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Internet Traffic Characterization

Internet Traffic Characterization. Amogh Dhamdhere. What is covered in this talk…. Why characterize Internet traffic ? Measurement and analysis methodologies. Measurement studies. Variation of Internet traffic (time of day, day of week effects) Packet level characteristics (packet sizes).

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Internet Traffic Characterization

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  1. Internet Traffic Characterization Amogh Dhamdhere Amogh Dhamdhere

  2. What is covered in this talk… • Why characterize Internet traffic ? • Measurement and analysis methodologies. • Measurement studies. • Variation of Internet traffic (time of day, day of week effects) • Packet level characteristics (packet sizes). • Flow level characteristics (Flow sizes, flow durations). • File size distributions. • Distribution by application. • Distribution by protocol. Amogh Dhamdhere

  3. What is not covered… • Everything that will be covered in future presentations !! • Delay and loss measurements • TCP related measurements (TCP flavors etc) • Self similarity of Internet traffic • Flow measurements • Peer to peer traffic measurements Amogh Dhamdhere

  4. Goals of this research.. • Observe Internet traffic characteristics. • Develop reasonable models to understand these characteristics. • Failure of traditional mathematical modeling techniques (e.g. Queueing theory). • Earlier models deal with issues which are non-critical from the practitioner’s point of view. • Attempt to close the void between theory and practice. Amogh Dhamdhere

  5. Why Characterize Internet Traffic ? • Provisioning network resources (capacity, buffer, etc) • How should the network be provisioned to satisfy certain constraints. • Constraints may differ with the type of traffic. • E.g. Buffer provisioning • Current tools (eg SNMP) may not be sufficient • Analyzing network performance • TCP performance • Routing performance Amogh Dhamdhere

  6. Why Characterize Internet Traffic ? • Obtain characteristic workloads for use in simulations • Typical packet sizes • Typical flow durations • Most commonly used TCP flavors • Important for ISPs to formulate policy decisions (Service Level Agreements) • Developing techniques to detect network anomalies e.g. Denial of Service attacks. • Verify ‘rule of thumb’ type design guidelines. Amogh Dhamdhere

  7. Measurement Methodologies Objectives of a monitor: • Collection of detailed traffic statistics from heterogeneous network links. • Non-interference with the measured network (non-intrusiveness). • Obtaining a global view of the monitored network from a reasonable number of monitoring points. Types of monitor: • Active monitors • Passive monitors Amogh Dhamdhere

  8. IPMON (Sprint) • Passive monitor for the Sprint backbone network. • Capable of monitoring links of capacities ranging from OC-3 to OC-48. • Uses an optical splitter on the monitored link. • Records packet traces including IP and TCP/UDP headers, timestamp. • Trace sanitizer. • Analysis component: • Flow statistics (start and end time of flows, flow sizes) • Protocol (TCP, UDP) and application (web, email, streaming) split of traffic. Amogh Dhamdhere

  9. IPMON Amogh Dhamdhere

  10. Other Projects • OC3MON (MCI) - Passive monitor designed for OC3 links (155 Mbps). • NetScope (AT&T) - A set of tools for traffic engineering in IP backbone networks. • Network Analysis Infrastructure (NAI) - Performance of vBNS (very high speed Backbone Network Service) and Abilene networks. • Some routers have built-in monitoring capabilities. • Netflow – Cisco routers. • Commercial tools • Niksun’s NetDetector and NikScout’s ATM Probes. Amogh Dhamdhere

  11. Measurement Studies Wide Area Internet Traffic Patterns and Characteristics – Thompson, Miller, Wilder, MCI Telecommunications, 1997. • One of the first studies of commercial backbone traffic. • Used the OC3MON traffic monitor described earlier, at two locations on MCI’s commercial backbone. • Characterize traffic on timescales of 24hrs and 7 days in terms of traffic volume, flow volume, flow duration, packet sizes, traffic composition (by protocol, application). • Two links monitored. Domestic and International. Amogh Dhamdhere

  12. MCI Study – Daily and weekly effects • Traffic volume shows a clear diurnal pattern, with traffic tripling from 06:00 through 12:00 noon EDT. • Traffic decreases by about 25% during the weekend. • The two directions of the monitored link are not symmetric. Amogh Dhamdhere

  13. MCI Study – Asymmetry in packet sizes • Packet sizes are different in the two directions, and are roughly inversely proportional to each other. Amogh Dhamdhere

  14. MCI Study – Packet size distributions • Packet size distributions are trimodal. • 40-44 bytes - TCP ACKs, control segments etc. • 552 or 576 bytes - Default MSS when MTU Discovery is not used is 512 or 536 bytes. • 1500 bytes MTU for Ethernet. Amogh Dhamdhere

  15. MCI Study – International Link Traffic • International link traffic shows similar time of day, day of week effects. • Packet sizes in the two directions are asymmetric – Larger packets in the U.S. to U.K. direction. Amogh Dhamdhere

