1. 1 Token Bucket Based CAC and Packet Scheduling for IEEE 802.16 Broadband Wireless Access Networks Chi-Hung Chiang (g9203@cs.nccu.edu.tw)g9203@cs.nccu.edu.tw Tzu-Chieh Tsai (ttsai@cs.nccu.edu.tw) CCNC 2006 01/09/06

2. 2 Outline Introduction –Problem –IEEE 802.16 Standard –Related Work –Motivation 802.16 CAC and Uplink Packet Scheduling Token Rate Estimation Model Simulation Results Conclusions & Future Work

3. 3 What is IEEE 802.16

4. 4 Problem The IEEE 802.16 standard defines QoS classes, but it does not completely define how to achieve the QoS support –Packets scheduling is the key part, which is not defined in the 802.16 standard

5. 5 IEEE 802.16 Standard Four QoS classes –Unsolicited Grant Service (UGS) –Real-time Polling Service (rtPS) –Non-real-time Polling Service (nrtPS) –Best Effort (BE)

6. 6 Point-to-MultiPoint mode SS

7. 7 Operation process of 802.16 802.16 standard only defined the scheduling of UGS class –Allocate fixed bandwidth during fixed time IEEE 802.16 Standard

8. 8 Frame Structure of 802.16 The downlink scheduling is simpler than uplink, hence we focus on uplink scheduling IEEE 802.16 Standard

9. 9 Token Bucket Mean rate: token rate r Burst size: bucket size b Maximum size generated during time t: rt+b

10. 10 Related Work More complete 802.16 QoS architecture: [4] –Our main reference –Call admission control (CAC) and uplink packet scheduling were both proposed in this paper –[4] Kitti Wongthavarawat, and Aura Ganz, “Packet scheduling for QoS support in IEEE 802.16 broadband wireless access systems”, International Journal of Communication Systems, vol. 16, issue 1, February 2003, pp. 81-96

11. 11 Scheduling Architecture proposed by [4] Each connection i is controlled by a pair of parameters –Token rate r i and bucket size b i Main scheduling architecture in [4]

12. 12 Earliest Deadline First (EDF) Calculate the number of arriving packets during last frame Calculate the deadline of these packets and record them into a database Grant bandwidth according to the database

13. 13 Earliest Deadline First (EDF) Calculate the deadline –d i : maximum delay requirement of the connection i

14. 14 Earliest Deadline First (EDF) Calculate the deadline –d i must satisfy: d i /f=m i, where m i ≧ 2 m i is an integer –t-f+d i -f and t-f+d i are integral multiples of f

15. 15 rtPS CAC proposed by [4] This is not the sufficient condition for guaranteeing the d i of the rtPS connection

16. 16 Motivation The QoS architecture in [4] has some shortcomings –The CAC is not precise enough –The scheduling may cause starvation Not all traffic flows originally have token bucket parameters –A model is needed to calculate the appropriate token bucket parameters of a traffic flow

17. 17 Introduction Our 802.16 CAC and Uplink Packet Scheduling –CAC –Uplink packet scheduling Token Rate Estimation Model Simulation Results Conclusions & Future Work

18. 18 CAC Assume an rtPS connection i has a burst from t to t+6f –The maximum generating size: 6r i f+b i –b i may be consumed in a single frame when the traffic is very high

19. 19 CAC If is 3, two frames can share the b i –In this situation, the maximum bandwidth requirement in a frame is r i f+ b i

20. 20 CAC For guaranteeing the d i of an rtPS connection i, BS should at least grant bandwidth The total bandwidth requirement of rtPS connections C rtPS is

21. 21 CAC For preventing starvation, we set up a “threshold” parameter for each class –“threshold” here means minimum guaranteed bandwidth for each class –If the bandwidth usage of some class exceed its threshold, its priority over accessing resource will be downgraded

22. 22 CAC Notations –C uplink : The total capacity of the uplink sub-frame –C UGS : The capacity used by UGS connections –C rtPS : The total bandwidth requirements of rtPS connections –C nrtPS : The capacity used by nrtPS connections –C BE : The capacity used by BE connections –T UGS : The bound parameter of UGS class –T rtPS : The bound parameter of rtPS class –T nrtPS : The bound parameter of nrtPS class –T BE : The bound parameter of BE class –r i : The token rate of the new connection i

23. 23 CAC CAC algorithm for UGS C remain =C uplink -C UGS -C rtPS -C nrtPS -C BE If C remain ≧ r i, we accept it Else If C BE ＞ T BE, we decrease C BE to get more bandwidth until C remain ==r i or C BE ==T BE If C remain ＜ r i and C nrtPS ＞ T nrtPS, we decrease C nrtPS to get more bandwidth until C remain ==r i or C nrtPS ==T nrtPS If C remain ＜ r i and C rtPS ＞ T rtPS, we decrease C rtPS to get more bandwidth until C remain ≧ r i or C rtPS ≦ T rtPS (degrade the r i of some rtPS connections and update C rtPS ) If C remain ≦ r i, we accept it. Else we deny it.

