Wireless Scheduling and Channel Assignment

1. Wireless Scheduling and Channel Assignment Presenter: Gaurang Sardesai & Xi Liu

2. Exploiting Medium Access Diversity (MAD) in Rate Adaptive Wireless LANs • Goal • Exploit variations in channel conditions and improve overall network throughput • How? • Obtain instantaneous channel information from multiple receivers and selectively transmit data to receiver that improves overall throughput of network • Maintain temporal fairness.

3. Rate Adaptivity and Multi User Diversity • Rate bounded by signal to noise ratio which changes over time. • Upper layers should react quickly to changes in channel conditions • What is multi user diversity? • Instantaneous channel conditions are time varying and not correlated • Aggressively assess channel conditions and select receiver whose channel conditions are at the peak • If experiencing failures, switch users • Not only best user, but best rate as well • So why can’t you apply this algorithm to DCF?

4. Overview Sender Receivers Data Query Reply

5. What do you need to take care off? • Overhead of Probing • Maximize throughput, especially when conditions are favorable • Be fair between multiple traffic flows. This usually conflicts with previous objective. • 3 phases • Channel Probing • Data Transmission • Receiver Scheduling

6. Channel Probing • Group RTS • CTS • 2 additional Fields • Rate • Gain • Probing concludes • Problems? • Duration Field • Conservative Estimate

7. Data Transmission • Might as well send as many as you can • OAR • Low data rate for hidden terminal problem • SIFS fixed duration • Enter PAC • Transmit SuperFrame, followed by string of Data frames • Receiver waits for SIFS, sends group ACK • Number of Packets should not exceed ratio of current to base rate for fairness • Retransmission counter for each packet • SF contains RA bitmap. ACK also modified

8. Data Transmission contd

9. Receiver Scheduling • Choose node with maximum relative gain for each transmission phase. • Maximum relative gain scheduling has temporal fairness, and the difference in throughput is bounded. • As number of receivers increase, overhead increases. So two approximations proposed for ideal scheduling algorithm. • K-set round robin • Revenue Based

10. Performance Analysis • How many do you want to query? • Optimal value 3 • Network Throughput • Compare OAR, PAC and DCF

11. Performance Evaluation

12. Performance Evaluation w.r.t. Topology

13. Fairness and Load Balancing in Wireless LANs Using Association Control • Motivation • User associates with AP that has strongest RSSI, ignoring the load • Load is unevenly distributed among APs • Unfair bandwidth allocation among users • Goal • Balanced load and fair bandwidth allocation • Basic idea • Association control (user-AP association) to ensure max-min fairness bandwidth allocation and min-max load balancing

14. Basic idea • Each user monitors the signal strength of beacons from nearby APs • Measures the effective bit rate • Clients submit this information to a network control center (NOC) • NOC runs scheduling and decides users associations • Users switch association accordingly

15. Single association vs. Fractional association Infrastructure Infrastructure AP1 AP2 AP2 AP1 Single association Fractional association

16. Max-min fairness • Informally • if there is no way to give more bandwidth to any user without decreasing the allocation of another user with less or equal bandwidth • Formally • Allocation vector B={b1,…,bn}, bi is the bandwidth allocated to user i • Lexicographically largest feasible allocation

17. a b c a b c 1 2 3 4 5 1 2 3 4 5 a b c a b c 1 2 3 4 5 1 2 3 4 5 Example of max-min fairness 2 2 2 2 4 1 1 2 2 B’ = {1, 1, 1, 1, 1} b=1 for each user Wireless System 2 1 2 4 4 2 1 4 4 2 2 B’ = {1, 4/3, 4/3, 4/3, 4/3} B’ = {1, 1, 1, 2, 2} Max-min fairness fractional association Max-min fairness single association

18. Load • What is a good indicator of load? • Number of users associated? (X) • Throughput of AP? (X) • Intuitively • the load of an AP needs to reflect its inability to satisfy the requirements of its associated users • it should be inversely proportional to the average bandwidth that it experiences

