1. On QoS Guarantees with Reward Optimization for Servicing Multiple Priority Class in Wireless Networks YaoChing Peng Eunyoung Chang

2. Agenda Introduction Related Works The Proposed Approach  System model  Elastic threshold based Call Admission Control (CAC) algorithms  Heuristic-based search algorithm Performances Analysis  Compares elastic threshold-based algorithm against existing CAC algorithms Conclusion

3. Introduction Call admission control (CAC)  For single-class network traffic  real-time multimedia service such as video and audio and non-real-time services such as text and image. The goal is to service multiple priority classes in wireless networks to maximize the system reward rate with QoS guarantees.  Dropping probability of handoff calls  Blocking probability of new calls. Handoff occurs when a mobile user with an ongoing connection leaves the current cell and enters another cell. An ongoing connection maybe dropped when if there is insufficient bandwidth in the new cell to support to it, Reducing the handoff call drop probability by rejecting new connection requests could result in an increase in the new call blocking probability. :

4. Related Works Some CAC algorithms proposed that partition system resources and allocate distinct partitioned resources to serve distinct service class. Some works focus on the distributed CAC algorithm that runs in each cell and partitions channel resources in the cell into three partitions: one for real-time, one for non-real time, and one for both. Some proposed a bandwidth reservation and reconfiguration mechanism to facilitate handoff processes for multiple service.

5. Related Works(Cont.) All CAC algorithms make acceptance decisions for new and handoff calls to satisfy QoS requirement in order to keep the dropping probability of handoff calls and the blocking probability of new calls lower than pre-specified thresholds. Without considering “value” issues associated with service classes.

6. Elastic Threshold-Based Call Admission Control (E-CAC) New threshold-based CAC algorithm has a goal to maximize the system reward rate with QoS guarantees.  “Reward” is referring to “value” brought to the system due to services.  It could map to “revenue” from the service provider’s perspective. Elastic threshold-based CAC developed is capable of satisfying QoS while generating higher rewards compared with existing CACs.

7. cell System Model A wireless cellular network Base station Mobile user Service typesExampleRewardPriorityClass Real timeVideo, AudioLarge rewardHigh priorityClass 1 Non-real timeText, ImageSmall rewardLow priorityClass 2 we will consider only two service classes in this paper.

8. New and Handoff call Cell Cell 2 System Model A handoff occurs when a mobile user with an ongoing connection leaves the current cell and enters another cell. New call: mobile user send request to current cell

9. Reward HandoffNew Class 1 Class2 The total reward generated by each cell per unit time would be the sum of the rewards generated by various priority classes

10. Blocking Probability Thresholds The QoS constraints are expressed in terms of blocking probability thresholds. Pre-specified thresholds Dropped handoff calls dissatisfy users more than blocked new calls do, so the drop probability requirement of handoff calls is likely to be more stringent than the blocking probability requirement of new calls Handoff dropping probability of class 1 New call blocking probability of class 1

11. Threshold-Based Admission Control Without Elastic Threshold-Based Previous CAC algorithms make acceptance decisions for new and handoff calls to satisfy QoS requirements to keep the dropping probability of handoff calls and the blocking probability of new calls lower than pre-specified thresholds. without considering value issues associated with service classes. maximizing the reward of the system through CAC in the context of multimedia services(High priority).

12. The probabilities of accepting a new call and a handoff call of service class i number of channels required by a service call of service class 1 the total number of channels high threshold of handoff call at class 1 Low threshold of new call at class 2

13. Simulation Accepting rate: P when n:the number of channels that have been allocated in the system

14. Place: hold service calls Transition : models the arrival rate Transition : models the departure rate

15. Guard the number of handoff calls it holds number of channels required by a service call of service class 1 the number of channels that have been allocated in the system

16. The probabilities of accepting SPN

17. The probabilities of accepting SPN

18. The probabilities of accepting SPN

19. Errors in the paper P.4 On the line right below Equation 12, "if Ein and Eih are enabled" should be "if Ein and Eih are disabled". Also right below Equation 14, "if Ein and Eih are disabled" should be "if Ein and Eih are enabled". by Dr. Chen

