Efficiency of Dynamic Pricing in Priority-based Contents Delivery Networks
|
|
- Anis Lucas
- 6 years ago
- Views:
Transcription
1 Efficiency of Dynamic Pricing in Priority-based Contents Delivery Networks Noriyuki YAGI, Eiji TAKAHASHI, Kyoko YAMORI, and Yoshiaki TANAKA, Global Information and Telecommunication Institute, Waseda Unviersity Nishi-Waseda, Shinjuku-ku, Tokyo, Japan Tel: Fax: Advanced Research Institute for Science and Engineering, Waseda University 17 Kikuicho, Shinjuku-ku, Tokyo, Japan Abstract A large amount of content is frequently delivered to users in contents delivery networks. The traffic for this content causes heavy congestion, especially during peak-usage hours. If the content is delivered on a best effort basis during peak-usage hours in a transport network, users cannot estimate the download completion time when they start using a service. This problem can be solved by providing a guaranteed bandwidth service. However, to provide this in content delivery networks, the transport network requires a large capacity to deal with peak traffic even if many of the resources are not used in off-peak hours. To avoid the need to build additional capacity to meet peak usage demands, price incentives can be used to shift traffic from peak hours to off-peak hours efficiently. In a price based content delivery system, the tariff of several service classes is shown to each user in response to the user s request for content. Each user chooses one of the service classes according to his own need. By setting the tariff adaptively, a guaranteed bandwidth service can be provided without using extra capacity, and users who selected the guaranteed class can estimate the download completion time. In this paper, two pricing methods are proposed. One is a time functional method and the other is a waiting-time dependent method. These proposed methods are compared with a conventional fixed pricing method. We show that by using the proposed pricing algorithms, a traffic is balanced between peak hours and off-peak hours, and the sum total of users utility is increased. Key Words Contents Delivery, Quality of Service, Waiting Time, Pricing, Utility 1. Introduction Content delivery will become more popular in the near future and it is thought the traffic will become dominant in the network. There are many kinds of content that are delivered in services, such as HTML documents, pictures, softwares, movies, etc. In contents delivery networks, large-size content is frequently delivered. This traffic causes heavy congestion, especially during peak-usage hours. If the content is delivered on a best effort basis in a transport network, users cannot estimate the download completion time. This problem can be solved by providing a guaranteed bandwidth service. However, to provide this in a content delivery network, the transport network requires a large capacity to deal with peak traffic. However, many of the resources are not used in off-peak hours. Consequently, we propose a market-based approach which tries to shift the traffic from peak hours to off-peak hours efficiently. Using this method, a guaranteed bandwidth service can be provided without extra capacity and users can estimate the download completion time. This paper proposes two pricing methods: a time functional approach and a waiting-time dependent approach. Time functional pricing is a kind of spot pricing and waiting-time dependent pricing is a kind of dynamic pricing. In time functional pricing, the prices of off-peak hours and peak hours are set up separately. Users have to pay a high price if they need content delivered instantly during peak hours. On the other hand, the price is low in off-peak hours. Therefore, user s demand will be stimulated. It is thus necessary to predict accurate
2 traffic. If traffic prediction is wrong, the effect of this pricing method decreases. In waiting-time dependent pricing the prices are set up based on the current state of the network. Compared with time functional pricing, there is no need to predict traffic accurately. Since the price is set up according to the network state, the effect of pricing does not decrease in any particular situation. We compare these proposed methods with a simple fixed pricing method. In that system, a table of service levels and corresponding prices (tariff) is shown to each user at the start of each service and the user chooses one of the service classes. The effectiveness of these systems is evaluated from the viewpoint of the users utility. 2. Market-based Priority Control in Contents Delivery Network 2.1 Framework We consider the market-based content delivery system, which is shown in Figure 1. Let s look at the case of two service classes to simplify the problem. The priority class is the class that waiting time is guaranteed and non-priority class is the best effort class. First, a user request is sent to the server. Then, the server shows the waiting time, which is calculated from the congestion state of the network, and the price of a priority class. Each user then chooses one of the service classes shown in the tariff. The content is delivered so as to keep the waiting time that is promised at the start of each service. The price of the non-priority class is fixed to 0. On the other hand, the charge for a priority class is determined adaptively according to the congestion state of the network. The requests of the same class is delivered on the basis of a FIFO system. 2.2 Time Functional Pricing SERVER Figure 1 set tariff (1) request (4) content (3) selected class In the case of fixed pricing method, the congestion is controlled by setting prices higher. However, the users demands at off-peak hours also decrease. Time functional pricing does not have this problem and thus is often successfully used in telephone services by telecommunication common carriers. There is a day periodicity in a users demand pat- select service class (2) tariff USER Market-based Content delivery system. tern. Although time, day, week and month periodicity is also considered in spot pricing, we consider only time periodicity because this influences efficiency a lot for networks. Therefore, we consider time functional pricing. We define off-peak and peak hours, as shown in Figure 2. Let C 0 denote minimum price, ie. the price when the network is on off-peak hours. Let t denote the arrival time of the request. Then, price C of the priority class is defined as follows: C 0 0 t t 1, C = C 0 + a 1 (t t 1 ) t 1 <t t peak, (1) C 0 + a 2 (t t 2 ) t peak <t t 2, where a 1, a 2 is a constant value set up for the priority class. Let C max denote the maximum price, ie. the price when the network is congested. Then a 1, a 2 is decided as: a 1 = C max C 0 t peak t 1, a 2 = C 0 C max t 2 t peak. (2) 2.3 Waiting-Time Dependent Pricing In this method the price is set up to reflect the queue length in the buffer of the priority class. For time functional pricing it is necessary to predict users demand pattern accurately to achieve effective transmission of content. However, it is not efficient when the accuracy of the predictions deteriorates. On the other hand, waiting-time dependent pricing performs well without predicting the users demand pattern. To manage transmission of content, the number of each request and size of content are stored in the buffer.
