Observing Home Wireless Experience through WiFi APs

Transcription

1 Observing Home Wireless Experience through WiFi APs Ashish Patro* Srinivas Govindan Prof. Suman Banerjee University of Wisconsin Madison

2 Dense residenfal WLANs today Apartment Building Apt 201 Apt 202 Access Points WiFi Clients Non- WiFi devices

3 Dense residenfal WLANs today Apartment Building Apt 201 Apt 202 NeSlix YouTube HD Rapidly increasing usage of WiFi devices and services (e.g., video)

4 Our goal: A measurement infrastructure How can we capture the wireless experience in home WLANs? What is the wireless performance at any instant? How o[en is wireless experience poor? What are the causes of poor performance?

5 PotenFal causes of poor performance Apartment Building Apt 201 Apt 202 NeSlix Busy Channel Ad- hoc deployment of APs with no/li^le coordinafon between APs

6 PotenFal causes of poor performance Apartment Building Apt 201 Apt 202 Link or external interference related performance issues

7 Measurement Framework: Capturing wireless experience Our approach: Inline measurements at home APs Internet

8 Why use WiFi APs as vantage points? Capture local link + channel condifons Observe neighboring WiFi + non- WiFi transmissions Inline Measurements at APs Monitor local sebngs (channel, tx power) Easy to maintain and deploy due to no addifonal infrastructure overhead

9 WiSe measurement infrastructure Wireless infrastructure for inline Sensing (WiSe) Microwave Oven Cordless Phone Laptop Smartphone Wireless TV Laptop Apartment Building AP AP ISP x Internet ISP y Last Hop ISPs Controller

10 WiSe deployment

11 WiSe deployment (30 APs) Building 1: APs 1 14 Individual Access Point per apartment Building 2: APs Deployment in common areas Others: APs Across different homes Ran deployment over 8 months

12 Data usage across WiSe APs Download per day (In Megabytes) Bldg 1 Others Bldg AP ID Highly variable data usage across different locafons Median of 30 MB 5.6 GB per day

13 Outline IntroducFon WiSe analydcs toolkit QuanFfying wireless performance (Wi^ metric) Measurement Results Related Work and Summary

14 Hardware OpenWrt based APs ALIX 2d2 plasorm: (500 MHz AMD Geocode CPU, 256 DDR RAM, flash storage) Two mini- PCI WiFi NICs Primary NIC acts as AP Secondary NIC as backup for addifonal measurments Ashish Patro / WiSe / MobiCom 2013

15 What do these WiSe APs collect? Aggregate StaFsFcs Non- WiFi acfvity Interference analysis

16 WiSe analyfcs toolkit Aggregate StaFsFcs Non- WiFi acfvity Interference analysis AirFme UFlizaFon Observed beacons CRC errors Client Signal Strength Packets sent + retried PHY rates stafsfcs AP StaFsFcs Client StaFsFcs

17 WiSe analyfcs toolkit Aggregate StaFsFcs Non- WiFi acfvity Interference analysis WiSe AP Generate signature IdenFfy device Device Type, Start Fme + durafon Signal strength Airshark (IMC 2011) Non- WiFi StaFsFcs

18 WiSe analyfcs toolkit Aggregate StaFsFcs Non- WiFi acfvity Interference Analysis WiSe AP MAC Fmestamp (in microseconds) Packet length PHY rates + RSSI Success/Loss Per- Link WiFi Header Summaries

19 WiSe analyfcs toolkit Aggregate StaFsFcs Non- WiFi acfvity Interference Analysis PIE (NSDI 2011) WiFiNet (NSDI 2012) WiSe AP 1. WiFi Packet Transmission Reports Co- relate transmissions (Fming overlaps) Apartment Building Wise AP n Non- WiFi acfvity informafon Controller

20 Outline IntroducFon WiSe analyfcs toolkit QuanDfying wireless performance (WiJ metric) Measurement Results Related Work and Summary

21 Characterizing wireless performance At a given Fme instant, what is the expected wireless throughput of my different wireless links? Good Moderate Poor WiSe AP Good Moderate Poor

22 Goal: Metric for wireless performance Capture impact of link and external wireless properfes on performance Only Passive + Coarse local measurements (10 sec local measurements at APs) ApplicaFon agnosfc Wi^ (WiFi based esfmated TCP throughput) Computed per AP Client pair

23 Building the Wi^ metric Ground Truth: AcFve TCP throughput measurements (wireless downlink) under different condifons Downlink throughput measurements Record wireless stafsfcs Wise AP AirFme UFlizaFon Signal strength MAC losses.... Test client

24 Determining candidates for Wi^ metric Correlated wireless stafsfcs with actual TCP throughput Feature CorrelaDon Coefficient (Absolute value) Busy airfme CRC Errors Local contenfon Signal strength EffecDve rate Link_exp model (Preferred) Best overall

25 Determining candidates for Wi^ metric Correlated wireless stafsfcs with actual TCP throughput Feature CorrelaDon Coefficient (Absolute value) Busy airfme Use Link CRC experience Errors model as candidate for Wi^ metric Local contenfon Signal strength EffecDve rate Link_exp model (Preferred) Best overall

