Characterizing the Use of a Campus Wireless NetworkOutlineOverviewRelated WorkNetwork EnvironmentSlide 6MethodologyAnalysisTraffic RateTraffic Rate AnalysisAuthentication DataSlide 12Authentication Data AnalysisNon-Wireless TrafficProtocol MixTraffic vs. AuthenticationDaily Traffic PatternsRoamingSlide 19Roaming AnalysisCollege of LawDesign PrinciplesConclusions and Future WorkDiscussionThe Synchronization of Periodic Routing MessagesSynchronySlide 27Synchrony in NetworkExamples of OccurrenceA Unique ExampleThis PaperMain ResultsHow do they find out…Periodic Messages ModelSlide 35Simulation of the ModelA Closer LookAvoiding SynchronizationMarkov Chain ModelSlide 40Analysis ResultsImportant ResultsSummaryInternet Routing InstabilitySlide 45Routing InstabilityEffects of InstabilityThe InternetBorder Gateway ProtocolInter-domain Routing UpdatesMajor ResultsMajor Results (2)Slide 53Some More BackgroundInitial ResultsEventsExpectations & ObservationsUpdate DistributionPathological ResultsOrigins of Routing PathologiesInstability AnalysisSlide 62A Representative WeekSlide 64Fine-Grained StatisticsImpact of Routing InstabilityRoutes AffectedConclusion / DiscussionCharacterizing the Use of a Campus Wireless Network David Schwab and Rick BuntUniversity of SaskatchewanINFOCOM 20042Outline•Goal and Related Work•Methodology•Experimental Results•Conclusions and Future Work3Overview•Performed a week long traffic trace–January 2003–Included centralized authentication log–Attempted to ensure privacy•Wireless network usage–Where, when, how much, for what–Roaming patterns4Related Work•Balachandran et al.–Gathered data at ACM conference–Set schedule forced traffic patterns–Close access points•Kotz and Essien–Gathered data at Dartmouth–Experienced access point failures and mis-configurations5Network Environment•Two different subnets for wired and wireless networks.•Centralized router connects the subnets to each other and the Internet•Traffic to and from wireless subnet was mirrored and recorded.RouterWiredWirelessInternetEtherPeek6Network Environment•18 Cisco access points•IP addresses are dynamically assigned–IP’s in trace had changed since analysis•Availability of technology not well advertised7Methodology•EtherPeek analyzed each packet–Date, time, origin, destination, and protocol–Able to record MAC addresses (identify unique users)•Anonymised administrator log provided–For each authentication•Date, time, MAC address, and IP of access point8Analysis•Perl scripts parse trace files•First pass indicated some non-standard packets as well as human error.•Focus order:1. Traffic data from trace file2. Authentication log3. Combined data9Traffic RatePacket sizes are unknown 10Traffic Rate Analysis•Clear night/day trend•Clear weekend/weekday trend•At least 15 packets/sec regardless of day and time.–From non-wireless traffic multicasted onto the network for maintenance11Authentication Data• Total authentications seems unreasonably high• Average authentications per user is much greater than median12Authentication DataBig skew towards Law School13Authentication Data Analysis•Some users are frequently switching access points.•Law School has (by far) the most authentications•Since wireless cards store authentication data, cards can rapidly switch APs if signal strength is similar.•Authentication != Distinct Sessions14Non-Wireless Traffic•38% of packets are not associated with users from the log.•Arrived at a constant rate of 15 packets/sec, regardless of time•Conclusion: Generated automatically and flooded onto wireless subnet, but why?•Still a mystery… but could improve performance if solved!15Protocol MixNon-Wireless Traffic• 87% SNAP• 7% ARP• Other trafficWireless Traffic• 27% HTTP• Normal file sharing, P2P, AIM, etc.• 34.6% “TCP Other”16Traffic vs. AuthenticationAuthentications do not seem to have much indication of traffic.17Daily Traffic Patterns18RoamingOn average, users visited 3 access points during the week19Roaming20Roaming Analysis•Even though the Geology Library is centrally located, few roaming users connected to it.•Clear relationship between proximity and roaming–High number of APs in close proximity = high rates of usages and roaming•Users won’t attempt to connect if they don’t know an AP is nearby21College of Law•86% of authentications from Law Student Lounge•Over one third of traffic by College of Law•Factors:–Major wireless commitment by the college–Wired computer labs have been closed–Paradigm shift within the legal community22Design Principles•Focus on location rather than movement–Better to have “islands” of connectivity then “continuous corridors” of coverage•Preference given to departments with lots of online material•Priority given to areas with a large number of mobile users (such as high-tech or professional programs)•For College of Law, a high level of connectivity was obtained with only a few APs23Conclusions and Future Work•Data collected in a centralized manner•Used both traffic trace and authentication log•Average user connected a small number of times from a limited number of APs•Popularity of an AP is determined by its familiarity•Continuing more in-depth studies and developing tools to measure performance24Discussion•As authors mention, this single week may not be representative of overall usage patterns.–What’s a better methodology?The Synchronization of Periodic Routing MessagesSally Floyd and Van JacobsonPresented by Wanmin Wu26Synchrony•In 1917, a letter to the journal Science described the phenomenon of fireflies apparently blinking on and off together in unison, but the writer dismissed it as the "twitching" of his eyelids.•Christian Huygens (1629-1695), a watchmaker, observed that two unsynchronized clocks would keep in time if hung on the same wall- synchronized by the barely perceptible vibrations each induced in the wall.27Synchrony•It is not just possible, it is inevitable.•Strogatz et al. proved mathematically that any system of "coupled oscillators" -- that is, entities capable of responding to each other's signals, be they crickets, electrons or planets -- will spontaneously self-organize28Synchrony in NetworkResearchers find the total network traffic is not uniform, but highly synchronized.29Examples of Occurrence•TCP congestion windows–Increase/decrease cycles shared by
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