A Measurement Study of a Commercial-grade Urban WiFi Mesh

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1 A Measurement Study of a Commercial-grade Urban WiFi Mesh Vladimir Brik Shravan Rayanchu Sharad Saha Sayandeep Sen Vivek Shrivastava Suman Banerjee Wisconsin Wireless and NetworkinG Systems (WiNGS) Lab University of Wisconsin Madison IMC 2008

2 Introduction What? Measurement study of the performance and usage characteristics of an operational commercial urban wireless mesh network Why? Better understanding of these networks Previous measurement studies: Roofnet, TFA, DGP (all are custom testbeds built for experimentation) What is the state-of-the-art in the industry? How much of prior work is applicable?

3 Introduction MadMesh overview MadMesh More than 250 MAPs covering 10 sq. miles Has been operational for about 2 years now Provides service to more than 1000 residential customers, small businesses, public safety organizations

4 Introduction MadMesh Architecture Architecture Cisco 1510 MAPs, RAPs, Mesh controller Tree-based routing (vendor proprietary protocols) Backbone interface (802.11a, 5GHz) Access interface ( b/g, 2.4 GHz)

5 Study Goals & Data Sets Categories of study: Mesh planning and deployment Mesh routing User experience Usage characterization Data collection: Period infrastructure logs: SNMP (every 3 min), tools at the controller Passive measurements: Deployed indoor/outdoor nodes Active measurements: coverage, throughput experiments Two week period million SNMP records; 100 hrs of active measurements

6 Mesh Planning & Deployment Deployment Characteristics: What kind of connectivity does each MAP have? Is the network robust against failures? What are the link-level error rates and signal qualities on the backbone and access links? Does the network topology lend itself to new techniques like wireless network coding?

7 Mesh Planning & Deployment Average MAP Degree Q. What is the average MAP degree? Lower degree less re-routing choices during losses Higher degree over-provisioning, self-interference

8 Mesh Planning & Deployment Average MAP Degree Fraction MadMesh Roofnet Mesh Average degree of MAPs (in Logscale) MAPs: 20% have degree 2 and 50% have degree > 3 Much higher degree for Roofnet

9 Mesh Planning & Deployment Robustness Q. Is the deployment robust? Min-cut: the minimum number of edges, whose removal would disconnect the MAP from the graph

10 Mesh Planning & Deployment Robustness Mincut of Node Average Degree of Node path to the root 102 mincut = Connectivity for the highlighted node % of the MAPs have min-cut 2 Some MAPs with degree as high as 7 have min-cut 2

11 Mesh Planning & Deployment Error Rates Q. How good are the access and backbone links?

12 Mesh Planning & Deployment Error Rates Backbone Access # of MAPs 224 # of MAPs 15 Fraction Fraction MAC level Error Rate MAC level Error Rate Much higher loss rates on access links Why is this the case?

13 Mesh Planning & Deployment Channel Selection Fraction Backbone # of MAPs SNR (in db) Good quality backbone links Fraction 100 PER on access: (1) low SNR (2) interference Access # of MAPs 224 Best 70dB interference on 80% of the access links Channel selection can help Current Interference (in dbm)

14 Mesh Planning & Deployment Applicability of Network Coding Q. Are techniques like network coding applicable?

15 Mesh Planning & Deployment Applicability of Network Coding Q. Are techniques like network coding applicable? COPE: XOR operations, opportunistic listening Coding rule: To transmit n packets, p 1,..., p n, to n nexthops, r 1,..., r n, a node can XOR the n packets together only if each next-hop r i has all n 1 packets p j for j i. We calculate the maximum coding gain at each MAP Depends on a number of factors; Measure of overhearing supported by the deployment

16 Mesh Planning & Deployment Applicability of Network Coding Maximum coding gain % (Overhearing) No Overhearing Leaf Nodes MAP Index For 66% of the MAPs, coding gain was 2 24% of MAPs had coding gain > 2 (Max. coding gain was 6) Network coding can indeed improve the performance

17 User Experience Characterizing the user experience: How good is the client connectivity? Are coverage holes prevalent? What are the observed client throughputs?

18 User Experience Client connectivity Q. How prevalent are coverage holes? Estimate using a path-loss model: P dbm (d) = P dbm (d 0 ) 10αlog 10 ( d d 0 ) + ɛ Collected signal strength information at 25 locations for each MAP, and then estimated α, ɛ

19 User Experience Client connectivity Path-loss modeling results: α = 2.3 (Campus) α = 2.9 (Downtown) Received Signal Strength (dbm) Mean +7 Stdev -7 Stdev Measurements -90 Pathloss Exp = Shadowing Std. = Distance (m) Received Signal Strength (dbm) Mean +4 Stdev -4 Stdev Measurements -90 Pathloss Exp = Shadowing Std. = Distance (m) Path-loss varies with each MAP (location)

20 User Experience Client connectivity Propagation model based radio map shows this area to be covered Simple monitoring tool at the clients More holes prevalent at vehicular speeds Client feedback can really help

21 User Experience Client Throughput Q. What are the throughputs achieved at different locations?

22 User Experience Client Throughput Q. What are the throughputs achieved at different locations? Random sample of 100 locations 3 runs of TCP iperf tests, 100 seconds each

23 User Experience Client Throughput Results of throughput tests: CDF of sampled locations TCP Throughput (Mbps) 10% of the clients have less than 0.2 Mbps, while 80% have less than 1 Mbps

24 User Experience Client Throughput Results of throughput tests: CDF of sampled locations Factors: RSSI Hop-count Channel Congestion Shared Congestion TCP Throughput (Mbps) 10% of the clients have less than 0.2 Mbps, while 80% have less than 1 Mbps

25 Usage Characterization Characterizing mesh usage: How many clients are using the network? How does the usage vary with time? How does client distribution vary with MAPs, across different hops etc.?

26 Usage Characterization Distribution across time Q. How does the usage vary with time?

27 Usage Characterization Distribution across time Q. How does the usage vary with time? Average number of users max = 627 avg = 498 3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:00 Time of day Most number of clients were connected at around 10 PM

28 Usage Characterization Distribution across MAPs Q. How does the usage vary across MAPs?

29 Usage Characterization Distribution across MAPs Q. How does the usage vary across MAPs? Average number of clients % (14) 3 50% (40) 2 75% (80) MAP Index Clearly, certain MAPs are more popular than others 50% of clients are connected to 40 MAPs (20%)

30 Summary Main observations: Robustness ensure path diversity Bottleneck it is the access link; channel selection can help Management client feedback can really help Techniques like network coding are applicable User characteristics night time peaks and uneven usage

31 Questions?

32 Other slides

33 Mesh Routing Route choices Routing Strategy EASE metric (SNR, hop-count) How well does it perform?

34 Mesh Routing Route choices # of MAPs 224, # of Clients 498 MAPs at each hop Users at each hop Fraction Hop count For 15% of the MAPs are RAPs 60% of MAPs have hop-count 2 8% of MAPs have hop-count 5

35 Mesh Routing Route choices Fraction of MAPs Best Current Hop count Chose only neighbors with SNR 14dB Shorter paths were indeed available

A Measurement Study of a Commercial-grade Urban WiFi Mesh

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