26 Inputs for the Wi^ metric (using link experience) AirFme UFlizaFon (a) Busy airfme due to external sources Local contenfon (c) Cross- traffic from other clients EffecFve rate (r) Capture impact of successful packet (s i ) over total packets (p i ) sent at each PHY rate (r i )

27 CreaFng the Wi^ metric EsFmaFng per- link performance ( Link Experience ) Using busy airfme (a), local contenfon (c), effecfve rate (r) Use linear regression to obtain coefficients (β 0, β 1 ) from ground truth TCP throughput values Use Wi^ as a passive esfmate of TCP throughput (per link)

28 PredicFon Errors Compare Ground truth TCP throughput with predicted values g (Max. tput ~ 19 Mbps) CDF Airtime Signal Strength 0.2 Local Contention Effective Rate 0 Link exp "model" Estimation Error (Actual - Estimated, in Mbps) n (Max. tput ~ 34 Mbps) CDF Airtime Signal Strength 0.2 Local contention Effective Rate 0 Link exp "model" Estimation Error (Actual - Estimated, in Mbps)

29 PredicFon Errors Compare Ground truth TCP throughput with predicted values g (Max. tput ~ 19 Mbps) CDF Airtime Signal Strength 0.2 Local Contention Effective Rate 0 Link exp "model" % of errors within 1.5 Mbps and 3Mbps for g and n Estimation Error (Actual - Estimated, in Mbps) respecfvely n (Max. tput ~ 34 Mbps) CDF Airtime Signal Strength 0.2 Local contention Effective Rate 0 Link exp "model" Estimation Error (Actual - Estimated, in Mbps)

30 Outline IntroducFon WiSe analyfcs toolkit QuanFfying wireless performance Measurement Results Related Work and Summary

31 How o[en was performance poor? AcFve periods: 10 sec intervals with > 500 downlink packets per AP- Client pair. AcFve Periods Wi^ <= 4 Mbps Poor Overall Poor performance periods of 2.1% in our wireless traces Wireless StaFsFcs

32 Diagnosing Poor Performance Poor: Wi^ < 4 Mbps High airfme usage (> 60%)

33 Diagnosing Poor Performance Poor: Wi^ < 4 Mbps Poor signal strength (< - 70 dbm)

34 Diagnosing Poor Performance Poor: Wi^ < 4 Mbps High MAC losses (> 50%)

35 Diagnosing Poor Performance Poor: Wi^ < 4 Mbps Low PHY rates (< 12 Mbps)

36 Diagnosing Poor Performance Poor: Wi^ < 4 Mbps

37 Diagnosing Poor Performance Poor: Wi^ < 4 Mbps High MAC losses but, good signal strengths Client- side recepfon issues, Interference

38 Diagnosing Very Poor Performance Very Poor: Wi^ < 1 Mbps

39 Diagnosing Very Poor Performance Very Poor: Wi^ < 1 Mbps High AirFme + Packet Losses Dense AP deployments In Building 1 Channel congesfon main cause of poor performance

40 Diagnosing Very Poor Performance Very Poor: Wi^ < 1 Mbps

41 Diagnosing Very Poor Performance Very Poor: Wi^ < 1 Mbps Weak Signal + High Losses + Low PHY Rates Centralized AP deployments in Building 2 Weak signal main Ashish Patro cause / WiSe / MobiCom of poor 2013 performance

42 Diagnosing Very Poor Performance Very Poor: Wi^ < 1 Mbps Weak Signal + High Losses + Low PHY Rates The nature of the wireless deployment impacted the causes of the degraded wireless performance Centralized AP deployments in Building 2 Weak signal main Ashish Patro cause / WiSe / MobiCom of poor 2013 performance

43 Results 2: Microwave oven interference (30 day period) Duration per day (in minutes) AP ID Interference periods per day 1 20 minutes Bldg 1: APs 1-14 Others: APs Bldg 2: APs Num. days with MWO inteference AP ID But, prevalent at many locafons

44 Results 2: Microwave oven interference (30 day period) Duration per day (in minutes) Num. days with MWO inteference 10 Interference periods per day minutes Mainly short periods of interference (1 20 minutes) but recurring impact AP ID Bldg 1: APs 1-14 Others: APs Bldg 2: APs Due to bursty nature of interferer and link transmissions AP ID But, prevalent at many locafons

45 Outline IntroducFon WiSe analyfcs toolkit QuanFfying wireless performance Measurement Results Related Work and Summary

46 Related Work Measuring home broadband performance BISMark (SIGCOMM 11) Enterprise sniffer deployments Kotz et al. (MobiCom 02) Jigsaw (SIGCOMM 06) Urban scale WiFi deployments TFA- Rice Mesh networks (MobiCom 08)

47 Summary Deployed an infrastructure that uses APs to study home WLANs Presented a simple wireless performance metric Overall poor performance periods of 2.1% in our deployment Wireless problems dependent on the nature of deployment Observed short (1 20 minutes) but recurring instances of interference Bursty nature of link and interferer acfvity

48 Thanks!