service

IEEE ACCESS 2022年 10卷 85470-85482页

作者： Fu, Wenbin Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China

In order to meet the growing needs of vehicle users for high-quality video experience, it can be used caching technology and transcoding technology to meet the needs of vehicle users. However, due to the complexity of the practical scene, when improving the service quality of vehicle users, it is necessary to consider the mobility of vehicle users and the utilization of network resources (caching, base station computing resources, backhaul links, etc.). In order to deal with the impact of vehicle mobility and network resource usage on the acquisition of video segments by vehicle users, this paper not only takes vehicle mobility into full account in mathematical modeling, but also proposes caching update algorithm and pricing algorithm. Firstly, considering the transcoding between different versions of video, the base station takes into account the value of energy consumption when caching video segments. Then, according to the different use of caching, base station computing resources and backhaul links by vehicle users, a pricing algorithm of network resources is proposed to improve the flexibility of network resource utilization. Finally, the mathematical model is optimized by convex optimization theory, and the influence of caching update algorithm on caching gain and the impact of pricing algorithm on network resource utilization are shown through simulation. The simulation results show that the total gain, caching gain, transcoding gain and flexibility of network resource utilization are improved significantly.

关键词： Transcoding Streaming media pricing Base stations Handover Delays Resource management Cache storage Video coding Vehicle video transcoding mobility caching update pricing algorithm network resource optimization

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A Novel pricing algorithm Based on Reward-Punishment Mechanism for Supply and Demand Balancing

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FRONTIERS IN ENERGY RESEARCH 2021年 9卷

作者： Chen, Xiaofei Weng, Liguo Zhu, Haiyan Lian, Deqiang Zhejiang Univ Coll Control Sci & Engn Hangzhou Peoples R China State Grid Zhejiang Hangzhou Maoshan Power Supply Hangzhou Peoples R China Zhejiang Zhongxin Power Engn Construct Co Ltd Hangzhou Peoples R China

Demand response (DR) is a powerful tool to maintain the stability of the power system and maximize the profit of the electricity market, where the customers engage in the pricing scheme and adjust their electricity demand proactively based on the price. In DR programs, most existing works are based on the assumption that the prediction of the electricity demand from customers is always accurate and trustworthy, which will lead to high cost and fluctuation of the electricity market once the prediction is obeyed. In this paper, we design a reward and punishment mechanism to constrain customers' dishonest behaviors and propose a novel pricing algorithm based on the reward and punishment mechanism to relax the assumption, which guarantees the total electricity demands of all customers are within a secure range and obtain the maximum profit of the supplier. Meanwhile, we obtain the optimal demand and provide a upper and lower bound of the proposed price for the electricity market. In addition to a single type of customer, we also consider multiple types of customers, each of whom has different characteristics to prices. Extensive simulation results are constructed to demonstrate the effectiveness of the proposed algorithm compared with other pricing algorithms. It also shows that the average electricity consumption of a whole community is mostly affected by the residents' electricity consumption and the balance of the supply and all types of customers is achieved under the proposed pricing algorithm.

关键词： smart grid supply and demand balancing reward and punishment mechanism optimization pricing algorithm

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The Effect of Outsourcing pricing algorithms on Market Competition

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MANAGEMENT SCIENCE 2022年第9期68卷 6889-6906页

作者： Harrington, Joseph E., Jr. Univ Penn Wharton Sch Dept Business Econ & Publ Policy Philadelphia PA 19104 USA

A third party developer designs and sells a pricing algorithm that enhances a firm's ability to tailor prices to a source of demand variation, whether high-frequency demand shocks or market segmentation. The equilibrium pricing algorithm is characterized that maximizes the third party's profit given firms' optimal adoption decisions. Outsourcing the pricing algorithm does not reduce competition but does make prices more sensitive to the demand variation, and this is shown to decrease consumer welfare and increase industry profit. This effect is larger when products aremore substitutable.

关键词： pricing algorithm competition competition policy outsourcing

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Dynamic pricing algorithm with Embedded Controller for High Occupancy Toll Lanes 18

Dynamic Pricing Algorithm with Embedded Controller for High ...

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18th IEEE International Conference on Intelligent Transportation Systems

作者： Zou, Guangyu Gil, Alvaro Kulkarni, Rakesh XRCW Xerox Cluj Napoca Romania Dalian Univ Technol Panjin 124221 Liaoning Peoples R China Palo Alto Res Ctr Inc Webster NY 14580 USA

ISBN: (纸本)9781467365963

Today more and more Intelligent Transportation System (ITS) strategies are introduced nationwide to reduce congestion and maintain desired service levels on freeways. High Occupancy Toll (HOT) lanes (form of managed lanes) is one such ITS strategy. HOT lanes are intended to maintain certain level of service such as average speed, throughput, etc. One of the major challenges in achieving these objectives is the time delay (due to long stretches of HOT lanes) and the resulting uncertainty in the throughput calculated at the end of the HOT lanes. HOT lanes need to have pricing algorithms that control the traffic by considering this time delay between the checking point and the tollbooth. This paper introduces a PID controller based pricing algorithm that addresses this need. A simulation model of I-95 is used to demonstrate the effectiveness of this approach.

