Self Management in Chaotic Wireless Networks Aditya Akella Glenn Judd Srini Seshan Peter Steenkiste Presented by Yanhua Mao 1 Wireless Proliferation Sharp increase in deployment Airports malls coffee shops homes 4 5 million APs sold in 3rd quarter of 2004 Past dense deployments were planned campus style deployments 2 Chaotic Wireless Networks Unplanned Independent users set up APs Spontaneous Variable densities Other wireless devices Unmanaged Configuring is a pain ESSID channel placement power Use default configuration Chaotic Deployments 3 Implications of Dense Chaotic Networks Benefits Great for ubiquitous connectivity new applications Challenges Serious contention Poor performance Access control security 4 Outline Quantify deployment densities and other characteristics Impact on end user performance Initial work on mitigating negative effects Conclusion 5 Characterizing Current Deployments Datasets Place Lab 28 000 APs Wifimaps 300 000 APs MAC ESSID GPS Selected US cities www placelab org MAC ESSID Channel GPS derived wifimaps com Pittsburgh Wardrive 667 APs MAC ESSID Channel Supported Rates GPS 6 AP Stats Degrees Placelab Placelab 28000 APs MAC ESSID GPS APs Max degree Portland 8683 54 San Diego 7934 76 San Francisco 3037 85 Boston 2551 50 m 1 2 1 39 7 Degree Distribution Place Lab 8 Unmanaged Devices WifiMaps com 300 000 APs MAC ESSID Channel Channel age 6 41 2 2 12 3 11 11 5 3 3 6 Most users don t change default channel Channel selection must be automated 9 Opportunities for Change Wardrive 667 APs MAC ESSID Channel Rates GPS Linksys Cisco Aironet Cisco Agere D Link Apple Netgear ANI Communications Delta Networks Lucent Acer Others 33 5 12 2 9 6 4 9 4 6 4 4 4 3 3 2 5 2 3 16 7 Major vendors dominate Incentive to reduce vendor self interference 10 Outline Quantify deployment densities and other characteristics Impact on end user performance Initial work on mitigating negative effects Conclusion 11 Impact on Performance Glomosim trace driven simulations D clients per AP Map Showing Portion of Pittsburgh Data Clients are located than 1m from their APs Transmit power 15dBm Trans range 31m Interference range 65m Each client runs HTTP FTP workloads HTTP transfers are separated by a sleep time drawn from Poisson s 12 Impact on HTTP Performance 3 clients per AP 2 clients run FTP sessions All others run HTTP 300 seconds Degradation 5s sleep time 20s sleep time 13 Max interference No interference Optimal Channel Allocation vs Optimal Channel Allocation Tx Power Control Channel Only Channel Tx Power Control Each AP is statically assigned 1 of the 3 non overlapping channels Some of the APs use a power level of 3dBm 14 Incentives for Self management Clear incentives for automatically selecting different channels Selfish users have no incentive to reduce transmit power Power control implemented by vendors Disputes can arise when configured manually Vendors want dense deployments to work Regulatory mandate could provide further incentive e g higher power limits for devices that implement intelligent power control 15 Impact of Joint Transmit Power and Rate Control Objective given load txPower dclient determine dmin require mediumUtilization 1 dclient APs txPower determines range dclient txPower determines rate dmin 16 Impact of Transmit Power Control minimum AP distance meters 100 0 1 Load 0 5 Mbps 0 5 0 7 0 9 1 1 10 2 Mbps 11 Mbps 5 5 Mbps Tx power dBm 19 16 13 10 7 4 1 2 5 8 2 0 1 7 1 4 1 1 1 Minimum distance decreases dramatically with transmit power High AP densities and loads requires transmit power 0 dBm 17 Highest densities require very low power can t use 11Mbps Outline Quantify deployment densities and other characteristics Impact on end user performance Initial work on mitigating negative effects Conclusion 18 Power Selection Algorithms Rate Selection Auto Rate Fallback ARF 6 consecutive packet transmissions selects the next higher transmission rate 4 consecutive packet trans failures selects the next lower transmission rate No packet is sent in 10 seconds uses the highest possible rate for the next transmission Estimated Rate Fallback ERF Each packet contains its transmit power level and the path loss and noise estimate of the last packet received This allows the sender to estimate the SNR at the receiver ERF then determines the highest transmission rate supported for this SNR 19 Power and Rate Selection Algorithms Joint Power and Rate Selection Power Auto Rate Fallback PARF At the highest rate after a given number of successful transmissions reduce the transmit power At the lowest rate after a given number of failures increase the transmit power Power Estimated Rate Fallback PERF The sender estimates the SNR at the receiver If SNR the decision threshold for the highest transmit rate lower the transmit power 20 Lab Interference Test Victim Pair Aggressor Pair Rate limited file transfer TCP benchmark 79 dB pathloss 95 dB pathloss 110 dB pathloss Topology Results 4 3 5 Throughput Mbps 3 2 5 2 1 5 1 0 5 21 0 No Interference ARF ERF PERF Conclusion Significant densities of APs in many metro areas Many APs not managed High densities could seriously affect performance Static channel allocation alone does not solve the problem Transmit power control effective at reducing impact 22 Ongoing Work Joint power and multi rate adaptation algorithms Extend to the case where TxRate could be traded off for higher system throughput Automatic channel selection Field tests of these algorithms 23
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