19. Load (contd.) • Each client associates with an AP fractionally • E.g. Node n1 associates with AP1 1/2 of the time, and effective data rate is 3Mbps • The load a client poses on an AP • E.g. Node n1 induce a load of 1/6 s/Mb on AP1 • The load on AP is the sum of loads from associated clients

20. a b c a b c 1 2 3 4 5 1 2 3 4 5 a b c a b c 1 2 3 4 5 1 2 3 4 5 Example of min-max load balance 2 2 2 Ỹ = { 1, 1, 1} 2 4 1 2 2 1 B’ = {1, 1, 1, 1, 1} b=1 for each user Wireless System 2 Ỹ = { 1, 3/4, 3/4} Ỹ = { 1, 1, 1/2} 1 2 4 4 2 1 4 4 2 2 B’ = {1, 4/3, 4/3, 4/3, 4/3} B’ = {1, 1, 1, 2, 2} Max-min fairness fractional association Max-min fairness single association

21. 1 1 2 2 3 3 4 4 5 5 Relationship of max-min fairness and min-max load balance • In the fractional association case, a min-max load balanced association X defines a max-min fair bandwidth allocation and vice versa. • However, the theorem is not satisfied in the case of a single association. Infrastructure Infrastructure APa APb APc APc APa APb 2 1 4 4 2 2 1 4 4 2 Y’ = { 1,1,1/2} Y’ = { 1,1,1/2} B’ = { 1,1,1,2,2} B’ = { 1,1,1,1,2}

22. Integral load balancing • It is NP-hard • Step 1: Finding optimal fractional association • In each iteration, identify bottleneck access points and users • Remove them and start the next iteration • This algorithm yields a min-max load balanced association • Step 2: Rounding to obtain approximate integral association

23. Bottleneck detection • Calculates an fractional association that minimizes the maximum load on all APs • Use linear program to minimize bottleneck load • It only optimizes bottleneck, but not other APs • Minimize sum of load on all APs, given bottleneck load (Identify those APs in bottleneck load group) • Use another linear program • Build a directed graph to see whether load can be shifted from one AP to another

24. ya = 1 yb = 1/2 yc = 1 APa APb APc 2 The nodes’ final colors The graph G(V,E) and the nodes’ initial colors 3 4 1 4 4 2 2 5 1 Example of bottleneck detection b c a a b c A possible association calculated by LP2

25. Simulation • Compare with Strongest-Signal-First and Least-Loaded-First • User effective bit rate only depends on distance only • Backhaul capacity is 10Mbps • Transmission range is 150m • 20 APs • 5 * 4 grid • Inter-AP distance is 100m • 100 users

26. Results – Per-user bandwidth

27. Summary • Consider fairness in conjunction with load balancing • "In the presence of hotspots, our algorithms provide fair service to all users accessing the network, while also maximizing the amount of bandwidth they receive," said Yigal Bejerano, a researcher in Bell Labs' Internet Management Lab. Bejarano continued, "Typically our algorithms also yield higher network utilization than the most commonly used 'strongest signal approach, while today's approaches tend to focus on overall throughput when allocating network resources. We believe that understanding the correlation between fairness and load-balancing are critical in order to maximize bandwidth for all users."

28. Coordinated Load Balancing, Handoff/Cell-site Selection, and Scheduling in Multi-cell Packet Data Systems • Motivation • Inter-cell interference • Asymmetric load distribution • Goal • Improve global resource utilization • Reduce regional congestion • Basic idea • Packet-level scheduling • Call-level cell-site selection and handoff • System-level load balancing

29. Model • Entities • Central server • Base station (BS) • Mobile station (MS): minRate requirement • Link model • Path loss • Fast Reyleigh fading • Slow shadowing fading • Channel rate depends on SINR

30. System Coordination • Mobile Station • Channel strength at from each BS • Number of active users at each BS • Choose the optimal serving BS • Constantly measure average throughput for load-aware handoff • Base Station • Broadcasts mean number of its binding MSs • Periodically updates load to a central controller • Central Controller • Executes centralized tuning of cell coverage (Cell breathing)