3 Arrival Rate 3. Simulation Models 3.1 Network Model To simplify the problem, a single link with a total capacity of B as shown in Figure 4 is considered in the simulation. Off-Peak Time Peak Time Off-Peak Time USER 1 t 1 t peak t 2 Hours of the day SERVER B USER 2 Figure 2 Distribution of arrival rate. USER N 1.0 Figure 4 Network model. Price W 1 W 2 W 3 W 4 W 5 W 6 Queue Length (Waiting Time) Figure 3 Price setting function for waiting-time dependent pricing. These requests are sorted in order of arrival. Requests for the priority class is delivered preferentially. The request for a non-priority class is delivered when there is no request in the buffer for a priority class. Then the queue length of the priority class data is given as: N Q priority = S j /B, (3) j=1 where B denotes the bandwidth of a bottleneck link in the content delivery network, where j = {1, 2,,N} and S j denotes the number of each requests of the priority class and the data size for each content. Prices are set up by calculating the waiting time and refering to Figure Traffic Model To simplify the calculation it is assumed that the arriving packets follow a Poisson distribution. Let us assume that the standardized mean arrival rate of requests for content is given as shown in Figure 5, where λ and λ 0 are the mean arrival rate and the mean arrival rate during the off-peak hours, respectively [2]. Demand Pattern 1 of the call arrival rate, shown in Figure 5, was given by two test services of VoD [3], [4]. Moreover, Demand Pattern 2 used in the simulation to examine the case when the accuracy of the demand prediction deteriorates. Then, λ 0 is decided as: λ 0 [1/sec] = R[1/day]/( ), (4) where R is the number of average demands during the day. 3.3 User Model User s Utility The utility function is used to measure how a user evaluates the priority service class shown in the tariff. Let W denote the waiting time shown in the tariff. Let us define the user s utility function U(W )as[5]: U(W )=Dexp( kw), (5) where parameter k (k > 0) expresses the sensitivity of each user against the waiting time. When the value of
4 Arrival Rate λ/λ Demand Pattern 1 Demand Pattern 2 t 1 t peak t Hours of the day The values of parameters a 1, a 2 of Equation (1) are given by substituting the values of C 0 and C max for Equation (2). 3.5 Waiting-Time Dependent Pricing The waiting time is partitioned off by 5 [min] and the price is set up for each section. Let l = {1, 2,,n} denote the ID of sections for waiting-time and C l the price of section l. The price of each section is set so as to fulfill the following conditions. Figure 5 rate. Distribution of standardized mean arrival 3.6 Parameters 0 C l 1, C l C l+1. (8) k is larger, the user s utility decreases rapidly when the delivery time becomes longer. On the other hand, if the value of k is close to 0, a user s utility has only a small influence on the waiting time. Parameter k can be statistically estimated by opinion tests [5]. In the simulation, however, we assume that there are many kind of users and we investigate the total utility when the value of k changes. Moreover, the parameter D expresses the user s utility at the time W = 0. Since each user has his own valuation on the service, the value of D is given randomly on (0,1]. User s Behavior Let us suppose each user behaves like the following as determined by prices. Let us define payoff H s to user s as [6]: H s = U(W ) C, (6) where W and C denote the waiting time and charge for the priority class shown in the tariff. When the payoff for priority class is positive he will select the priority class. And when the payoff is zero or less he will select the non-priority class. 3.4 Time Functional Pricing The parameters of Equation (1) are set as following. The parameters are t 1 = 18, t 2 = 24 and t peak = 21, as shown in Figure 5. Moreover, the parameters C 0, C max of Equation (2) are set up to fullfill the following conditions. 0 C 0,C max 1, C 0 C max. (7) Individual parameters used in the simulation are shown in Table 1. Table 1 Parameters. Parameter Value k [0.1,0.9] R 747 number of service classes 2 size of each content 650 MB capacity of bottleneck link 50 Mbps 4. Simulation Results 4.1 Optimal Price Set Time functional pricing Table 2 shows the set of optimal prices for Demand Pattern 1. The sum of users utility is maximized when we set the price according to Table 2. Parameter k is changed by 0.1 on[0.1, 0.9], and the optimal price set is calculated for each value of parameter k. The optimal price set is not so dependant on the value of k, as shown in Table 2. Waiting-time dependent pricing We calculated the optimal price set for waiting-time dependent pricing for Demand Pattern 1 for each value of k. Next, we derived the approximated curve of the optimal price, shown in Equation (9). The ratio of total users utility when using the optimal price set and when using the approximated curve is 1.00 : 0.99 at any value of parameter k. Therefore, Equation (9) can be used to get the approximation of the optimal price whatever the value of parameter k.