24. 24 CAC CAC algorithm for rtPS C remain =C uplink -C UGS -C nrtPS -C BE Calculate new C rtPS by using (1) If C remain ≧ C rtPS, we accept it Else If C BE ＞ T BE, we decrease C BE to get more bandwidth until C remain == C rtPS or C BE ==T BE If C remain ＜ C rtPS and C nrtPS ＞ T nrtPS, we decrease C nrtPS to get more bandwidth until C remain == C rtPS or C nrtPS ==T nrtPS If C remain ＜ C rtPS, C rtPS ＜ T rtPS, and C UGS ＞ T UGS, we decrease C UGS to get more bandwidth until C remain ≧ C rtPS or C UGS ≦ T UGS (degrade the r i of some UGS connections and update C UGS ) If C remain ≦ C rtPS, we accept it. Else we deny it.

25. 25 CAC CAC algorithm for nrtPS C remain =C uplink -C UGS -C rtPS -C nrtPS -C BE If C remain ≧ r i, we accept it Else If C BE ＞ T BE, we decrease C BE to get more bandwidth until C remain ==r i or C BE ==T BE If C remain ＜ r i, C nrtPS ＜ T nrtPS, and C rtPS ＞ T rtPS, we decrease C rtPS to get more bandwidth until C remain ≧ r i or C rtPS ≦ T rtPS (degrade the r i of some rtPS connections and update C rtPS ) If C remain ＜ C rtPS, C nrtPS ＜ T nrtPS, and C UGS ＞ T UGS, we decrease C UGS to get more bandwidth until C remain ≧ C rtPS or C UGS ≦ T UGS (degrade the r i of some UGS connections and update C UGS ) If C remain ≦ C rtPS, we accept it. Else we deny it.

26. 26 CAC CAC algorithm for BE Accept it C remain =C uplink -C UGS -C rtPS -C nrtPS -C BE If C remain ＜ r i If C BE ＞ T BE and C nrtPS ＞ T nrtPS, we decrease C nrtPS to get more bandwidth until C remain == r i or C nrtPS ==T nrtPS If C remain ＜ r i, C BE ＜ T BE, and C rtPS ＞ T rtPS, we decrease C rtPS to get more bandwidth until C remain ≧ r i or C rtPS ≦ T rtPS (degrade the r i of some rtPS connections and update C rtPS ) If C remain ＜ r i, C BE ＜ T BE, and C UGS ＞ T UGS, we decrease C UGS to get more bandwidth until C remain ≧ C rtPS or C UGS ≦ T UGS (degrade the r i of some UGS connections and update C UGS )

27. 27 Uplink Packet Scheduling Step 1. –Calculate the arriving packets of each rtPS connection during the last frame –Calculate the deadlines of these packets by applying (1) and record them in the database Step 2. –Allocate bandwidth to UGS connections according to their token rates

28. 28 Uplink Packet Scheduling Step 3. –Allocate bandwidth to rtPS connections according to the database. We limit that the maximum allocated size of each rtPS connection is packets due to degradation

29. 29 Uplink Packet Scheduling Step 4. –Assume the total bandwidth requirements of nrtPS connections and BE connections are R nrtPS and R BE. We allocate Min(R nrtPS, T nrtPS ) bandwidth to nrtPS connections first. Then we allocate Min(R BE, T BE ) bandwidth to BE connections

30. 30 Uplink Packet Scheduling Step 5. –If there is remainder bandwidth, we look if R nrtPS ＞ T nrtPS. If R nrtPS ＞ T nrtPS, we grant Min(remainder bandwidth, R nrtPS -T nrtPS ) to nrtPS connections Step 6. –If there is remainder bandwidth, we look if R BE ＞ T BE. If R BE ＞ T BE, we grant Min(remainder bandwidth, R BE -T BE ) to BE connections