31. Example

32. Packet-level scheduling • Assignment problem • Goal is to maximize the long-term revenue • At each timeslot, each BS can choose any MS to serve • MS can be served by at most one BS at a time • Problems • Require fine-grained global knowledge • The computation is required for each timeslot • Suboptimal solution • Each MS binds to a BS (dynamic binding) • Each cell schedule by BS

33. Cell-site selection (MS) • Cross-layer scheme • Instead of merely SINR-based • Goal is to maximize the net increment of utility • + New utility • - Utility drop by other competing stations • Estimate new throughput • Rate / Num of users • BS accepts admission of MS if and only if total capacity after accepting the MS does not exceed 1 • Conservative but robust

34. Weighted Alpha Rule (BS) • Assignment problem inside a cell • Utility function to achieve (w,α)-proportional fairness • w is weight • α is a tuning knob balancing fairness and aggregate throughput • α = 0, scheduler is biased toward maximum throughput • α = 1, scheduler assigns slots equally • Minimum rate requirement • Tune weight

35. Cell breathing (Controller) • If a cell is more congested than its neighbor cells, it reduces α • Reducing α makes the scheduler to bias toward fast station • BS will allocated less slots to MSs at cell boundary • Boundary MSs will monitor less throughput and may trigger handoff • Effectively the cell coverage is reduced • Load is defined to be the ratio of minimum required rate to actual data rate

36. 3-tier cell system

37. Analysis - dynamics

38. Analysis - performance

39. Performance Anomaly of 802.11b • Useful throughput is much smaller than nominal bit rate • 7.74Mbps vs. 11Mbps • Contention time strongly depends on number of contending hosts • Fast host obtain the same throughput as slow host • Slow host will may considerably limit throughput

40. Facilitating Access Point Selection in IEEE 802.11 Wireless Networks • Basic idea • The bandwidth an end-host is likely to receive if it were to affiliate with a given access point • Use timing to estimate the load on AP and the contention inside the network

41. Experimental results

42. Questions • What is the difference between load on access point in wireless network and load on load on routers in wired network? • If the users always associate with the AP with the highest throughput, will it lead to max-min fairness?

43. Reference • Improving protocol capacity with model-based frame scheduling in IEEE 802.11-operated WLANs, Proceedings of the 9th annual international conference on Mobile computing and networking, ACM, San Diego, CA, USA • Yigal Bejerano Seung-Jae Han and Li (Erran) Li, Fairness and Load Balancing in Wireless LANs Using Association Control, Proc. International Conference on Mobile Computing and Networking (MobiCom), Philadelphia, PA, September 2004. • Exploiting Medium Access Diversity in Rate Adaptive Wireless LANs Z. Ji, Y. Yang, J. Zhou, M. Takai and R. Bagrodia. To appear in Proceedings of ACM MOBICOM 2004, Philadelphia, Sep 26 - Oct 1, 2004. • Martin Heusse, Franck Rousseau, Gilles Berger-Sabbatel, and Andrzej Duda. Performance Anomaly of 802.11b. In Proc. of IEEE INFOCOM, March 2003 • Victor Bahl, Ranveer Chandra, and John Dunagan. SSCH: Slotted Seeded Channel Hopping for Capacity Improvement in IEEE 802.11 Ad-Hoc Wireless Networks. Proc. of ACM Mobicom 2004, Sept.-Oct. 2004. • Ashish Raniwala and Tzi-Chiueh. Architecture and Algorithms for an IEEE 802.11-based Multi-channel Wireless Mesh Network In Proc. of IEEE INFOCOM, March 2005. • Coordinated Load Balancing, Handoff/Cell-site Selection, and Scheduling in Multi-cell Packet Data Systems, Aimin Sang, Xiaodong Wang, Mohammad Madihian, Richard Gitlin, ACM Mobicom 2004. • Facilitating Access Point Selection in IEEE 802.11 Wireless Networks, S. Vasudevan, K. Papagiannaki, C. Diot, J. Kurose, and D. Towsley, In ACM Internet Measurement Conference, 2005

44. Q & A • Thanks!