  16. MCI Study – Protocol and Application Mix • Protocol composition • TCP dominates (95% of bytes, 90% packets, 75% flows) • UDP second (5% bytes, 10% packets, 20% flows) • ICMP most of the remaining. • Application composition • Web (75% bytes, 70% packets, 75% flows) • Other (may also be web-related) • DNS (1% bytes, 3% packets, 18%) • SMTP (5% bytes, 5% packets, 2% flows) • FTP (5% bytes, 3% packets, <1% flows) • NNTP (2% bytes, <1% packets, <1% flows) • Telnet (<1% bytes, 1% packets, <1% flows) Amogh Dhamdhere

  17. Measurement Studies Trends in Wide Area IP Traffic Patterns – McReary, Claffy, CAIDA, 2000. • Data collected by the NAI project from May 1999 through March 2000 at the NASA Ames Internet Exchange. • Analysis of packet size distributions, protocol/application mix etc. • Show increasing trends in traffic from new (at that time) applications e.g. streaming media, online games, Peer to Peer (Napster). • No change in the overall trend in the TCP/UDP traffic ratio as compared to the analyses at MCI and CAIDA in 1998. Amogh Dhamdhere

  18. CAIDA Study – Packet Size Distributions • Packet size distributions show same trimodal trend as previous results. Amogh Dhamdhere

  19. CAIDA Study – Protocol and Application Mix • Protocol mix • TCP and UDP are still the most popular protocols, and in roughly the same proportions. • Application mix (TCP) • Web is still the most popular application • New applications like peer to peer file sharing (Napster) now appear in the list. (Napster at 5th position) • Application mix (UDP) • Streaming media (RealAudio) now comprises a substantial portion of total UDP traffic. • Online games (Half Life, EverQuest, Unreal, Quake 3) also have substantial share. Amogh Dhamdhere

  20. CAIDA Study – Long Term Trends • The protocol mix of the traffic (TCP and UDP) does not change significantly over time. • Decline in the contribution of FTP to the overall traffic mix. • Possibly due to shift from active to passive mode FTP, because of an increase in packet filtering firewalls. • Alternate protocols for file transfer. • Decline in the fraction of RealAudio traffic. • RealAudio traffic has remained fairly constant, while other traffic has increased. • Decline in the fraction of game traffic Amogh Dhamdhere

  21. CAIDA Study – Long Term Trends • Significant increase in peer to peer traffic (Napster) Amogh Dhamdhere

  22. CAIDA Study – Short Term Trends • Email traffic increased significantly in November and early December, decreasing after December holidays. Amogh Dhamdhere

  23. CAIDA Study – Short Term Trends • Online gaming shows day of week effects, with traffic nearly doubling over weekend periods. Amogh Dhamdhere

  24. Measurement Studies Longitudinal study of Internet traffic from 1998-2001 – Fomenkov, Keys, Moore, Claffy, CAIDA, 2001. • Unique long term view of Internet traffic. • Multiple observation sites (20) • Four metrics of measured traffic • Number of bytes. • Number of packets. • Number of flows. • Number of source-destination pairs (port number and protocol fields ignored). This measures the number of Internet hosts communicating via the monitored link. Amogh Dhamdhere

  25. Longitudinal Study • Bit and packet rates show diverse behavior • Some sites show sustained growth, some are constant and some fluctuate between growth and reduction. • No clear diurnal pattern in the measured traffic ! • No consistent long term growth – Refutes the notion that Internet traffic ic universally and rapidly increasing. • Usage patterns • Traffic composition varies significantly from site to site. • WWW traffic reached maximum between late 1999 and early 2000. • Has been constant or decreased since. • This could be due to the onset of noticeable amounts of P2P traffic. Amogh Dhamdhere

  26. Longitudinal Study – Application Mix Amogh Dhamdhere

  27. Measurement Studies Packet Level Traffic Measurements from the Sprint IP Backbone – Fraleigh, Moon, Lyles, et al. Sprint Labs, 2003 • Most recent (2001-2002) study of traffic on a commercial backbone link. • Analyses the impact of new applications (distributed file sharing, streaming media) • New results for end-to-end loss and delay performance of TCP connections. • Measurements of network delays in the backbone and U.S. transcontinental links. • Methodology – Uses the IPMON architecture described earlier. Amogh Dhamdhere

  28. SPRINT Study – Traffic Load • Traffic load in bytes • SNMP is not able to capture the burstiness of the traffic at smaller timescales. • Most backbone links are utilized under 50%. Less than 10% of the backbone links experience utilization higher than 50% in any 5 min interval. • Noticeable peaks in traffic load are observed due to DoS attacks. • Traffic in a bidirectional link is asymmetric. • Many applications are inherently asymmetric. • Hot potato routing. Amogh Dhamdhere

  29. SPRINT Study SNMP is not able to capture the burstiness of the traffic at smaller timescales. Amogh Dhamdhere

  30. SPRINT Study – Application Mix • Application mix varies from link to link. • In most cases, web represents more than 40% of total traffic (As seen in previous studies) • However, on some links, the web contributes less than 20%, while P2P accounts for 80%. • Streaming applications are a stable component of the traffic. Amogh Dhamdhere