31. 31 Uplink Packet Scheduling Step 7. –If there is remainder bandwidth and there are nrtPS or BE connections that need BW-request contention opportunities, we allocate the remainder bandwidth to nrtPS connections and BE connections in order for BW-request contention periods

32. 32 Introduction 802.16 CAC and Uplink Packet Scheduling Token Rate Estimation Model –Case of infinite queue –Case of finite queue Simulation Results Conclusions & Future Work

33. 33 Token Rate Estimation Model Use a simple search algorithm to find appropriate token rate of a Poisson traffic flow given a reasonable bucket size

34. 34 Case of Infinite Queue Predict the queuing delay of a Poisson traffic flow in the token bucket queue –Assume a Poisson traffic flow with Infinite queue Mean arrival rate λ i Token rate r i Bucket size b i –We analyze the problem by applying Markov Chain

35. 35 Case of Infinite Queue Markov Chain –State(t, p): t is the amount of tokens in the bucket and p is the amount of packets in the queue –We use discrete Markov Chain The time interval is 1/r i The probability that n packets arrives during time interval 1/r i is –From State(b i, 0) to State(0, )

36. 36 Case of Infinite Queue States

37. 37 Case of Infinite Queue We denote State(t, p) by π (b i -t+p) and assume We can list the equations

38. 38 Case of Infinite Queue We can derive –given r i ＞ λ i

39. 39 Case of Finite Queue Predict the queuing delay and loss rate of a Poisson traffic flow in the token bucket queue –Assume a Poisson traffic flow with Finite queue whose size is q Mean arrival rate λ i Token rate r i Bucket size b i –We also analyze the problem by applying Markov Chain

40. 40 Case of Finite Queue Markov Chain –From State(b i, 0) to State(0, q-1) States

41. 41 Case of Finite Queue We denote State(t, p) by π (b i -t+p) and assume We can list the equations

42. 42 Case of Finite Queue We can derive –Where

43. 43 Case of Finite Queue The average loss rate L avg can be expressed as –[State(b i, 0)(1P(b i +q+1)+2P(b i +q+2)+ 3P(b i +q+3)+…)+ State(b i -1, 0)(1P(b i +q)+2P(b i +q+1)+ 3P(b i +q+2)+…)+ State(b i -2, 0)(1P(b i +q-1)+2P(b i +q)+ 3P(b i +q+1)+…)+.. State(0, 1)(1P(q)+2P(q+1)+ 3P(q+2)+…)+ State(0, 2)(1P(q-1)+2P(q)+ 3P(q+1)+…)+.. State(0, q-1)(1P(2)+2P(3)+ 3P(4)+…)]/(λ i /r i )

44. 44 Case of Finite Queue We can derive

45. 45 Introduction 802.16 CAC and Uplink Packet Scheduling Token Rate Estimation Model Simulation Results –CAC and uplink packet scheduling –Token rate estimation model Case of infinite queue Case of finite queue Conclusions & Future Work

46. 46 CAC and Uplink Packet Scheduling Uplink capacity: 37500000 bps Frame duration: 1 ms Simulation time: 150 ms Size of BW-request: 48 bits There are 100 UGS, nrtPS, and BE connections All connections send data in full speed and didn’t terminate

47. 47 CAC and Uplink Packet Scheduling Parameters of each class

48. 48 CAC and Uplink Packet Scheduling Avg. rtPS delay and acceptance of rtPS calls v.s. number of rtPS calls

49. 49 CAC and Uplink Packet Scheduling Avg. delay and throughput v.s. number of rtPS calls modifiedoriginal

50. 50 Case of Infinite Queue Simulation time ms Parameters

51. 51 Case of Infinite Queue Avg. delay v.s. token rate

52. 52 Case of Finite Queue All parameters are the same as the last case but an extra parameter –Queue size: 5120 bits

53. 53 Case of Finite Queue Avg. delay and loss rate v.s. token rate

54. 54 Introduction 802.16 CAC and Uplink Packet Scheduling Token Rate Estimation Model Simulation Results Conclusions & Future Work

55. 55 Conclusions & Future Work We present –CAC and uplink packet scheduling the delay requirements of real-time traffic were guaranteed Starvation was prohibited –A model that can determine the appropriate token rate of a Poisson traffic flow given the queuing delay and loss rate requirements We can precisely predict the queuing delay and loss rate of a traffic flow given some necessary parameters

56. 56 Conclusions & Future Work Future work –We may extend the token estimation model to the traffic flows that are not Poisson arrival in the future –An integration scenario is also one of the main objectives in the future.