关键词： High Occupancy Toll Modeling and Simulation pricing algorithm

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When does it pay to invest in pricing algorithms?

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PRODUCTION AND OPERATIONS MANAGEMENT 2023年第0期

作者： Wang (Shane), Xin Li, Xi Kopalle, Praveen K. Virginia Tech Pamplin Coll Business Blacksburg VA USA Univ Hong Kong Fac Business & Econ Hong Kong Peoples R China Dartmouth Coll Tuck Sch Business Hanover NH 03755 USA

Nowadays, firms frequently use big data and pricing algorithms to offer consumers personalized prices according to their willingness to pay. Advances in information technologies have further facilitated the use of customized pricing, which we consider an important facet of transformative marketing. At the outset, personalized pricing may appear to reduce the asymmetry of information between the firm and consumers, and benefits the firm but hurts consumers. To investigate this view, we consider a novel setting in which consumers must incur search costs to make an informed purchase from the firm. Contrary to conventional wisdom, we find that personalized pricing can sometimes make both the firm and consumers better off, thus leading to a win-win situation. We also show that an imperfect pricing algorithm can outperform a perfect one, thereby explaining why certain retailers like Amazon are adopting imperfect pricing algorithms. On the one hand, a moderately reliable pricing algorithm gives high-preference consumers a chance to be misclassified as low-preference consumers and obtain a low price, thereby encouraging consumer search. On the other hand, a highly reliable pricing algorithm significantly reduces consumers' surplus, which stifles consumer search. As a result, both firm profit and consumer surplus can be nonmonotone in the reliability of the algorithm. To the best of our knowledge, this is the first paper that documents the effect of personalized pricing under consumer search.

关键词： holdup opportunism personalized pricing pricing algorithm search

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Optimal pricing and Introduction Timing of Technology Upgrades in Subscription-Based Services

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OPERATIONS RESEARCH 2023年第2期71卷 665-687页

作者： Kash, Ian A. Key, Peter B. Zoumpoulis, Spyros I. Univ Illinois Comp Sci Chicago IL 60607 USA INSEAD Decis Sci F-77305 Fontainebleau France

In the context of subscription-based services, many technologies improve over time, and service providers can provide increasingly powerful service upgrades to their customers but at a launching cost and the expense of the sales of existing products. We propose a model of technology upgrades and characterize the optimal pricing and timing of technology introductions for a service provider who price-discriminates among customers based on their upgrade experience in the face of customers who are averse to switching to improved offerings. We first characterize optimal discriminatory pricing for the infinite horizon pricing problem with fixed introduction times. We reduce the optimal pricing problem to a tractable optimization problem and propose an efficient algorithm for solving it. Our algorithm computes optimal discriminatory prices within a fraction of a second even for large problem instances. We then show that periodic introduction times, combined with optimal pricing, enjoy optimality guarantees. In particular, we first show that, as long as the introduction intervals are constrained to be nonincreasing, it is optimal to have periodic introductions after an initial warm-up phase. When allowing general introduction intervals, we show that periodic introduction intervals after some time are optimal in a more restricted sense. Numerical experiments suggest that it is generally optimal to have periodic introductions after an initial warm-up phase. Finally, we focus on a setting in which the firm does not price-discriminate based on customers' experience. We show both analytically and numerically that in the nondiscriminatory setting, a simple policy of Myerson (i.e., myopic) pricing and periodic introductions enjoys good performance guarantees.

关键词： price discrimination pricing algorithm introduction times technology upgrades subscription-based services

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Fast core pricing algorithms for path auction

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AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS 2020年第1期34卷 1-37页

作者： Cheng, Hao Zhang, Wentao Zhang, Yi Zhang, Lei Wu, Jun Wang, Chongjun Nanjing Univ Dept Comp Sci & Technol Natl Key Lab Novel Software Technol Nanjing Peoples R China

Path auction is held in a graph, where each edge stands for a commodity and the weight of this edge represents the prime cost. Bidders own some edges and make bids for their edges. The auctioneer needs to purchase a sequence of edges to form a path between two specific vertices. Path auction can be considered as a kind of combinatorial reverse auctions. Core-selecting mechanism is a prevalent mechanism for combinatorial auction. However, pricing in core-selecting combinatorial auction is computationally expensive, one important reason is the exponential core constraints. The same is true of path auction. To solve this computation problem, we simplify the constraint set and get the optimal set with only polynomial constraints in this paper. Based on our constraint set, we put forward two fast core pricing algorithms for the computation of bidder-Pareto-optimal core outcome. Among all the algorithms, our new algorithms have remarkable runtime performance. Finally, we validate our algorithms on real-world datasets and obtain excellent results.