5 C(W )=0.2542Ln(W ) (9) Table 2 Optimal Price Set for Time Functional Pricing. k Off-peak price C 0 Peak Price C max Standardized total utility Standardized total utility for Demand Pat- Figure 6 tern 1. Fixed Pricing Time Functional Pricing Waiting-Time Dependent Pricing Parameter k 4.2 Comparison of Pricing Methods With Users Utility Figure 6 shows the relationship between the standardized total users utility and parameter k for Demand Pattern 1. Comparing fixed pricing with the two proposed methods, we can increase the sum of a users utility by using proposed methods. This is because the proposed methods can balance traffic load by setting prices adaptively while fixed pricing cannot. The proposed methods are more effective when the value of k is larger. This result suggests that when each user s utility is greatly influenced by the waiting time the proposed methods become more effective. Comparing the time functional and waiting-time dependent pricing systems, the values of the sum of users utility are almost the same. This result suggest that spot pricing performs as well as dynamic pricing when we can predict the demand pattern accurately. 4.3 Robustness Against Variations in Demand Pattern To analyze the robustness of the pricing methods againt deterioration of demand prediction, Demand Pattern 1 is used for the first 15 days and Demand Pattern 2 is used for the latter 15 days. However prices are optimized for Demand Pattern 1 in this simulation. Figure 7 shows the relationship between the standardized total users utility and parameter k. Comparing fixed pricing with the two proposed methods, we can increase the sum of a users utility using proposed methods similar to the results shown in 4.2. Waiting-time dependent pricing performs better than time functional pricing. This result suggests that dynamic pricing performs better than spot pricing when the accuracy of the demand prediction is lower. Standardized total utility Standardized total utility for Demand Pat- Figure 7 tern 2. Fixed Priceing Time Functional Pricing Waiting-Time Dependent Pricing Parameter k 5. Conclusion We focused on market-based content delivery systems and proposed two adaptive pricing methods: a time functional pricing method and a waiting-time dependent pricing method. First, we compared these two methods with fixed pricing from the view point of the user s utility. Using the proposed systems each user selects one of the service classes in accordance with his own valuation of the content and traffic load can be balanced in an efficient manner. On the other hand, since the prices are fixed in fixed pricing, the traffic load cannot be balanced efficiently. Therefore, the
6 sum of a users utility increases by the proposed methods. Second, we compared time functional pricing with waiting-time dependent pricing for when the users demand can be predicted correctly. The values of the sum of a users utility were same for time functional pricing and waiting-time dependent pricing. This result shows that same effect occurs for spot and dynamic pricing when traffic is predicted accurately. Third, we examined what happens when the accuracy of prediction deteriorates. In this case waiting-time dependent pricing performs better than time functional pricing, as the sum of a users utility of the time function pricing depends on the accuracy of the prediction. From the simulation results when the price is set statically, exact predictions are required to achieve an efficient transmission. Moreover, when the users demand is unpredictable, efficient transmission can be achieved by dynamic pricing by setting prices according to the state of the queue length in the buffer. The easy model was used in this examination to simplify the calculation. It is left for further study to use more realistic models. We have to take into account background traffic, however. It is difficult to keep the service level agreements completely because we would have to predict background traffic exactly. The assumptions of users behaviors in corresponding to the prices should be verified statistically. and Y. Tanaka, Waiting time versus utility to download images, 2001 Asia Pacific Symposium on Information and Telecommunication Technologies (APSITT2001), pp , November [6] R. Gibbons, Game theory for applied economists (in Japanese), Sobunsha, July References [1] J. K. MacKie-Mason and H. R. Varian, Pricing the Internet, Public Access to the Internet, The MIT Press, [2] N. Kamiyama, An efficient transmission protocol for multicast video-on-demand system (in Japanese), IEICE Technical Report, SSE , IN , March [3] Haar P.G. de, et al., DIAMOND Project: Video-on-demand system, and trials, Eur. Trans. Telecommun., vol. 8, no. 4, pp , [4] Bell Atlantic, Fact sheet: Results of Bell Atlantic video services video-on-demand market trial, Trial Results, [5] K. Nomura, K. Yamori, E. Takahashi, T. Miyoshi,
Waiting Time versus Utility to Download Images
Waiting Time versus Utility to Download Images Kazutomo NOMURA, Kyoko YAMORI, Eiji TAKAHASHI, Takumi MIYOSHI, and Yoshiaki TANAKA Graduate School of Science and Engineering, Waseda University Global Information
More informationState-Dependent Pricing and Its Economic Implications 1
Telecommunication Systems Journal, Vol. 18, No. 4, pp. 315-29, Dec. 2001 State-Dependent Pricing and Its Economic Implications 1 Qiong Wang 2 and Jon Peha 3 Abstract: In a packet-switched integrated-services
More informationAn Adaptive Pricing Scheme for Content Delivery Systems
An Adaptive Pricing Scheme for Content Delivery Systems Srinivasan Jagannathan & Kevin C. Almeroth Department of Computer Science University of California Santa Barbara, CA 936-5 fjsrini,almerothg@cs.ucsb.edu
More informationAn Adaptive Pricing Scheme for Content Delivery Systems
An Adaptive Pricing Scheme for Content Delivery Systems Srinivasan Jagannathan & Kevin C. Almeroth Department of Computer Science University of California Santa Barbara, CA 936-5 jsrini,almeroth @cs.ucsb.edu
More informationUsage-sensitive Pricing in Multi-service Networks
