A client-driven management approach for 802.11 (and other) networks

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

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 Recvd: 1, 2, 4 AP-1 Client-A AP-2 Recvd: 1, 3, 4, 5 Sent: 1, 2, 3, 4, 5 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 AP-1 What channels to assign to APs?

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

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

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

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

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

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 (6) (0) (20) (0) (4) (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 (6) (0) (20) (0) (4) (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 (6) (0) (20) (0) (4) (7) 37 (6) (8) (0) (0) (0) 21 (7)

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 (6) (0) (20) (0) (4) (7) 37 (0) (0) (0) Better (9) (4) (0) 13

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 (6) (0) (20) (0) (4) (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 “Degree of interference at clients” Vertex coloring 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. Limitations of vertex coloring • Overly conservative: • Does not examine how client-AP associations should be made (0) (3) (2) (0) (0) (2) (2) (0) (0) For conflict freedom, need 3 channels? It depends on client association

23. Limitations of vertex coloring • Overly conservative: • Does not examine how client-AP associations should be made (0) (3) (2) (0) (0) (2) (2) (0) (0) 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

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 C3 C1 C2 A2 A3 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 A1 C3 C1 C1 C2 A2 A3 A2 A3 C4

27. Conflict set coloring approach • Conflict-free set coloring formulation (a simplified view) • Each client is a set of one or more APs A1 A1 C3 C1 C2 C2 A2 A3 A2 A3 C4

28. Conflict set coloring approach • Conflict-free set coloring formulation (a simplified view) • Each client is a set of one or more APs A1 C3 C1 C2 A2 A3 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 C3 C1 C2 A2 A3 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 A1 C3 C1 C2 C2 A2 A3 A2 A3 C4

31. Conflict set coloring approach • Conflict-free set coloring formulation (a simplified view) • Each client is a set of one or more APs A1 A1 C3 C1 C1 C2 A2 A3 A2 A3 C4

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 C3 C1 C2 A2 A3 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 C3 C1 C2 A2 A3 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 CFAssign Vertex coloring > factor of 2 CFAssign Vertex coloring Std-dev of throughput even indicates greater fairness

38. CFAssign (Set approach) MAC level collisions CFAssign 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. Channel A Channel B Channel C Channel D Radio Frequency Spectrum Wireless channels Available spectrum is typically divided into disjoint channels

45. 2.4 GHz ISM Band Ch 1 Ch 6 Ch 11 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

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

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

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

49. Link A Ch 1 Link B Ch X How do we use I-Factor ? A1 A2 • Given I-Factor Node B1 can `estimate’ interference on all partially overlapped channels • And choose the best one! PX = I-Factor(1,X) * P1 B1 B2

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