关键词： Path auction Core pricing algorithm Constraint set

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Internet of Things Assisted Learning Strategy based Effectual Parking System

Internet of Things Assisted Learning Strategy based Effectua...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Vijayalakshmi, S. Gururaj, D. Kumar, K. Suresh Benisha, M. Mavaluru, Dinesh R.M.K. Engineering College Department of ECE Chennai Kavaraipettai India Rajalakshmi Engineering College Department of ECE Chennai India Panimalar Engineering College Department of MBA Chennai India Jeppiaar Institute of Technology Department of ECE Kunnam Chennai India Saveetha School of Engineering Department of VLSI Microelectronics Chennai India

ISBN: (纸本)9798350319200

To manage data, parking systems now depend on a centralized gateway solution (i.e., the cloud), which creates various possible failure modes, including data loss, extensive delays, and multiple points of failure. Furthermore, features that do not improve parking utilization and revenue are considered throughout the parking space selection process. Because a steady price point is chosen, the pricing strategy is not particularly lucrative. We describe a cloud-based parking management platform that makes use of IoT devices in this research. The proposed Smart Parking system employs Internet of Things (IoT) modules installed regionally to monitor and inform users to the availability of parking spots. A user may also use a mobile app to enquire about parking space availability and book a place. In addition, the paper gives an overview of the system architecture. The paper finishes with a description of system functioning provided as a use case, demonstrating the effectiveness of the proposed paradigm. © 2023 IEEE.

关键词： Internet of Things parking-spot selection pricing algorithm pricing strategy Smart Parking System

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Resource-Aware Control via Dynamic pricing for Congestion Game with Finite-Time Guarantees 21

Resource-Aware Control via Dynamic Pricing for Congestion Ga...

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21st IEEE International Workshop on Signal Processing Advances in Wireless Communications (IEEE SPAWC)

作者： Tampubolon, Ezra Ceribasic, Haris Boche, Holger Tech Univ Munich Lehrstuhl Theoret Informationstech Munich Germany Munich Ctr Quantum Sci & Technol MCQST Munich Germany

ISBN: (纸本)9781728154787

Congestion game is a widely used model for modern networked applications. A central issue in such applications is that the selfish behavior of the participants may result in resource overloading and negative externalities for the system participants. In this work, we propose a pricing mechanism that guarantees the sub-linear increase of the time-cumulative violation of the resource load constraints. The feature of our method is that it is resource-centric in the sense that it depends on the congestion state of the resources and not on specific characteristics of the system participants. This feature makes our mechanism scalable, flexible, and privacy-preserving. Moreover, we show by numerical simulations that our pricing mechanism has no significant effect on the agents' welfare in contrast to the improvement of the capacity violation.

关键词： Congestion Game Resource Allocation Decentralized algorithm Mirror Descent pricing algorithm Network Routing

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Practical closed-loop dynamic pricing in smart grid for supply and demand balancing

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AUTOMATICA 2018年 89卷 92-102页

作者： He, Jianping Zhao, Chengcheng Cai, Lin Cheng, Peng Shi, Ling Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou Zhejiang Peoples R China Univ Victoria Dept Elect & Comp Engn Victoria BC Canada Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

pricing strategy for power systems is an important and challenging problem, due to the difficulties in predicting the demand and the reactions of customers to the price accurately. Any prediction errors may result in higher costs to the supplier. To address this issue, in this paper, we propose a novel, practical closed-loop pricing algorithm (PCPA). Using the closed-loop control to well coordinate the customers and the supplier, the power system can run more efficiently, resulting in both cost saving for customers and higher profit for the supplier. We prove the convergence of PCPA, i.e., a stable price can be achieved. We provide sufficient conditions to guarantee the win-win solution for both the customers and the supplier, and an upper bound of the gain. We also provide a necessary and sufficient condition of that the highest win for both the customers and the supplier can be achieved. Extensive simulations have shown that PCPA can outperform the existing prediction-based pricing algorithms. It shows that the profit gain of the proposed algorithm can up to 100% when the total demand can be fixed to the optimal demand. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Smart grid Demand and supply pricing algorithm Win-win solution Closed-loop control

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