Yung-Chih Chen Jim Kurose and Don Towsley Computer Science Department University of Massachusetts Amherst A Mixed Queueing Network Model of Mobility in a Campus Wireless Network

Motivation Mobility modeling (till now) – Theoretical models Random WayPoint/Walk – Real world user mobility modeling Mobility pattern [Kim’07, Hsu’06] – Contact-based mobility [Chaintreau’06, Hsu’10] – Group-based mobility [Hong’99, Wang’02, Chen’10] Merge/split process [Heimlicher’10] Modeling becomes complicated…. 2

Simple model to capture user behavior – Users moving from AP to AP – Predict system level performance AP occupancy distribution – Predict user level performance Time stay in network Number of visited APs Network dimensioning 3 Goal

4 User Behavior: Campus Network Focus on modeling the period when network more active and heavily used A closer look of this stable period Midnight Early Morning Evening

User Behavior: Campus Network Stay Transition Depart AP i AP j Some “arrive and depart” Some “always” in the network Transitions between APs Stay times at AP Some “arrive and depart” Some “always” in the network Transitions between APs Stay times at AP AP M 5 Arrive

Model 6 AP i AP j AP k AP i AP j AP k M/G/∞ queues

Trace Mobility – User moving from AP to AP – User/AP association/disassociation messages Dartmouth Trace* – 17 weeks on Dartmouth College campus users 550+ Cisco APs – Simple Network Management Protocol (SNMP) Central controller polls each AP every 5 minutes AP replies which clients (MAC addresses) are with it – Know when a user joins network, how long he stays – Infer departure by a user’s absence in the subsequent poll *CRAWDAD archive: 7

Trace (Con’t) Interested in periods most active – Remove weekends/holidays/inter-session breaks – Stable network traffic 9 AM to 5 PM 544 APs with 5,715 distinct MAC addresses 8

Validation: User Occupancy at APs Example: The most heavily loaded AP How about other APs?

Validation: Mean Network Stay Time, #Visited APs Only 5% difference ! Only 1.4% difference ! 10

Network Dimensioning 11

Network Dimensioning -Open 12

Increase of closed population (N ) Network Dimensioning - Closed 13 Must double capacity if N  5N to maintain the same QoS

Conclusion Proposed simple queueing model of mobility – open and closed class users Validated against empirical traces Good predictions of metrics of interest – System-level % accuracy on user occupancy distribution – User-level Mean network stay time: 8 minutes difference # visited APs: 1.4% difference The model can be used for network dimensioning – Increase of arrival rate to each AP – Increase of always active population 14

References W.-j. Hsu, D. Dutta, and A. Helmy. ”Mining behavioral groups in large wireless lans,” Mobicom’07 M. Kim and D. Kotz. “Extracting a mobility model from real user traces,” Infocom’06 A. Chaintreau, P. Hui, J. Crowcroft, C. Diot, R. Gass, and J. Scott. “Impact of human mobility on the design of opportunistic forwarding algorithms,” Infocom’06 W.-j. Hsu and A. Helmy. “On nodal encounter patterns in wireless lan traces,” IEEE Transactions on Mobile Computing’10 S. Heimlicher and K. Salamatian. “Globs in the primordial soup: the emergence of connected crowds in mobile wireless networks” MobiHoc’10. Y.-C. Chen, E. Rosensweig, J. Kurose, and D. Towsley. “Group detection in mobility traces,” IWCMC’10 X. Hong, M. Gerla, G. Pei, C-C. Chiang. “A Group Mobility Model for Ad Hoc Wireless Networks,” IEEE MSWiM’99 K. H. Wang, and B. Li. “Group Mobility and Partition Prediction in Wireless Ad-Hoc Networks,” ICC’02 15

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Departure threshold – User did leave the system and returned – User was in motion, moving from AP 1 to AP 2 – Missing SNMP reports Trace Pre-Processing (Con’t) Session: start w/ first AP association; end w/ disassociating w/ all campus APs S1S1 S2S2 ∆ S’=S 1 + ∆ +S 2 <threshold 17

Multiple associations – In the same 5-minute window, more than 1 AP report a specific user is associated with it – User is in motion, moving from 1 AP to another AP(s) – Keep the last associated AP, and remove all the rest Ping-Pong effect – User associates with a fixed set of AP, one after one but only with very short amount of time – Mainly due to weak Wi-Fi signal – Hard to tell when this happens/ how many APs involved – Treat as regular transitions Trace Pre-Processing (Con’t) 18

Verifying Poisson Assumption 19

User Occupancy at APs 20