A Theory of QoS for Wireless

1. A Theory of QoS for Wireless I-Hong Hou Vivek Borkar P.R. Kumar University of Illinois, Urbana-Champaign

2. Background: Wireless Networks • There will be increasing use of wireless networks for serving traffic with QoS constraints: • VoIP • Video Streaming • Real-time Monitoring • Networked Control 1/19

3. Challenges • How to formulate a relevant and tractable framework for QoS? • Relevant – Jointly deal with three criteria: • Deadlines • Delivery ratios • Channel unreliabilities • Tractable – Provide solutions for QoS support: • Admission control policies for flows • Scheduling policies for packets 2/19

4. Client-Server Model • A system with N wireless clients and one AP • Time is slotted • One packet transmission in each slot • successful with probability pn • failed with probability 1-pn • Non-homogeneous link qualities • p1,p2,…,pN may be different 1 2 p1 p2 AP slot length = transmission duration p3 3 pN N 3/19

5. Protocol for Operation • AP indicates which client should transmit in each time slot • Uplink • Poll (ex. CF-POLL in 802.11 PCF) Data No need for ACK pn = Prob( both Poll/Data are delivered) Downlink Data ACK pn = Prob( both Data/ACK are delivered) n CF-POLL AP 4/19

6. Protocol for Operation • AP indicates which client should transmit in each time slot • Uplink • Poll (ex. CF-POLL in 802.11 PCF) • Data No need for ACK pn = Prob( both Poll/Data are delivered) Downlink Data ACK pn = Prob( both Data/ACK are delivered) n Data AP 4/19

7. Protocol for Operation • AP indicates which client should transmit in each time slot • Uplink • Poll (ex. CF-POLL in 802.11 PCF) • Data • No need for ACK • pn = Prob( both Poll/Data are delivered) Downlink Data ACK pn = Prob( both Data/ACK are delivered) n AP 4/19

8. Protocol for Operation • AP indicates which client should transmit in each time slot • Uplink • Poll (ex. CF-POLL in 802.11 PCF) • Data • No need for ACK • pn = Prob( both Poll/Data are delivered) • Downlink • Data ACK pn = Prob( both Data/ACK are delivered) n Data AP 4/19

9. Protocol for Operation • AP indicates which client should transmit in each time slot • Uplink • Poll (ex. CF-POLL in 802.11 PCF) • Data • No need for ACK • pn = Prob( both Poll/Data are delivered) • Downlink • Data • ACK • pn = Prob( both Data/ACK are delivered) n ACK AP 4/19

10. QoS Model time slot Timeline of the system 5/19

11. QoS Model • Clients generate packets with fixed period τ Deadline = Period Packets expire and are dropped if not delivered by the deadline Delay of successfully delivered packet is at most τ Delivery ratio of packets that meet deadline for client n should be at least qn arrival arrival arrival arrival τ 5/19

12. arrival deadline QoS Model • Clients generate packets with fixed period τ • Deadline = Period • Packets expire and are dropped if not delivered by the deadline Delay of successfully delivered packet is at most τ Delivery ratio of packets that meet deadline for client n should be at least qn 5/19

13. QoS Model • Clients generate packets with fixed period τ • Deadline = Period • Packets expire and are dropped if not delivered by the deadline • Delay of successfully delivered packet is at most τ Delivery ratio of packets that meet deadline for client n should be at least qn arrival deadline τ 5/19

14. QoS Model • Clients generate packets with fixed period τ • Deadline = Period • Packets expire and are dropped if not delivered by the deadline • Delay of successfully delivered packet is at most τ • Delivery ratio of packets that meet deadline for client n should be at least qn delivered delivered dropped 5/19

15. Problem Formulation • Admission control • Decide whether a set of clients is feasible • Scheduling policy • Design a policy that fulfills every feasible set of clients 6/19

16. Work Load • The proportion of time slots needed for client n is 7/19

17. Work Load • The proportion of time slots needed for client n is expected number of time slots needed for a successful transmission 7/19

18. Work Load • The proportion of time slots needed for client n is number of required successful transmissions in a period 7/19

19. Work Load • The proportion of time slots needed for client n is normalize by period length 7/19

20. Work Load • The proportion of time slots needed for client n is • We call wn the “work load” 7/19

21. Necessary Condition for Feasibility of QoS Requirements • Necessary condition from classical queuing theory: • But the condition is not sufficient • Packet drops by deadline misses cause more idleness than in queuing theory forced idleness 1 2 S X S X AP 8/19

22. Stronger Necessary Condition • Let I be the expected proportion of idle time • Stronger necessary condition • Sufficient? Still NO! 9/19

23. Even Stronger Necessary Condition • Let IS = Expected proportion of the idle time when the set of clients is S • IS decreases as S increases • Theorem: the condition is both necessary and sufficient 10/19

24. Largest Debt First Scheduling Policies • Give higher priority to client with higher “debt” F F F S 1 2 AP 3 F S 11/19

25. Two Definitions of Debt • The time debt of client n • time debt = wn – actual proportion of transmission time given to client n • The weighted delivery debt of client n • weighted delivery debt = (qn – actual delivery ratio)/pn • Theorem: Both largest debt first policies fulfill every feasible set of clients • Feasibility Optimal Policies 12/19

26. Tractable Policy for Admission Control • Admission control consists of determining feasibility • Apparently, we need to check: • 2N tests, so computationally complex • Theorem: Can be made into N tests • Polynomial time algorithm for admission control • Order clients by qn 13/19

27. Simulation Setup • Implement on IEEE 802.11 Point Coordination Function (PCF) • Application: VoIP standard • Deadline miss ratio(DMR) = shortfall in delivery ratio 14/19

28. Evaluated Four Policies • DCF • PCF with randomly assigned priorities • Time debt first policy • Weighted-delivery debt first policy 15/19

29. Test at Edge of Feasibility • Two groups of clients: • Group A requires 99% delivery ratio • Group B requires 80% delivery ratio • The nth client in each group has (60+n)% channel reliability • Feasible set: 6 group A clients and 5 group B clients • Infeasible set: 6 group A clients and 6 group B clients 16/19

30. Results for a Feasible Set 17/19

31. Results for a Feasible Set fulfilled 17/19

32. Results for a Feasible Set 17/19

33. Results for a Feasible Set 17/19

34. Results for an Infeasible Set 18/19

35. Results for an Infeasible Set small DMR 18/19

36. Results for an Infeasible Set 18/19

37. Results for an Infeasible Set 18/19

38. Conclusion • Formulate a framework for QoS that deal with deadlines, delivery ratios, and channel unreliabilities • Characterize when QoS is feasible • Provide efficient scheduling policy • Provide efficient admission control policy • Address implementation issues 19/19

39. Thank You

40. Backup Slides • An example: • Two clients, τ = 3 • p1=p2=0.5 • q1=0.876, q2=0.45 • w1=1.76/3, w2=0.3 • I{1}=I{2}=1.25/3, I{1,2}=0.25/3 • w1+I{1}=3.01/3 > 1 • However, w1+w2+I{1,2}=2.91/3 < 1