Usage-sensitive Pricing in Multi-service Networks Yuhong Liu and David W. Petr Aug.2, 2000 1 Contents Pricing schemes for multi-service networks Influence of Pricing on PVC vs. SVC Service Preference Service
More informationA Modeling Tool to Minimize the Expected Waiting Time of Call Center s Customers with Optimized Utilization of Resources
A Modeling Tool to Minimize the Expected Waiting Time of Call Center s Customers with Optimized Utilization of Resources Mohsin Iftikhar Computer Science Department College of Computer and Information
More informationIncreasing Wireless Revenue with Service Differentiation
Increasing Wireless Revenue with Service Differentiation SIAMAK AYANI and JEAN WALRAND Department of Electrical Engineering and Computer Sciences University of California at Berkeley, Berkeley, CA 94720,
More informationTraffic Shaping (Part 2)
Lab 2b Traffic Shaping (Part 2) Purpose of this lab: This lab uses the leaky bucket implementation (from Lab 2a) for experiments with traffic shaping. The traffic for testing the leaky bucket will be the
More informationAn Optimal Service Ordering for a World Wide Web Server
An Optimal Service Ordering for a World Wide Web Server Amy Csizmar Dalal Hewlett-Packard Laboratories amy dalal@hpcom Scott Jordan University of California at Irvine sjordan@uciedu Abstract We consider
More informationOptimal Design Methodology for an AGV Transportation System by Using the Queuing Network Theory
Optimal Design Methodology for an AGV Transportation System by Using the Queuing Network Theory Satoshi Hoshino 1, Jun Ota 1, Akiko Shinozaki 2, and Hideki Hashimoto 2 1 Dept. of Precision Engineering,
More informationSimple Competitive Internet Pricing
Simple Competitive Internet Pricing Jose Costa-Requena Networking Laboratory Otakaari 5, 02150 ESPOO, Helsinki FINLAND jose@netlab.hut.fi Abstract The Internet is free of charge by nature but since it
More informationDeveloping DSO s Tariff Structure
Developing DSO s Tariff Structure WHO IS GEODE AND WHAT DOES IT DO FOR ITS MEMBERS? GEODE Position Paper Brussels, 6 November 2013 Kenneth Hänninen GENERAL PRINCIPLES FOR THE FUTURE DEVELOPMENT OF DSO
More informationLab: Response Time Analysis using FpsCalc Course: Real-Time Systems Period: Autumn 2015
Lab: Response Time Analysis using FpsCalc Course: Real-Time Systems Period: Autumn 2015 Lab Assistant name: Jakaria Abdullah email: jakaria.abdullah@it.uu.se room: 1235 Introduction The purpose of this
More informationPricing with Bandwidth Guarantees for Clients with multi-isp Connections
Pricing with Bandwidth Guarantees for Clients with multi-isp Connections Rohit Tripathi and Gautam Barua Department of Computer Science and Engineering Indian Institute of Technology, Guwahati Guwahati-78039,
More informationMonopoly without a Monopolist: Economics of the Bitcoin Payment System. Gur Huberman, Jacob D. Leshno, Ciamac Moallemi Columbia Business School
Monopoly without a Monopolist: Economics of the Bitcoin Payment System Gur Huberman, Jacob D. Leshno, Ciamac Moallemi Columbia Business School Cryptocurrencies Electronic payment systems Bitcoin being
More informationThe Price of Anarchy in an Exponential Multi-Server
The Price of Anarchy in an Exponential Multi-Server Moshe Haviv Tim Roughgarden Abstract We consider a single multi-server memoryless service station. Servers have heterogeneous service rates. Arrivals
More informationC. Wohlin, P. Runeson and A. Wesslén, "Software Reliability Estimations through Usage Analysis of Software Specifications and Designs", International
C. Wohlin, P. Runeson and A. Wesslén, "Software Reliability Estimations through Usage Analysis of Software Specifications and Designs", International Journal of Reliability, Quality and Safety Engineering,
More informationPricing for Market Segmentation in Data Networks
Association for Information Systems AIS Electronic Library (AISeL) AMCIS 1996 Proceedings Americas Conference on Information Systems (AMCIS) 8-16-1996 Pricing for Market Segmentation in Data Networks Philipp
More informationModeling of competition in revenue management Petr Fiala 1
Modeling of competition in revenue management Petr Fiala 1 Abstract. Revenue management (RM) is the art and science of predicting consumer behavior and optimizing price and product availability to maximize
More informationUC Irvine UC Irvine Previously Published Works
UC Irvine UC Irvine Previously Published Works Title ISP Service Tier Design Permalink https://escholarshiporg/uc/item/9n71s7bj Journal IEEE/ACM Transactions on Networking, 24(3) ISSN 1063-6692 1558-2566
More informationNoise Figure Analyzers
The new standard for today s fast-paced measurement environments Agilent Noise Figure Analyzers NFA Series The new standard for noise figure measurement If you design or manufacture subsystems or components
More informationExamining and Modeling Customer Service Centers with Impatient Customers
Examining and Modeling Customer Service Centers with Impatient Customers Jonathan Lee A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF BACHELOR OF APPLIED SCIENCE DEPARTMENT
More informationIntroduction - Simulation. Simulation of industrial processes and logistical systems - MION40
Introduction - Simulation Simulation of industrial processes and logistical systems - MION40 1 What is a model? A model is an external and explicit representation of part of reality as seen by the people
More informationAnalysis of Price Competition under Peering and Transit Agreements in Internet Service Provision to Peer-to-Peer Users
Analysis of Price Competition under Peering and Transit Agreements in Internet Service Provision to Peer-to-Peer Users Luis Guijarro, Vicent Pla, Jose R. Vidal, and Jorge Martinez-Bauset Universidad Politécnica
More informationAEM 4160: STRATEGIC PRICING CORNELL UNIVERSITY PROFESSOR JURA LIAUKONYTE
EXAM 1 NAME: AEM 4160: STRATEGIC PRICING CORNELL UNIVERSITY PROFESSOR JURA LIAUKONYTE FEBRUARY 26, 2015 100 points = 100% 103 points = 103%! Show all work. Write legibly. Calculators permitted. No computers.
