1. A client-driven management approach for 802.11 (and other) networks Suman Banerjee Email: suman@cs.wisc.edu http://www.cs.wisc.edu/~suman Department of Computer Sciences University of Wisconsin-Madison Wisconsin Wireless and NetworkinG Systems (WiNGS) Laboratory

2. Wireless devices Experiencing phenomenal growth Dell ‘Oro group prediction: –“ … wireless LAN sales will grow 47% annually through 2008.” Wireless LAN industry annual sales is more than 2 billion dollar industry in the US Increasing deployment of Access Points (APs) in offices, homes, neighborhoods, etc.

3. Wireless LAN coverage Chicago area Bay area A handful of hotspots in 1998 Today: more than 2.5 million hotspots just in urban areas * * Source: war-driving reports in wigle.net

4. Management objectives Reduce costs –Eliminate the human in the loop Improve performance –At the clients Problem is inherently hard

5. Management in wired networks Mostly performed through central entities –Firewalls –Nameservers –DHCP servers A logical approach for many basic networking tasks –But needs some re-thinking in the wireless domain Many properties in wireless domain are location- specific –Can only be observed at the clients and by the clients

6. Impact of location Sent: 1, 2, 3, 4, 5 Client-A AP-1 AP-2 Recvd: 1, 3, 4, 5 Recvd: 1, 2, 4 Experience is property of location and cannot be always replicated

7. Talk outline Introduction Client-driven management example –Channel assignment and load balancing in wireless LANs An architecture for client-driven management –Virtualized wireless grids Other examples within this architectural framework –Secure localization –Network management: fault monitoring and diagnosis –Fast handoffs Summary of other activities in WiNGS

8. Channel assignment in WLANs Current best practices RF site survey based approaches –Fairly tedious signal strength maps of the area under consideration Least Congested Channel Search (LCCS) –Each AP examines congestion-level in a channel –If high congestion (i.e., it hears other APs), it tries to move to different channel –Repeat the process Other proprietary approaches (Airespace) None of them are client-centric in nature

9. Channel assignment problem AP-2 AP-3 What channels to assign to APs? AP-1

10. Channel assignment problem AP-2 AP-3 What channels to assign to APs? LCCS may assign same to all APs AP-1

11. Channel assignment problem AP-2 AP-3 Correct answer depends on client distribution and association AP-1

12. Channel assignment problem AP-2 AP-3 Correct answer should also adapt with client distributions AP-1

13. Channel assignment problem AP-2 AP-3 AP-1 Correct answer should also adapt with client distributions

14. A possible client-driven approach Client provide feedback to about observed “interference” Construct a virtual graph and do “weighted” graph coloring And then minimize graph weight AP-1 AP-2 AP-3 (4) (2) (0) Edge weight corresponds to number of interfered clients Higher edge weight implies greater importance of assigning APs to different channels [Vertex coloring: MC2R05]

15. Graph coloring approach Iterative approach Start with any initial coloring (even derived from LCCS) Each instant: –Pick an edge with maximum contribution to graph weight –Re-assign channel of one of its APs with a minimization objective –Leads to reduction to total graph weight (20) (0) (4) (6) (0) (7)

16. Graph coloring approach Iterative approach Start with any initial coloring (even derived from LCCS) Each instant: –Pick an edge with maximum contribution to graph weight –Re-assign channel of one of its APs with a minimization objective –Leads to reduction to total graph weight (20) (0) (4) (6) (0) (7)

17. Graph coloring approach Iterative approach Start with any initial coloring (even derived from LCCS) Each instant: –Pick an edge with maximum contribution to graph weight –Re-assign channel of one of its APs with a minimization objective –Leads to reduction to total graph weight (20) (0) (4) (6) (0) (7) (0) (8) (0) (6) (0) (7) 37 21

18. Graph coloring approach Iterative approach Start with any initial coloring (even derived from LCCS) Each instant: –Pick an edge with maximum contribution to graph weight –Re-assign channel of one of its APs with a minimization objective –Leads to reduction to total graph weight (20) (0) (4) (6) (0) (7) (0) (4) (0) (9) (0) 37 13 Better

19. Graph coloring approach Iterative approach Start with any initial coloring (even derived from LCCS) Each instant: –Pick an edge with maximum contribution to graph weight –Re-assign channel of one of its APs with a minimization objective –Leads to reduction to total graph weight Algorithm converges –Every step we are reducing the graph weight –Stops when cannot reduce further (20) (0) (4) (6) (0) (7)

20. Vertex coloring approach Client provide feedback to about observed interference Construct a virtual graph and do “weighted” graph coloring Minimize: Wt of graph Evaluation in simulations and on deployed testbed of 70+ APs LCCS Vertex coloring “Degree of interference at clients” Number of channels

21. Limitations of vertex coloring Overly conservative: –Does not examine how client-AP associations should be made ? ? ? For conflict freedom, how many channels do we need?

