utilizing call admission control for pricing optimization of multiple service classes in wireless...
TRANSCRIPT
Utilizing Call Admission Controlfor Pricing Optimization
of Multiple Service Classesin Wireless Cellular Networks
Authors : Okan Yilmaz, Ing-Ray Chen
Presentator : Mehmet Saglam
Department of Computer ScienceVirginia Polytechnic Institute and State UniversityNorthern 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
FIXED PRICE
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
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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 function
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
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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 ann-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
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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
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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
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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
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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
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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
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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
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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
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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