More informationNumerical investigation of tradeoffs in production-inventory control policies with advance demand information
Numerical investigation of tradeoffs in production-inventory control policies with advance demand information George Liberopoulos and telios oukoumialos University of Thessaly, Department of Mechanical
More informationMidterm for CpE/EE/PEP 345 Modeling and Simulation Stevens Institute of Technology Fall 2003
Midterm for CpE/EE/PEP 345 Modeling and Simulation Stevens Institute of Technology Fall 003 The midterm is open book/open notes. Total value is 100 points (30% of course grade). All questions are equally
More informationAsymptotic Analysis of Real-Time Queues
Asymptotic Analysis of John Lehoczky Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213 Co-authors: Steve Shreve, Kavita Ramanan, Lukasz Kruk, Bogdan Doytchinov, Calvin Yeung, and
More informationEconomics 335 Price Discrimination Answer Key. (a) (page 1) You have to show with the aid of graphs that the monopolist s total demand is given by
conomics 335 Price iscrimination Answer Key # (a) (page ) You have to show with the aid of graphs that the monopolist s total demand is given by 0 if P 00 Q= Q+ Q = 00 P if 00 P 00 50.5P if 0 P 00 The
More informationEcon 121b: Intermediate Microeconomics
Econ 11b: Intermediate Microeconomics Dirk Bergemann, Spring 01 Week of 3/6-4/3 1 Lecture 16: Imperfectly Competitive Market 1.1 Price Discrimination In the previous section we saw that the monopolist
More informationBERTHING PROBLEM OF SHIPS IN CHITTAGONG PORT AND PROPOSAL FOR ITS SOLUTION
66 BERTHING PROBLEM OF SHIPS IN CHITTAGONG PORT AND PROPOSAL FOR ITS SOLUTION A. K. M. Solayman Hoque Additional Chief Engineer, Chittagong Dry Dock Limited, Patenga, Chittagong, Bnagladesh S. K. Biswas
More informationImplementing a Pricing Mechanism for Public Logistics Networks
Industrial Engineering Research Conference, Atlanta, GA, May 14 18, 2005 Implementing a Pricing Mechanism for Public Logistics Networks Michael G. Kay and Ashish Jain Department of Industrial Engineering
More informationTextbook: pp Chapter 12: Waiting Lines and Queuing Theory Models
1 Textbook: pp. 445-478 Chapter 12: Waiting Lines and Queuing Theory Models 2 Learning Objectives (1 of 2) After completing this chapter, students will be able to: Describe the trade-off curves for cost-of-waiting
More informationCOMPUTATIONAL ANALYSIS OF A MULTI-SERVER BULK ARRIVAL WITH TWO MODES SERVER BREAKDOWN
Mathematical and Computational Applications, Vol. 1, No. 2, pp. 249-259, 25. Association for cientific Research COMPUTATIONAL ANALYI OF A MULTI-ERVER BULK ARRIVAL ITH TO MODE ERVER BREAKDON A. M. ultan,
More informationLoad tariffs in the Nordic countries
Load tariffs in the Nordic countries WHO IS GEODE AND WHAT DOES IT DO FOR ITS MEMBERS? Developing DSO s tariff structure Stockholm, 5th November 2015 Kenneth Hänninen SUMMARY Current energy based DSOs
More informationUsing Utility Information to Calibrate Customer Demand Management Behavior Models
IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 16, NO. 2, MAY 2001 317 Using Utility Inmation to Calibrate Customer Demand Management Behavior Models Murat Fahriog lu, Student Member, IEEE and Fernando L. Alvarado,
More informationProblem Set #2 Suggested Solutions
Economics 155 Stanford University Spring Quarter 2007 Problem Set #2 Suggested Solutions 1. An externality occurs when an agent s action directly affects the consumption or production of another agent,
More informationstatus of processors. A Job Scheduler dispatches a job to the requested number of processors using a certain scheduling algorithm
Eect of Job Size Characteristics on Job Scheduling Performance Kento Aida Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology 4259, Nagatsuta, Midori-ku, Yokohama-shi
More informationINTRODUCTION AND CLASSIFICATION OF QUEUES 16.1 Introduction
INTRODUCTION AND CLASSIFICATION OF QUEUES 16.1 Introduction The study of waiting lines, called queuing theory is one of the oldest and most widely used Operations Research techniques. Waiting lines are
More informationEquilibrium customers choice between FCFS and Random servers
Equilibrium customers choice between FCFS and Random servers Refael Hassin July 22, 2009 Dedicated to the memory of my mother Zipora (Fella) Hassin (1924-2009) Abstract Consider two servers of equal service
More informationStatic (or Simultaneous- Move) Games of Complete Information
Static (or Simultaneous- Move) Games of Complete Information Dominated Strategies Nash Equilibrium F.Valognes - Game Theory - Chp 2 1 Outline of Static Games of Complete Information Introduction to games
More informationFrequently Asked Questions: Lyndon Township Broadband Initiative
Frequently Asked Questions: Lyndon Township Broadband Initiative Q. What is the Lyndon Township Broadband Initiative? A. Citizens of Lyndon Township have asked the Lyndon Township board to address the
More informationBhalchandra Agashe and Vineet Gorhe Department of Computer Sciences The University of Texas at Austin {bsagashe,