22. (3) (0) For conflict freedom, need 3 channels? It depends on client association Overly conservative: –Does not examine how client-AP associations should be made Limitations of vertex coloring (2) (0) (2) (0)

23. Overly conservative: –Does not examine how client-AP associations should be made Limitations of vertex coloring We should look at load-balancing (AP-client association) too! In this paper we define channel management to be: Channel assignment + load balancing through client-AP associations (3) (0) (2) (0) (2) (0)

24. Conflict set coloring approach CFAssign algorithms Jointly solve channel assignment and load balancing through client association Problem formulated as a set coloring problem, where each client is a set, and each AP is an element in one or more sets

25. Conflict set coloring approach Conflict-free set coloring formulation (a simplified view) –Each client is a set of one or more APs A1 A3A2 C1 C2 C3 C4

26. Conflict set coloring approach Conflict-free set coloring formulation (a simplified view) –Each client is a set of one or more APs A1 A3A2 C1 C2 C3 C4 A1 A3A2 C1

27. Conflict set coloring approach Conflict-free set coloring formulation (a simplified view) –Each client is a set of one or more APs A1 A3A2 C1 C2 C3 C4 A1 A3A2 C2

28. Conflict set coloring approach Conflict-free set coloring formulation (a simplified view) –Each client is a set of one or more APs A1 A3A2 C1 C2 C3 C4

29. Conflict set coloring approach Conflict-free set coloring formulation (a simplified view) –Each client is a set of one or more APs –Color all elements s.t. each set has an element with a unique color A1 A3A2 C1 C2 C3 C4

30. Conflict set coloring approach Conflict-free set coloring formulation (a simplified view) –Each client is a set of one or more APs A1 A3A2 C1 C2 C3 C4 A1 A3A2 C2

31. Conflict set coloring approach Conflict-free set coloring formulation (a simplified view) –Each client is a set of one or more APs A1 A3A2 C1 C2 C3 C4 A1 A3A2 C1

32. Conflict set coloring approach Conflict-free set coloring formulation (a simplified view) –Each client is a set of one or more APs –Color all elements s.t. each set has an element with a unique color –Associate each client to the unique colored AP in its set A1 A3A2 C1 C2 C3 C4

33. Conflict set coloring approach Conflict-free set coloring formulation (a simplified view) –Each client is a set of one or more APs –Color all elements s.t. each set has an element with a unique color –Associate each client to the unique colored AP in its set A1 A3A2 C1 C2 C3 C4 This is a conflict-free assignment of clients to APs (Prior vertex coloring approach will have used 3 colors)

34. Details What if conflict-freedom cannot be guaranteed? –Minimize the amount of conflict Load balancing fits into this objective function –It increases with number of clients added to the same AP Handle client-client interference –Sets consist of APs both in direct and indirect interference [Range and Interference sets]

35. A centralized algo (CFAssign-RaC) Pick an AP ordered by a random permutation Perform compaction step –For that AP, pick the best color assignment that maximizes the number of conflict-free clients based on the set formulation Repeat with another AP Can be repeated multiple times to obtain best solution Also have two distributed algorithms –[See our upcoming Mobicom 2006 paper]

36. Implementation details Feedback from clients to APs (infrastructure) uses mechanisms available in IEEE 802.11k standards –Site report Process is periodic in general, but triggered by client mobility Implementation is easy (~100 lines of code) Channel switching can be made quite fast –< 1 ms latency is achievable (ongoing work) –New Intel cards promising very fast switching (~ 100 us)

37. CFAssign (Set approach) Throughput Std-dev of throughput even indicates greater fairness > factor of 2 Vertex coloring CFAssign

38. CFAssign (Set approach) MAC level collisions LCCS CFAssign LCCS

39. CFAssign (Set approach) Adaptation to node mobility (3 channels)

40. We can do EVEN better! Should we restrict to non-overlapped channels? –In 802.11b: 1, 6, and 11 By using partially-overlapped channels

41. We can do EVEN better! Should we restrict to non-overlapped channels? –In 802.11b: 1, 6, and 11 How about 1, 4, 7, 11? –These are partially-overlapped channels Tradeoff between increased interference due to partially overlapped channels and more efficient utilization of spectrum Questions: –Can we define a mechanism to systematically model interference of partially- overlapped channels and extend existing channel assignment algorithms? –What performance improvement can we expect?

