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

Nov. 12, 2001Mingyu Sun, CSE808 Presentation 2 Today’s topics Role of simulations in Internet research Difficulties in simulating the Internet Strategies to accommodate Develop a common network simulator

Nov. 12, 2001Mingyu 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

Nov. 12, 2001Mingyu 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

Nov. 12, 2001Mingyu 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

Nov. 12, 2001Mingyu 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

Nov. 12, 2001Mingyu 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

Nov. 12, 2001Mingyu 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.

Nov. 12, 2001Mingyu 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

Nov. 12, 2001Mingyu 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.

Nov. 12, 2001Mingyu 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)

Nov. 12, 2001Mingyu 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

Nov. 12, 2001Mingyu 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

Nov. 12, 2001Mingyu 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

Nov. 12, 2001Mingyu 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.

Nov. 12, 2001Mingyu 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

Nov. 12, 2001Mingyu 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

Nov. 12, 2001Mingyu 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

Nov. 12, 2001Mingyu 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!