GARFIELD: A Robust Supply Chain Management Agent Bhalchandra Agashe and Vineet Gorhe Department of Computer Sciences The University of Texas at Austin {bsagashe, vmgorhe}@cs.utexas.edu 7 December 2006
More informationA Comparison of Optimization of Charging Scheme in Multiple QoS Networks. Baru Nilai, Negeri Sembilan Darul Khusus Malaysia
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 A Comparison of Optimization of Charging Scheme in Multiple QoS Networks Fitri Maya Puspita 1, and Kamaruzzaman Seman 1*, Bachok M. Taib 1 1 Faculty of Science and
More informationChapter 9: Static Games and Cournot Competition
Chapter 9: Static Games and Cournot Competition Learning Objectives: Students should learn to:. The student will understand the ideas of strategic interdependence and reasoning strategically and be able
More information14.01 Principles of Microeconomics, Fall 2007 Chia-Hui Chen November 7, Lecture 22
Monopoly. Principles of Microeconomics, Fall Chia-Hui Chen November, Lecture Monopoly Outline. Chap : Monopoly. Chap : Shift in Demand and Effect of Tax Monopoly The monopolist is the single supply-side
More informationORDER MANAGEMENT: UNDERSTANDING THE ELEMENTS OF END-TO-END SERVICE FULFILLMENT
ORDER MANAGEMENT: UNDERSTANDING THE ELEMENTS OF END-TO-END SERVICE FULFILLMENT An Incognito Software Systems White Paper Abstract In the modern era of telecommunication services, automation of B/OSS processes
More informationQuasi linear Utility and Two Market Monopoly
Quasi linear Utility and Two Market Monopoly By Stephen K. Layson Department of Economics 457 Bryan Building, UNCG Greensboro, NC 27412 5001 USA (336) 334 4868 Fax (336) 334 5580 layson@uncg.edu ABSTRACT
More informationInformation Effects on Performance of Two-tier Service Systems with Strategic Customers. Zhe George Zhang
Information Effects on Performance of Two-tier Service Systems with Strategic Customers Zhe George Zhang Department of Decision Sciences, Western Washington University, Bellingham, WA98225, USA & Beedie
More informationVARIABILITY PROFESSOR DAVID GILLEN (UNIVERSITY OF BRITISH COLUMBIA) & PROFESSOR BENNY MANTIN (UNIVERSITY OF WATERLOO)
VARIABILITY PROFESSOR DAVID GILLEN (UNIVERSITY OF BRITISH COLUMBIA) & PROFESSOR BENNY MANTIN (UNIVERSITY OF WATERLOO) Istanbul Technical University Air Transportation Management M.Sc. Program Logistic
More informationPh.D. Defense: Resource Allocation Optimization in the Smart Grid and High-performance Computing Tim Hansen
Ph.D. Defense: Resource Allocation Optimization in the Smart Grid and High-performance Computing Tim Hansen Department of Electrical and Computer Engineering Colorado State University Fort Collins, Colorado,
More informationCall Accounting MITEL
MITEL Call Accounting To optimize your business potential, you need to know if your telecommunication costs are excessive and if so, why? Are your employees making unauthorized telephone calls? Are you
More informationAgilent 86100A Infiniium DCA Time Domain Reflectometry Product Overview
Agilent 86100A Infiniium DCA Time Domain Reflectometry Product Overview Infiniium DCA Agilent 86100A Wide-Bandwidth Oscilloscope with Digital Communications Analysis and Time Domain Reflectometry 2 The
More informationA Dynamic Scheduling Problem in Cost Estimation Process of EPC Projects
A Dynamic Scheduling Problem in Cost Estimation Process of EPC Projects Nobuaki Ishii, Yuichi Takano and Masaaki Muraki Faculty of Engineering, Kanagawa University, -7- Rokkakubashi, -8686, Kanagawa-ku,
More informationA Quantitative Comparison of Bottleneck Detection Methods in Manufacturing Systems with Particular Consideration for Shifting Bottlenecks
A Quantitative Comparison of Bottleneck Detection Methods in Manufacturing Systems with Particular Consideration for Shifting Bottlenecks Christoph Roser 1, Masaru Nakano 2 1 Karlsruhe University of Applied
More informationLecture 6: Offered Load Analysis. IEOR 4615: Service Engineering Professor Whitt February 10, 2015
Lecture 6: Offered Load Analysis IEOR 4615: Service Engineering Professor Whitt February 10, 2015 What is the Problem? What capacity is needed in a service system? In order to meet uncertain exogenous
More informationKey words: Franchise Fees, Competition, Double Marginalization, Collusion
The Role of Franchise Fees and Manufacturers Collusion DongJoon Lee (Nagoya University of Commerce and Business, Japan) Sanghoen Han (Nagoya University of Commerce and Business, Japan) Abstract: This paper
More informationPaying Networks for Marks. Do Greedy Autonomous Systems Make for a Sensible Internet? Sale!!! 10 /Gb. Bandwidth brokers
X Do Greedy Autonomous Systems Make for a Sensible Internet? Bruce Hajek The Internet is a federation of thousands of autonomous systems. Understanding and modeling the interaction of the autonomous systems
More informationDON T FORGET ABOUT MEASUREMENT. Written by: Miko Kershberg, WSI Digital Marketing Expert
Don t Forget About Measurement // 1 2 12.ch DON T FORGET ABOUT MEASUREMENT Written by: Miko Kershberg, WSI Digital Marketing Expert Don t Forget About Measurement // 2 Table of Contents Introduction...