20. Blocking/Dropping Probabilities: SPN Expected value of a random variable X Handoff dropping probability of class i New call blocking probability of class i

21. Charge-by-time The reward earned per unit time the reward for a call of service class i per unit time.

22. Threshold problem High threshold of handoff call at class 1 Low threshold of new call at class 1 High threshold of new call at class 1 High threshold of handoff call at class 2 High threshold of new call at class 2 Low threshold of handoff call at class 1 Low threshold of new call at class 2 Low threshold of handoff call at class 2 Handoff dropping probability of class 1 Handoff dropping probability of class 2 New call blocking probability of class 1 New call blocking probability of class 2

23. Heuristic-based Search Algorithm To determine the optimal threshold combinations that would maximize the reward earned per unit time with satisfying QoS constraints. The E-CAC algorithm utilizes a greedy search method to determine a legitimate solution which maximizes the reward rate. A delta (∆) value as input : to determine the set of threshold combinations in the range of current threshold ± Δ.  Step 1: Finding a legitimate solution Method1 : finding a legitimate solution which satisfied the QoS constraints of all service call types Method 2: determine the minimum number of channels needed,  Step 2 : determining a locally optimal solution by applying a greedy search starting from the legitimate solution found in the first step

24. Search for Optimal Threshold Values Method 1 : finding a legitimate solution.  Set all thresholds to the total number of channels (namely C), and check if this combination is legitimate.  Reduce the bandwidth used by lowering the low threshold of low-priority classes until find a legitimate solution or start missing QoS constraints of low-priority classes.  Next, start reducing arrivals of high-priority new calls as well by lowering their low threshold.  If none of these approaches generates a legitimate solution, this method returns no legitimate solution.

25. Search for Optimal Threshold Values Method 2 : determine the minimum number of channels needed to satisfy the QoS constraint  This helps eliminate all threshold combinations that do not provide the minimum number of channels in the search.  In subsequent iterations,  If the low threshold ≤ the high threshold, Then increase the low thresholds of low-priority calls  Otherwise, increase the high threshold until the high threshold reaches C or find a legitimate solution.  Similarly, increase the low threshold of high-priority new calls  If cannot satisfy the QoS constraints of high-priority handoff calls, then decrease the high thresholds of low-priority calls and the high threshold of high-priority new calls  At any point, if determine that we have a threshold combination, break looping and continue with checking threshold values one less or one more than the current threshold value.

26. Search for Optimal Threshold Values Step 2  After finding a legitimate solution, adjacent threshold values in the range of current threshold ± Δ to determine a legitimate solution with a higher reward rate.  When no adjacent threshold returns a higher reward rate, the CAC algorithm returns the optimal value.

27. Numeric Data and Analysis A Simulated Wireless Cellular Network with a Wrap-Around Structure. Each cell having 6 adjacent cells The system with 1024 mobile user roaming there cells Poisson distribution to model call arrivals Exponential distribution to model the duration of a call Call inter-arrival and call departure rate to each mobile user by using uniform distribution

28. Performance Analysis Parameters used in Simulation Study

29. Reward Rate versus Number of Mobile Units. Partitioning CAC performs the worst in terms of reward rate among the four algorithms evaluated. up to 563 mobile users. Threshold-based and spillover CAC algorithms generates legitimate solutions up to 768 Elastic threshold-based CAC is up to 819

30. QoS of admission Control Alogrithms. Partitioning CAC rejects about 1% of low priority calls when the number of mobile user is 358, 0.7%, 2%, 3%, and 4.5% of class 1 handoff calls, class 1 new calls, class 2 handoff calls, and class 2 new calls, respectively. Threshold-based and spillover CAC algorithms have similar performance characteristics. Elastic threshold-based CAC is able to limit the acceptance rate of low-priority new calls to 92% by properly adjusting the low and high threshold values of low-priority new calls.

31. Conclusion Elastic threshold-based CAC algorithm, even in heavy load condition, is still capability of satisfying QoS requirement. Capable of leveraging the low threshold to regulate traffic and the high threshold to reject traffic generated by service calls. New threshold-based CAC algorithm has a goal to maximize the system reward rate with QoS guarantees.

32. Thank You ! Questions?