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Measuring the Congestion Responsiveness of Internet Traffic Ravi Prasad & Constantine Dovrolis Networking and Telecommunications Group College of Computing.

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Presentation on theme: "Measuring the Congestion Responsiveness of Internet Traffic Ravi Prasad & Constantine Dovrolis Networking and Telecommunications Group College of Computing."— Presentation transcript:

1 Measuring the Congestion Responsiveness of Internet Traffic Ravi Prasad & Constantine Dovrolis Networking and Telecommunications Group College of Computing Georgia Tech

2 Outline  Motivation  Session arrival models Closed-loop Open-loop  Congestion Responsiveness Metric Closed-loop Traffic Ratio (CTR)  CTR Measurements Methodology Results  Summary

3 Congestion Responsiveness  Congestion Responsive Traffic: Reduces the offered load in the event of congestion  Conventional wisdom: the Internet traffic is congestion responsive due to TCP TCP carries more than 90% of Internet traffic TCP reduces offered load (send window) upon sign of congestion Negative-feedback loop, stabilizing queuing system Key modeling unit: persistent flows (they last forever!)  Most Internet flows are non-persistent  Is an aggregate of non-persistent TCP flows congestion responsive?

4 Flows are generated by users/applications, not by the transport layer!  Examples: user clicks web page, p2p transfers, machine- generated periodic FS synchronization  Session: Set of finite (i.e., non- persistent) flows, generated by single user action  Key issue: session arrival process  Does the session arrival rate reduce during congestion? ReceiverSender Transport Application Response Request Network

5 Two session arrival models  Closed-loop model Fixed number of users, each user can generate one session at a time New session arrival: depends on completion of previous session E.g., ingress traffic in campus network  Open-loop model Sessions arrive in network independently of congestion Theoretically, infinite population of users E.g., egress traffic at popular Web server 1 2 3 N

6 Closed-loop model  N users: cycles of transfer and idle periods S : Average session size T T : Average transfer duration T I : Average idle time N a : Number of active sessions  Congestion responsive Congestion increases T T : reduces offered load R offered

7 Open-loop model  Poisson session arrivals S : Average session size : Session arrival rate Stable only if  <1  Congestion unresponsive Offered load R offered independent of congestion

8 Mixed Traffic  Internet traffic: mix of open-loop and closed-loop traffic  Mixed traffic can be characterized by Closed-loop Traffic Ratio ( CTR )

9 Measuring Congestion Responsiveness  Direct congestion responsiveness measurements difficult Require highly intrusive experiments to cause congestion Require access at bottleneck link  Alternative: Measure CTR (Closed-loop Traffic Ratio) Indirect metric for congestion responsiveness High CTR: more congestion responsive Low CTR: less congestion responsive

10 CTR estimation (overview)  Start with packet trace from Internet link Per-packet: arrival time, src/dst address & ports, size Focus only on TCP traffic: HTTP and well-known ports  Identify users: Downloads: user is associated with unique DST address Uploads: user is associated with unique SRC address  For each user, identify sessions: Session: one or more connections (“transfers”) associated with same user action  E.g., Web page download: multiple HTTP connections  Classify sessions as open-loop or closed-loop: Successive sessions from same user: closed-loop Session from a new user, or session arriving from known user after a long idle period: open-loop

11 From Connections to Transfers  An HTTP 1.1 connection can stay alive across multiple sessions  Transfer : Segment of TCP connection that belongs to a single session  Intra-transfer packet interarrivals: TCP and network-dependent (short)  Inter-transfer packet interarrivals: caused by user actions (long)  Classify interarrivals based on Silence Threshold (STH) 1105126179.423931 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.478309 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.478438 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.478554 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.488433 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.488666 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.488918 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.539748 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.539870 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.539993 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.549085 163.157.239.61 127.207.1.255 80 2290 154 T 114 1105126179.549399 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.611572 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.611702 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.612235 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.612507 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.612752 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.613121 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.672432 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 Inter transfer gap Intra transfer gap

12 Silence Threshold (STH) estimation Inter tranfer gap Intra transfer gap  STH=40sec

13 Group transfers from same user in sessions  Intuition: transfers from same session will have short interarrivals (machine-generated)  Minimum Session Interarrival (MSI) threshold  MSI aims to distinguish machine-generated from user-initiated events MSI = 1-5 seconds 1105126179.423931 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.478309 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.478438 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.478554 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.488433 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.488666 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.488918 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.539748 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.539870 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.539993 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.549085 163.157.239.61 127.207.1.255 80 2290 154 T 114 1105126179.549399 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.611572 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.611702 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.612235 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.612507 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.612752 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.613121 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.672432 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 Inter transfer gap Intra transfer gap <MSI >MSI session 1 session 2 session 3

14 Classify sessions as open/closed-loop  First session from a user is always open-loop  Session from a returning user is also open-loop, if it starts Before last session finish, or Long time after completion of last session  Long time = MTT: Maximum Think Time 1105126179.423931 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.478309 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.478438 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.478554 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.488433 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.488666 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.488918 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.539748 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.539870 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.539993 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.549085 163.157.239.61 127.207.1.255 80 2290 154 T 114 1105126179.549399 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.611572 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.611702 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.612235 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.612507 163.157.239.61 127.207.1.255 80 2289 1420 T 1380 1105126179.612752 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.613121 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 1105126179.672432 163.157.239.61 127.207.1.255 80 2290 1420 T 1380 Inter transfer gap Intra transfer gap <MSI>MSI session 1 Open session 2 Open session 3 Close > MTT < MTT

15 Robustness to MSI & MTT thresholds  Examined CTR variation in the following ranges: Minimum Session Interarrival (MSI): 0.5sec-2sec Maximum Think Time (MTT) : 5min-25min  CTR variation < 0.1  Linear regression: CTR/MSI = 0.0232/sec CTR/MTT = 0.0020/min  We use: MSI=1sec. MTT=15min.

16 Sample CTR measurements Link locationYearDirectionDurationTCPWell-known ports GB(%)Bytes(%)CTR Georgia Tech. 05In2Hr.129(97)63.50.71 Out2Hr.208(99)47.90.57 Los Nettos04Core1Hr.59(95)65.60.77 UNC, Chapel Hill 03In1Hr.41(87)26.60.76 Out1Hr.153(97)35.80.61 Abilene, Indianapolis 02Core1Hr.172(96)41.90.70 Core1Hr.178(85)47.30.64 Univ. of Auckland, NZ 01In6Hr.0.6(95)73.00.73 Out6Hr.1.4(98)78.00.67

17 Summary  TCP or TCP-like protocols are necessary but not sufficient for a congestion responsive aggregate  Show importance of arrival process for non- persistent transfers Focus on open-loop and closed-loop models Closed-loop Traffic Ratio (CTR) used to characterize traffic in a given link  Measurements show CTR values of 60-80% for most Internet links we examined Session level feedback could be making internet traffic congestion responsive

18 Thank you!

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