  31. SPRINT Study - Flows • The number of flows and the traffic load are not necessarily correlated. i.e a large number of flows does not always mean a large traffic load. Amogh Dhamdhere

  32. Measurement Studies – Flow level Understanding Internet Traffic Streams: Dragonflies and Tortoises – Brownlee, Claffy – CAIDA. • Results of flow level measurements from two links: OC3 link (Auckland) and OC12 link (UCSD) • Uses an extension of NeTraMet to monitor stream lifetimes. • Previous classifications of flows were on basis of size (packets or bytes) • Elephants (large transfers) • Mice (short transfers) • Propose alternate classification of TCP flows on basis of their lifetime. • Tortoises (long lasting transfers) • Dragonflies (short duration transfers) • Here flows are defined as sets of packets traveling in either direction between a pair of end-points. Amogh Dhamdhere

  33. Dragonflies and Tortoises • Percentages of streams and bytes. • Long Running (LR) streams (>15 mins) account for about 1% of the streams. • Very Short streams (<2 sec) account for 40 – 70 % of streams, showing a diurnal pattern of variation. • At UCSD site, 50% of all bytes were in LR streams, while this fraction was 5% for Auckland. Most of these streams are non-web traffic. Amogh Dhamdhere

  34. Short Streams – Streams lasting less than 15 mins • Lifetime distributions • 45% of streams have lifetimes less than 2 sec. • Distributions do not change rapidly over time. Amogh Dhamdhere

  35. Short Streams – Streams lasting less than 15 mins • Byte size distributions • Short stream size distributions for UDP, non-web TCP and web TCP are considerably different. • Distributions are stable over long periods of time Amogh Dhamdhere

  36. Tortoises – Streams lasting more than 15 mins • Bit rates • Longer duration LR streams are low-rate (interactive) or high rate (multimedia) with approximately equal frequency. • Medium duration LR streams tend to be high-rate. (file transfers) • UDP streams run at constant bit rates, but these rates may change in response to the application’s state (online games). Amogh Dhamdhere

  37. Tortoises – Streams lasting more than 15 mins • LR stream lifetimes • LR stream lifetimes seem to follow a power law distribution. Amogh Dhamdhere

  38. Measurement Studies – Flow level Internet Stream Size Distributions – Brownlee, Claffy, CAIDA 2002. • Measurements of • Per minute distributions of stream sizes in bytes for a period of one hour. • Two different types of traffic considered: Web traffic, and non-web TCP traffic. • Web streams • 87% under 1kB, 8% between 1 and 10 kB, 4.8% between 10 and 100 kB. • Non-web streams • 89% under 1kB, 7% between 1 and 10 kB, 1.5% between 10 and 100 kB. Amogh Dhamdhere

  39. Internet Stream Size Distributions Amogh Dhamdhere

  40. File Size Distributions The Structural cause of file size distributions – Downey, 2001. • A new model for the operations that create new files. • Files appear because of common operations. • Copying. • Translating and filtering. • Editing. • Using this, the distribution of file sizes can be predicted to be lognormal. • Start with a single file of size s*. • Select a file size s at random from the current distribution. • Create a new file with size fs and add to the distribution. (f is a factor chosen from some other distribution. • Hence size of nth file is sn = s* · f1 · f2 · f3…..fm • log(sn) = log(s*) + log(f1) + …. Amogh Dhamdhere

  41. File Size Distributions • File sizes on web servers • Studies by Arlitt and Williamson claim file size match the Pareto model. • This may not be true !! • Some of the analyzed data sets better fit the lognormal model. • Traces of downloaded files. • Fits a hybrid model with lognormal distribution with a Pareto tail. • Two mode lognormal model is also a good match. • Summary – The distribution of file sizes is NOT heavy tailed ! • Implications on self-similarity of Internet traffic • Most explanations assume that distribution of file sizes is long-tailed. • Need to revise explanations of self-similarity. Amogh Dhamdhere

  42. Non-commercial networks Some results from the abilene network during the duration of one week. • Application mix • Web traffic is much lower as compared to commercial backbone networks. • Email traffic is higher. • Measurement traffic amounts to 5% of all traffic !! • Protocol mix • TCP is still the most dominant (90% of bytes). • UDP accounts for 5%. • ICMP around 4%. • Numbers similar to that on commercial backbone links. Amogh Dhamdhere

  43. Future Directions • Self-similarity – The need to verify assumptions. • Downey questioned the assumptions about file size distributions. • Inter-arrival time distributions. • Transfer length distributions. • Burst size distributions. • Dependence of traffic characteristics on TCP algorithms. • Measurement based forecasting of DoS attacks and flash crowds. • Real time monitoring of critical parameters. Use this characterization to automatically make decisions. • Provisioning. • Routing etc. Amogh Dhamdhere

  44. Future Directions • Characterization of P2P traffic. • Previous measurement studies on P2P systems focused on node behavior, topology etc. • Need to better characterize the traffic generated by P2P applications. Amogh Dhamdhere

  45. Thank You ! Amogh Dhamdhere

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