More informationCHAPTER 5 SOCIAL WELFARE MAXIMIZATION FOR HYBRID MARKET
61 CHAPTER 5 SOCIAL WELFARE MAXIMIZATION FOR HYBRID MARKET 5.1 INTRODUCTION Electricity markets throughout the world continue to be opened to competitive forces. The underlying objective of introducing
More informationDesigned with the Programmer in Mind
Agilent Technologies 8960 Series 10 Wireless Communications Test Set Product Note Designed with the Programmer in Mind Agilent Technologies 8960 Series 10 Designed with the programmer in mind T he Agilent
More informationThe Math of Contact Center Staffing
3/14/16 2016 Annual Conference The Math of Contact Center ing Session Overview In this session, you will learn to: Outline the implications of overstaffing and understaffing. Identify the unique characteristics
More informationTicker: Dutch Auctions With A Money-Back Guarantee Sandeep Baliga and Jeff Ely
Ticker: Dutch Auctions With A Money-Back Guarantee Sandeep Baliga and Jeff Ely The Problem A venue like a stadium, a theatre, or an orchestra has a fixed capacity of seats in different locations. All venues
More informationAgilent 8490G Coaxial Attenuators
Agilent 849G Coaxial Attenuators Technical Overview Key specifications Maximize your operating frequency range for DC to 67 GHz application Minimize your measurement uncertainty with low SWR of 1.45 up
More informationA Queuing Approach for Energy Supply in Manufacturing Facilities
A Queuing Approach for Energy Supply in Manufacturing Facilities Lucio Zavanella, Ivan Ferretti, Simone Zanoni, and Laura Bettoni Department of Mechanical and Industrial Engineering Università degli Studi
More informationSponsored Search Markets
COMP323 Introduction to Computational Game Theory Sponsored Search Markets Paul G. Spirakis Department of Computer Science University of Liverpool Paul G. Spirakis (U. Liverpool) Sponsored Search Markets
More informationMotivating Examples of the Power of Analytical Modeling
Chapter 1 Motivating Examples of the Power of Analytical Modeling 1.1 What is Queueing Theory? Queueing theory is the theory behind what happens when you have lots of jobs, scarce resources, and subsequently
More informationTelecom expense management platform CAAB Enterprise
Telecom expense management platform CAAB Enterprise NEC Australia au.nec.com Introduction CAAB Enterprise is a market leading Telecom Expense Management platform that enables organisations to manage and
More informationVerify if a device can stand all kinds of protocol variations
The Agilent Technologies E2920 PCI/PCI-XSeries Application Note 2 Verify if a device can stand all kinds of protocol variations Validating servers and workstations that contain various I/O systems, various
More informationQueuing Theory 1.1 Introduction
Queuing Theory 1.1 Introduction A common situation occurring in everyday life is that of queuing or waiting in a line. Queues (waiting lines) are usually seen at bus stop, ticket booths, doctor s clinics,
More informationCarbon Footprint Optimization - Game Theoretic Problems and Solutions
Carbon Footprint Optimization - Game Theoretic Problems Solutions DEEPAK BAGCHI SHANTANU BISWAS Y. NARAHARI P. SURESH L. UDAYA LAKSHMI N. VISWANADHAM S. V. SUBRAHMANYA We introduce the carbon emission
More informationAgilent N4420B S-Parameter Test Set
Agilent N4420B S-Parameter Test Set Technical Overview Expand your 2-port PNA Series network analyzer to a complete 4-port solution Compatible with Agilent PNA & PNA-L network analyzers Solid-state switches
More informationThe Robustness Of Non-Decreasing Dynamic Pricing Laura van der Bijl Research Paper Business analytics Aug-Oct 2017 Prof.
The Robustness Of Non-Decreasing Dynamic Pricing Laura van der Bijl Research Paper Business Analytics Aug-Oct 2017 The Robustness Of Non-Decreasing Dynamic Pricing Laura van der Bijl Laura.vanderbijl5@gmail.com
More informationPERFORMANCE EVALUATION OF DEPENDENT TWO-STAGE SERVICES
PERFORMANCE EVALUATION OF DEPENDENT TWO-STAGE SERVICES Werner Sandmann Department of Information Systems and Applied Computer Science University of Bamberg Feldkirchenstr. 21 D-96045, Bamberg, Germany
More informationIEOR 130 Methods of Manufacturing Improvement Practice Examination Problems Part II of Course Prof. Leachman Fall, 2017
IEOR 130 Methods of Manufacturing Improvement Practice Examination Problems Part II of Course Prof. Leachman Fall, 2017 1. For a particular semiconductor product, the customer orders received to date are
More informationSpecial Issue: Intelligent Transportation Systems
Journal of Advanced Transportation, Vol. 36 No. 3, pp. 225-229 www. advanced-transport. corn EDITORIAL Special Issue: Intelligent Transportation Systems Guest Editor: William H.K. Lam Recent rapid development
More informationSystem. Figure 1: An Abstract System. If we observed such an abstract system we might measure the following quantities:
2 Operational Laws 2.1 Introduction Operational laws are simple equations which may be used as an abstract representation or model of the average behaviour of almost any system. One of the advantages of
More informationREAL-TIME PRICING SYSTEM FOR DEMAND-SIDE MANAGEMENT IN FRIENDS
REAL-TIME PRICING SYSTEM FOR DEMAND-SIDE MANAGEMENT IN FRIENDS *Shigeki Yoshikawa, *Hiroyuki Kita, *Eiichi Tanaka, **Jun Hasegawa *Hokkaido University **Hakodate National College of Technology *Sapppro,
More informationREGIONAL RESTRICTION, STRATEGIC DELEGATION, AND WELFARE
Discussion Paper No. 761 REGIONAL RESTRICTION, STRATEGIC DELEGATION, AND WELFARE Toshihiro Matsumura Noriaki Matsushima November 2009 The Institute of Social and Economic Research Osaka University 6-1
More informationLoading Control of Complex Conveyor System
Loading Control of Complex Conveyor System PEEP MIIDLA, JENS HAUG Institute of Mathematics, VKG AS University of Tartu Liivi 2, 50409 Tartu ESTONIA peep.miidla@ut.ee Abstract: - Problem of conveyor system
More informationMass Customized Large Scale Production System with Learning Curve Consideration
Mass Customized Large Scale Production System with Learning Curve Consideration KuoWei Chen and Richard Lee Storch Industrial & Systems Engineering, University of Washington, Seattle, U.S.A {kwc206,rlstorch}@uw.edu
More informationRobust Supply Function Bidding in Electricity Markets With Renewables
Robust Supply Function Bidding in Electricity Markets With Renewables Yuanzhang Xiao Department of EECS Email: xyz.xiao@gmail.com Chaithanya Bandi Kellogg School of Management Email: c-bandi@kellogg.northwestern.edu
More information1 Incentives and Pricing in Communications Networks
1 Incentives and Pricing in Communications Networks Asuman Ozdaglar and R. Srikant This chapter studies the problem of decentralized resource allocation among competing users in communication networks.
