AboutTraceAnalysis

AboutTraceAnalysis - Things about Trace Analysis Wei-jen...

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Things about Trace Analysis Wei-jen Hsu In class presentation for CIS6930 wjhsu@ufl.edu (Advisor: Ahmed Helmy)
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Objective More background knowledge related to trace-based study Details about the trace format – an intro for one of the assignments Share the experience in trace analysis
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Why trace analysis? Traces provide the “realism” of how the system work Verification of established system Diagnosis of system operation (identify faults) Identifying design flaws Large-scale properties (e.g. self-similar traffic) Understand how a new system works Provide domain knowledge for analysis work Verifying an idea
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Typical Work Flow for Trace Analysis 1. Build the system 2. Identify point(s) of trace collection and the methodology used 3. Obtain the data 4. Clean-up and sanity check 5. Analyze the data and post processing 6. Explain the results 7. Apply the results to further study or modify the existing system
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WLAN Traces Study It starts back around 2000 WLAN was new, people wanted to understand how people used it (usage study) Surveys v.s. trace Work by Tang and Baker (’00), Kotz and Essien (’02) are pioneer examples Statistics of usage (# of users, amount of traffic, etc.)
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WLAN Traces Study Mobility-related MIT work (home location, prevalence, and persistence) UCSD (PDA users) WLAN mobility model (INFOCOM05, T-model, T++-model) Other user properties Handoff Pause time distribution
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Trace Format For association Usually with format (Node_id, start_time, location, end_time) But with various ways to get you there…. Syslog: Event-based SNMP: Polling USC raw trace Wireless association (time start/stop switch- port MAC) DHCP log (time MAC IP) Traffic log
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Trace Format Example USC wireless association trace (Time Start/Stop Switch_IP Switch_port MAC_of_node) Mon Oct 10 01:16:52 Start 172.16.8.245 31005 0:30:65:f9:c0:ae Mon Oct 10 01:17:00 Stop 172.16.8.245 21044 0:e:35:99:64:d1 Mon Oct 10 01:17:02 Start 172.16.8.245 31015 0:11:24:df:c0:3a USC DHCP trace (Time IP_of_node MAC_of_node) Jan 27 00:21:19 207.151.229.50 0:18:f3:10:ea:4c Jan 27 00:21:20 207.151.232.184 0:18:de:33:7:92 Jan 27 00:21:20 207.151.229.50 0:18:f3:10:ea:4c USC traffic trace (Start_time End_time Destination_IP_port Source_IP_port protocol(TCP=6, UDP=17) “?” Packet_number Data_size) 0127.23:59:42.925 0127.23:59:44.905 128.125.253.143 53 207.151.239.208 1795 17 0 3 1368 0127.23:59:42.925 0127.23:59:52.677 63.236.56.237 80 207.151.239.208 3257 6 2 4 192
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Work with the Trace An exercise: “Does the Encounter-Relationship graph change with respect to time??” From WLAN traces, We find “encounters” to measure inter- node relationship Note: Is this a good assumption??
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Encounter distribution How many other nodes does a node encounter with? Prob. (unique encounter fraction > x) Not many for WLAN users. On avg. only 2%~7% of population 0.5
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Encounter-Relationship graph Imagine that there is a link to connect the node pairs if they ever encounter with each other … What does the graph look like?
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This note was uploaded on 05/27/2011 for the course CIS 4930 taught by Professor Staff during the Spring '08 term at University of Florida.

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AboutTraceAnalysis - Things about Trace Analysis Wei-jen...

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