Utilizing Call Admission Control for Pricing Optimization of Multiple Service Classes in Wireless Cellular Networks Authors : Okan Yilmaz, Ing-Ray Chen Presentator : Mehmet Saglam Department of Computer Science Virginia Polytechnic Institute and State University Northern Virginia Center, USA

Outline  Introduction  System Model  Methodology  Admission Control Algorithms  Numerical Analysis  Summary 2/27

Introduction  Diverse Multi-Media Services Real Time Services 3/27 Non-Real Time Services

Introduction  REVENUE OPTIMIZATION  QoS requirements 4/27  Charge clients by the amount of time  Change the price periodically  Total number of channels

Introduction  Related Work  Call admission control for single-class network traffic  Call admission control for multiple classes  Concept of maximizing the payoff of the system through admission control  Admission control algorithms integrated w/ QoS guarantees  Partitioning-based  Threshold-based  Hybrid 5/27  Aims to satisfy QoS requirements  This paper address the issue of determining OPTIMAL PRICING

Introduction 6/27  The Goal of this paper; Utilize admission control algorithms for revenue optimization with QoS guarantees to derive optimal pricing Show that a hybrid admission control algorithm combining the benefits of partitioning and threshold-based call admission control would perform the best in terms of pricing optimization

System Model 7/27  Cellular Network Consist of a number of cells, each of which has a base station at the center Fixed number of channels,  Service Classes Characterized by service types(Real Time, Non-Real Time)  Call Types Handoff calls New calls

System Model 8/27  Quality of Service Requirements Each service type requires a number of BW channel  Arrival/Departure Rates  Each cell makes admission control decissions for new and handoff call requests to maximize revenue  Optimal pricing related to pricing algorithm charge-by-time charge-rate is per time unit

Methodology 9/27  Pricing-Demand Function  Constants Elasticity: Effect of pricing changes on service demand Elastic: Increase in demand faster than decrease in pricing Inelastic: Increase in demand is slower than decrease in pricing Determined by analyzing statistical data Proportionality constant Calculated from pricing-demand functio n

Methodology 10/27  Total Revenue Function  Obtain max revenue by using  The approach is to exhaustively search all possible combinations of for all service classes and look for the best combination of service class prices that would maximize the system revenue.

Methodology 11/27  Pricing Range : Divide into parts  Total number of possible price combination for all service classes:

Methodology 12/27  Predict the arrival rates of service classes for a given price combinations  Determine the revenue generated under a call admission control algorithm and store all the revenue values in an n-dimensional table, by every cell independently  Collect the tables and merge them to determine global optimal pricing

Admission Control Algorithms 13/27  Overview of partitioning, threshold-based and hybrid algorithms Integrated with pricing for revenue optimization Quality of Service guarantees  Assume that there are 2 service types Class 1 / high priority / real time Class 2 / low priority / non-real time  Traffic input parameters

Admission Control Algorithms 14/27  Divides total number of channel into fixed partitions for reserving a particular service class and call type Partitioning Admission Control 1/2  Identify the best partition that would maximize the cell’s revenue while satisfying the imposed QoS constraints defined by

Admission Control Algorithms 15/27 Partitioning Admission Control 2/2  The system behaves as M/M/n/n queue  Call dropping and blocking probabilities can be determined easily by calculating the probability of the partition allocated to serve the specific calls being full  Compute the revenue per unit time to the cell by where  Optimal partition that max the revenue can be find by exhaustively searching all possibilities

Admission Control Algorithms 16/27 Threshold-Based Admission Control 1/2  When the number of channels used in the cell exceeds threshold, then new or handoff calls from service class 2 (low-priority) will not be admitted  Aims to find an optimal set of satisfying the above conditions that would yeld the highest revenue with QoS guarantees  This algorithm can be analyzed by using a SPN model to compute

Admission Control Algorithms 17/27 Threshold-Based Admission Control 2/2  The revenue generated per unit time could calculated by  The optimal hreshold set can be computed by searching through all the combinations  There is no close-form solution  It requires evaluating the SPN performance model to generate the blocking probabilities and the revenue obtainable by the system

Admission Control Algorithms 18/27 Hybrid Partitioning-Threshold Admission Control 1/2  Takes the advantege of both partitioning and threshold-based  Divides channels into fixed partitions  Shares a partition to allow calls of all service classes/types to compete for its usage  Let be the numbers of calls by service and class types and the number of channels allocated to the shared partition 

Admission Control Algorithms 19/27 Hybrid Partitioning-Threshold Admission Control 2/2  The performance model for the hybrid algorithm is composed of 2 sub-models  Partitioning algorithm with 4 fixed partitions (M/M/n/n)  Threshold-based algorithm  Compute the revenue per unit time by sum of revenue earned from fixed partitions plus from shared partition  This takes minutes to search for the best solution for C=80 channels  There is no close-form solution

Numerical Analysis 20/27  The paper used numerical data for possible future price combinations  Compared performance characteristics of these admission control algorithms with QoS guarantees  Class 1 (real-time) has more stringent call blocking probabilities than class 2 (non-real-time), as well as higher pricing  The call arrival process is poisson thus, inter-arrival time of calls is exponential (SPN model used for performance evaluation)

Numerical Analysis 21/27  The revenue obtainable increases as the anticipated arrival rate increases as a result of lowering the prices Partitioning Admission Control  Max revenue=664  v1=80, v2=10

Numerical Analysis 22/27  By sharing resources among service classes and controlling the effect of higher class 2 arrival rate, threshold algorithm performed better than partitioning algorithm Threshold-based Admission Control  Max revenue=722  v1=80, v2=6

Numerical Analysis 23/27 Hybrid Admission Control  Applies a lower threshold to class 2 calls in the common partition  Max revenue=736  v1=60, v2=8  It reserves

Numerical Analysis 24/27  The multiplexing power of the shared partition is clearly demonstrated  The performance of threshold algorithm is comparable to hybrid algorithm  Superiority of hybrid algorithm is the ability to optimally reserve resources through fixed partitioning and to optimally allocate resources to the shared partition in accordance with threshold-based admission control algorithm

Numerical Analysis 25/27  Each cell would collect statistical data periodically to estimate a set of reference arrival/departure rates of new/handoff calls of various service classes based on statistical analysis  Then each cell determines new/handoff call arrival rates for a range of “future” potential pricing for each service class  The optimal settings for all future price combinations are then summarized in a revenue table and reported to a central entity which collects and analyzes revenue tables

Summary 26/27  A methodology proposed&analyzed to determine optimal pricing for revenue optimization with QoS guarantees in wireless mobile networks  The admission control algorithms are utilized (integrated with pricing)  Partitioning admission control  Threshold-based admission control  Hybrid admission control  Within the 3 algorithms the hybrid scheme performed the best combining the benefits of the others

Questions & Answers Thank You! Mehmet Saglam 27/27