More informationOnline shopping and platform design with ex ante registration requirements
Online shopping and platform design with ex ante registration requirements O A Florian Morath Johannes Münster June 17, 2016 This supplementary appendix to the article Online shopping and platform design
More informationPreface. Skill-based routing in multi-skill call centers
Nancy Marengo nmarengo@fewvunl BMI-paper Vrije Universiteit Faculty of Sciences Business Mathematics and Informatics 1081 HV Amsterdam The Netherlands November 2004 Supervisor: Sandjai Bhulai Sbhulai@fewvunl
More informationDuopoly Competition Considering Waiting Cost
Duopoly Competition -..-- Considering - Waiting Cost Duopoly Competition Considering Waiting Cost Nam, Ick-Hyun Seoul National University College of Business Administration March 21, 1997 1. Introduction
More informationIS THE GOLD /PRAY SIMULATION DEMAND MODEL VALID AND IS IT REALLY ROBUST?
IS THE GOLD /PRAY SIMULATION DEMAND MODEL VALID AND IS IT REALLY ROBUST? Kenneth R. Goosen, University of Arkansas at Little Rock krgoosen@cei.net ABSTRACT The purpose of this paper is to evaluate the
More informationWIN-WIN DYNAMIC CONGESTION PRICING FOR CONGESTED URBAN AREAS
1 WIN-WIN DYNAMIC CONGESTION PRICING FOR CONGESTED URBAN AREAS Aya Aboudina, Ph.D. Student University of Toronto Baher Abdulhai, Ph.D., P.Eng. University of Toronto June 12 th, 2012 ITS Canada - ACGM 2012
More informationMS&E 246: Game Theory with Engineering Applications. Lecture 1 Ramesh Johari
MS&E 246: Game Theory with Engineering Applications Lecture 1 Ramesh Johari Outline Administrative stuff Course introduction A game Administrative details My e-mail: ramesh.johari@stanford.edu Course assistant:
More informationPindyck and Rubinfeld, Chapter 13 Sections 13.1, 13.2, 13.3 and 13.6 continued
Pindyck and Rubinfeld, Chapter 13 Sections 13.1, 13.2, 13.3 and 13.6 continued In deciding whether a threat is credible or not, reputation can play a role. For example, in the product choice game, if Far
More informationBEFORE THE CANADIAN RADIO-TELEVISION AND TELECOMMUNICATIONS COMMISSION
BEFORE THE CANADIAN RADIO-TELEVISION AND TELECOMMUNICATIONS COMMISSION TELECOM PUBLIC NOTICE CRTC 2011-77, Review of billing practices for wholesale residential highspeed access services Opening Statement
More informationOn Optimal Tiered Structures for Network Service Bundles
On Tiered Structures for Network Service Bundles Qian Lv, George N. Rouskas Department of Computer Science, North Carolina State University, Raleigh, NC 7695-86, USA Abstract Network operators offer a
More informationClock-Driven Scheduling
NOTATIONS AND ASSUMPTIONS: UNIT-2 Clock-Driven Scheduling The clock-driven approach to scheduling is applicable only when the system is by and large deterministic, except for a few aperiodic and sporadic
More informationMathematical Modeling and Analysis of Finite Queueing System with Unreliable Single Server
IOSR Journal of Mathematics (IOSR-JM) e-issn: 2278-5728, p-issn: 2319-765X. Volume 12, Issue 3 Ver. VII (May. - Jun. 2016), PP 08-14 www.iosrjournals.org Mathematical Modeling and Analysis of Finite Queueing
More informationIntroduction to Computer Simulation
Introduction to Computer Simulation EGR 260 R. Van Til Industrial & Systems Engineering Dept. Copyright 2013. Robert P. Van Til. All rights reserved. 1 What s It All About? Computer Simulation involves
More informationA Simple EOQ-like Solution to an Inventory System with Compound Poisson and Deterministic Demand
A Simple EOQ-like Solution to an Inventory System with Compound Poisson and Deterministic Demand Katy S. Azoury San Francisco State University, San Francisco, California, USA Julia Miyaoka* San Francisco
More informationModelling buyer behaviour - 2 Rate-frequency models
Publishing Date: May 1993. 1993. All rights reserved. Copyright rests with the author. No part of this article may be reproduced without written permission from the author. Modelling buyer behaviour -
More informationSpring 06 Assignment 4: Game Theory and Auctions
15-381 Spring 06 Assignment 4: Game Theory and Auctions Questions to Vaibhav Mehta(vaibhav@cs.cmu.edu) Out: 3/08/06 Due: 3/30/06 Name: Andrew ID: Please turn in your answers on this assignment (extra copies
More informationThe Staffing Problem of the N-Design Multi-Skill Call Center Based on Queuing Model
3rd International Conference on Wireless Communication and Sensor etwork (WCS 06) The Staffing Problem of the -Design Multi-Skill Call Center Based on Queuing Model Chun-Yan Li, De-Quan Yue Zhijiang College
More informationService Proveider s Optimal Pricing for PVC and SVC Service
The University of Kansas Technical Report Service Proveider s Optimal Pricing for PVC and SVC Service Yuhong Liu and David W. Petr ITTC-FY2000-TR-18836-02 December 1999 Project Sponsor: Sprint Corporation
More information