42. Talk outline Introduction Client-driven management example –Channel assignment and load balancing in wireless LANs –Partially overlapped channels and how to use them An architecture for client-driven management –Virtualized wireless grids Other examples within this architectural framework –Secure localization –Network management: fault monitoring and diagnosis –Fast handoffs Summary of other activities in WiNGS

43. Wireless channels Wireless communication happens over a restricted set of frequencies Collectively they constitute a channel

44. Wireless channels Available spectrum is typically divided into disjoint channels Radio Frequency Spectrum Channel AChannel BChannel CChannel D

45. Partially Overlapped Channels IEEE 802.11 defines 11 partially overlapped channels in 2.4 GHz band Only channels 1, 6 and 11 are non-overlapping 54 / 12 partially overlapped / non-overlapping channels in 5 GHz ISM band 2.4 GHz ISM Band Ch 1Ch 6Ch 11

46. Partially Overlapped Channels Partially overlapped channels are avoided –In order to avoid such interference Ch 1Ch 6Ch 3 Amount of Interference Link A Ch 1 Link C Ch 6 Link B Ch 3 ?

47. Simple Experiment Link A Ch 1 Link B Ch X

48. Define Interference Factor or “I-factor” Transmitter is on channel j P j denotes power received on channel j P i denotes power received on channel I Captures amount of overlap between channels I-Factor : Model for Partial Overlap PiPjPiPj I-factor(i,j) =

49. How do we use I-Factor ? Given I-Factor Node B1 can `estimate’ interference on all partially overlapped channels And choose the best one! Link A Ch 1 Link B Ch X A1A2 B1B2 P X = I-Factor(1,X) * P 1

50. Can we estimate I-factor? Measurement is an active process –Best if avoided We have designed a simple model of I-factor that is based on the transmit spectrum mask (IEEE standards specified) and the receiver’s band-pass filter profile

51. Estimating I-Factor Actual frequency response is hard to compute Transmit Spectrum Mask specified by IEEE 802.11

52. Estimating I-Factor Empirical Estimation: –Measure P i and P j –Take multiple samples –Calculate I-Factor = P i / P j

53. Overall methodology Wireless communication technology Such as 802.11, 802.16 Estimate I-factor Theory/empirical I-Factor Model Algorithm for channel assignment Channel assignment with overlapped channels Estimated once per wireless technology

54. How much Improvement to Expect ? Randomly distributed nodes Ad-hoc single hop network M channels in all, N non-overlapping M = 5*N - 4 for 802.11 (2.4 and 5 GHz) Throughput Improvement = 5 N – 4 1.2 N = a factor of 3.05 for 802.11 channels !

55. Can we use POV to pack more APs? Square grid, clients distributed uniformly at random Compare between: –3 non-overlapping channels 1, 6, 11 –4 partially-overlapping channels 1, 4, 7, 11 Same amount of wireless spectrum being used

56. Systematic scenario Three channels, the best case - three clique (three colorable) 1 6 11 1 0 0.2 0.4 0.6 0.8 1.0 4006008001000 3 channels

57. Systematic scenario Four partially overlapped channels: 1, 4,7, 11 Use four clique, to cover the same region More APs can be placed ‘closer’ Use I-factor to compute optimal placement 1 115 7 1 1000 0 0.2 0.4 0.6 0.8 1.0 400600800 3 channels 4 POV channels 5

58. Arbitrary Wireless LAN Modifications to existing CFAssign algorithm High density random topologies 2.6 x

59. Modifications to CFAssign algorithm Low density random topologies 1.7 x Arbitrary Wireless LAN

60. Summary of channel assignment Adaptation Better spectrum re-use Solution implicitly solves –Client-AP association –Extensions also provide load balancing Interoperates with legacy systems –Even systems that do not implement CFAssign benefit See papers [Infocom 2006], [MC2R 2005], [IMC 2005], [Mobicom 2006]