Difficulties in Simulating the Internet

1. Difficulties in Simulating the Internet Sally Floyd, Senior Member, IEEE, and Vern Paxson IEEE/ACM Transactions on Networking, August 2001 Mingyu Sun Instructor: Dr. Herman D. Hughes CSE808 Fall 2001

2. Today’s topics • Role of simulations in Internet research • Difficulties in simulating the Internet • Strategies to accommodate • Develop a common network simulator Mingyu Sun, CSE808 Presentation

3. Role of simulation • Measurement • Is needed for crucial ‘reality check’ • Challenge implicit assumptions • Experiment • For dealing with implementation issues • For understanding behavior of intractable systems • Analysis • Providing the possibility of exploring a model of the Internet • Simulation • Complementary to analysis Mingyu Sun, CSE808 Presentation

4. Measurement and experiment • What can do: • explore “real world” • the existing Internet • new environment • What cannot do: • explore future Internet • explore different possible architectures • “Success Disaster” Example: HTTP protocol used by World Wide Web Mingyu Sun, CSE808 Presentation

5. Analysis and simulation • Analysis: • Fundamental role • Complete control the model of Internet • Disadvantage: risk of losing Internet key behavior • Simulation: • Complementary to Analysis • Check correctness • Overcome the difficult, impossible cases in analysis • Develop research intuition Mingyu Sun, CSE808 Presentation

6. Simulation: dangers and pitfalls • Dynamic aspect of Internet • Picking the underlying models • Models used in Analysis and simulation • Capable, accurate simulators • Community requirement: immediate or long term • Validating simulation: make scripts public Mingyu Sun, CSE808 Presentation

7. Properties of Internet • Unify diverse networking technologies and administrative domains. • Uniform connectivity,not uniform behavior • Big Size • 99.8 million computers at the end of 2000 • Range of heterogeneity is large • Scaling problem: works fine with small number of computers, but not on large scale • Changes: drastically Mingyu Sun, CSE808 Presentation

8. USENET Traffic Volume • Bytes per day sent through the USENET bulletin board system, averaged over two-week intervals. The growth rate corresponds to exponential increase of 80% per year. Mingyu Sun, CSE808 Presentation

9. LBNL Traffic Volume • Internet connections per day at LBNL. The growth rate corresponds to exponential increase of 52% per year. • LBNL- Lawrence Berkeley National Laboratory Mingyu Sun, CSE808 Presentation

10. Growth of LBNL’s WWW Traffic • World Wide Web (HTTP) connections per month at LBNL.The growth rate corresponds to doubling every 7- 8 weeks. Mingyu Sun, CSE808 Presentation

11. Heterogeneity -- Topology and link • Hard to characterize the protocol over a range of topologies • Constantly change, secret topological information • Topology generator based on current Internet • Large-scale nature of network topology and protocols • Modems Vs. fiber optic links • Wire (Traverse copper, glass wire) Vs.Wireless (radio, infrared-based) • Point-to-point Vs. broadcast links (multi-access) • Land-based Vs. satellite links( geosynchronous orbit, low-earth orbit) • Dynamic routing (latency, bandwidth, load of paths) • Routing asymmetric (large topology, scaling) Mingyu Sun, CSE808 Presentation

12. Heterogeneity -- Protocol • Conceptually, the Internet uses a unified set of protocols • In reality, each protocol has been implemented differently. • Some studies are sensitive to the details of the protocol • different user, different features, even bugs • Example: TCP 400 implementation and versions.TCP Tahoe and Reno replaced by NewReno and SACK • One must decide which applications to simulate using those protocols • Different applications have major different characteristics • These characteristics can vary considerably from site to site Mingyu Sun, CSE808 Presentation

13. Heterogeneity -- Traffic • Basic question: • How to introduce different traffic sources into the simulation, while retaining the end-to-end congestion control • Trace-driven • Problem: Rate adaptation from end-to-end congestion control causes shaping • Example: a connection observed on a high-speed unloaded link might still send packages at a rate much lower than what the link could sustain because somewhere else along the path insufficient resources are available. • Solution: Trace-driven source-level simulation preferable to trace-driven packet-level because data volume and the application-level pattern are NOT shaped by the network’s current property Mingyu Sun, CSE808 Presentation

14. Problems with source-level trace-driven • NOT all sources can be reliably characterized by traffic traces • Example: remote login users terminate or justify commands • Applications that are inherently adaptive (Internet Video) will be shaped at both packet-level and application level. • Complexity of traffic congestion level to the network link • From all degrees of congestion to none at all • Predicting future evolution of congestion harder than characterizing certain level of congestion at a particular time • Scaling issue arises using source models of individual connections to generate aggregated cross-traffic for simulation • Expensive to simulate a traffic aggregate on a per source basis Mingyu Sun, CSE808 Presentation

15. Rapid and unpredictable change • Possibilities for unpredictable areas of change: • Pricing structures • Scheduling • Wireless • Impoverished devices • Native multicast • Differentiated service • Ubiquitous web-caching • a new “killer app” • Implications: • Our research should not be heavily biased by network details that are likely to change. • Our research should not be invalidated by major architectural changes that might or might not come to dominate the Internet architecture several years down the road. Mingyu Sun, CSE808 Presentation

16. Coping strategies (I) Search for invariants • What is invariant? • Some facet of behavior that has been empirically shown to hold in a very wide range of environments (traffic characteristics, call arrival processes, session durations…) • Promising candidates: • Diurnal patterns of activity • Different patterns for different protocols, NNTP • Different patterns for the same protocols, work/leisure surf • Geographic effects on across time zone communication • Self- similarity • Poisson session arrivals • Log-normal connection sizes • Heavy-tailed distributions • Invariant distribution for Telnet packet • Invariant characteristics of the global topology Mingyu Sun, CSE808 Presentation

17. Coping strategies (II) Exploring the parameter space • Explore network behavior as a function of changing parameters • Parameters include protocol specifics, router queue management, packet scheduling, network topologies and link properties, or traffic mixes. • Rule of thumb: Order of magnitude in parameter • Pitfall: Internet includes nonlinear feedback mechanisms that may cause mistakes made by simulation artifacts not present in the real world. • Correct interpretation of simulation result Mingyu Sun, CSE808 Presentation

18. NS simulator • What is NS? • NS is a multiprotocol simulator that implements unicast and multicast routing algorithms, transport and session protocols (including both reliable and unreliable multicast protocols), reservations and integrated services. • NS incorporates a range of link-layer topologies and scheduling and queue management algorithms. • NS also incorporates libraries of network topology generators and traffic generators, the network animator NAM, an emulation interface to allow the NS simulator to interact with real-world traffic. • Advantage: • Ability to build upon the work of others in sharing a common simulator • Disadvantages: • affected by the same bugs or the same modeling assumptions that subtly skew the results. • No simulator eliminates the difficulties inherent in Internet simulation • Trends in network simulation: Parallel and distributed simulators Mingyu Sun, CSE808 Presentation

19. Final notes • Discussed issues & difficulties in modeling Internet traffic, topology and protocols • Acknowledged limitations & potential of simulations and model-based research • Interpreting simulation results and drawing conclusions • Besides intuition and good judgment, rely on other tools (measurements, experiments and analysis) Thank you! Mingyu Sun, CSE808 Presentation