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1 Simulating the Internet: challenges & methods Kevin Fall Network Research Group, Lawrence Berkeley National Laboratory Berkeley, CA USA.

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Presentation on theme: "1 Simulating the Internet: challenges & methods Kevin Fall Network Research Group, Lawrence Berkeley National Laboratory Berkeley, CA USA."— Presentation transcript:

1 1 Simulating the Internet: challenges & methods Kevin Fall Network Research Group, Lawrence Berkeley National Laboratory Berkeley, CA USA

2 2 LBNLs Network Research Group: Members: Van Jacobson, group leader Kevin Fall Sally Floyd * Craig Leres Vern Paxson *

3 3 Outline Simulating the Internet is not easy The VINT project: an effort in Internet-style simulation

4 4 Simulations for Network Research Models of interesting behavior Easily-varied parameters Controlled environment, reproducible results

5 5 Problems in Characterizing the Internet Large Scale:Large Scale: –even a small fraction of misbehaving entities is non- negligible –scale stresses assumptions in protocol design and implementation Drastic Change:Drastic Change: –will the rate of change continue? –predominant use not obvious (e.g. the web, continuous media, ?) Heterogeneity everywhere!

6 6 Link and Topology Heterogeneity Delay and bandwidth span 5 to 6 orders of magnitude! –20 sec to 2s round-trip prop delay –10Kb/s to 10Gb/s bandwidth range Topology –hierarchy and clustering chosen by ISPs –performance tied to which path packets take in network –paths may change dynamically –IP routes are frequently asymmetric

7 7 Protocol Heterogeneity Adaptive and non-adaptive Internet protocols –react to congestion (TCP) –nonreactive (UDP) Application Dynamics –multi-protocol interactions –user activity –application mix varies greatly by site Implementations may not be consistent

8 8 Traffic Internet traffic not easily characterized –no commonly accepted model –traffic may be shaped by congestion response Dependent on source behavior –application protocol limitations –new applications –pricing policies

9 9 So, what can be done in simulation? StrategyStrategy –1: Look for invariants –2: Explore the parameter space –3: Understand the limits of simulation

10 10 1: Searching for Invariants What do we really know about Internet dynamics ? How to characterize statistically? –traffic –users –sessions –congestion, etc. Mathematical simplicity does not imply accuracy

11 11 The Self-Similar Nature of Traffic packet arrivals not exponentially distributed –thus, arrival process is not Poisson –bursts over multiple time-scales –they exhibit long-range dependence –suggests self-similar models –(there is still contention on this point) Implications –aggregation does not smooth out variation –traffic synthesis more difficult –network buffering may be much less effective than thought based on Markovian models

12 12 User-generated Sessions look Poisson user-generated session arrivals look Poisson (machine-generated connection arrivals are not) distribution is invariant, parameterized only by a (fixed, hourly) rate

13 13 Network Activity tends to have a heavy- tailed distribution Examples: packets in a users TELNET session; bytes in FTP-DATA transfers distribution looks Pareto with 0.9 < Pareto distribution with shape has: –infinite mean if –infinite variance if This type of Pareto has infinite mean and variance (and is very unlike an exponential) burstiness remains across aggregation

14 14 2: Exploring the Parameter Space Consider a large range for parameters –recall, 5-6 orders of magnitude range in bandwidth and delay –note that behavior is often non-linear in parameter values Repeat, repeat, repeat –topology generators –randomness

15 15 3: The Limits of Simulation Simplified Models –useful for gaining intuition and exploring parameters –danger of oversimplification Need for a Reality Check –compare simulation results with measurement –Internet measurements often offer surprises

16 16 USC/ISI: Deborah Estrin, Mark Handley, John Heideman, Ahmed Helmy, Polly Huang, Satish Kumar, Kannan Varadhan, Daniel Zappala LBNL: Kevin Fall, Sally Floyd UCBerkeley: Elan Amir, Steven McCanne Xerox PARC: Lee Breslau, Scott Shenker VINT is currently funded by DARPA through mid The VINT Project (Virtual InterNet Testbed)

17 17 VINT Goals provide common platform for network research explore issues of scale and multi-protocol interaction Specific Areas:Specific Areas: –multicast, end-to-end transport –simulation scaling –traffic management –emulation

18 18 Multicast Research Reliable Multicast Transport –Large Scale –SRM-- Scalable Reliable Multicast Multicast Congestion Management –Group formation –(still ongoing) Layered Transmission –layered encoding –dynamic multi-group join/leave

19 19 Simulation Scaling Simulator capable of 1000s of nodes Want 100,000s of nodes (or more) Session Abstraction –abstract away some simulation details –trade detail for time/space –scales simulation by about 10X

20 20 Traffic Management Active Buffer Management –Random Early Detection Gateways –Explicit Congestion Notification (ECN) Packet Scheduling –Class-Based Queuing (CBQ) –Round-Robin and Fair Queuing Variants Differentiated Services –Admission Control –Reservation Support

21 21 Emulation Interface Simulator with Live Network Live Traffic Passes through Simulated Topology Special Real-Time Scheduler –may not keep synchronized under load

22 22 The VINT Simulation Environment ns2 namComponents: ns2 and nam NS2 (network simulator, version 2): –Discrete-event C++ simulation engine scheduling, timers, packets –Split Otcl/C++ object library protocol agents, links, nodes, classifiers, routing, error generators, traces, queuing, math support (random variables, integrals, etc) Nam (network animator) –Tcl/Tk application for animating simulator traces available on UNIX and Windows 95/NT

23 23 NS Supported Components Protocols : –TCP (2modes + variants),UDP, IP, RTP/RTCP, SRM, MAC, MAC Routing –global topology map, classifiers –static unicast, dynamic unicast (distance-vector), multicast Queuing and packet scheduling –FIFO/drop-tail, RED, CBQ, WRR, DRR, SFQ Topology: nodes, links Failures: link errors/failures Emulation: interface to a live network

24 24 TCP Animation

25 25 SRM Animation

26 26 Benefits Common simulation environment –simulations expressed in scripting language –separate visualization tool –topology and scenario generators –modular structure is extensible; sources provided Unique Features –Rich Protocol Set –Session abstraction provides scaling simulations by a factor of 4 –Visualization and Emulation capabilities separate Network Animator (nam) tool low-level interface to systems protocols

27 27 The NS Architecture Simulator is a Object-Tcl shell Split Objects –fine-grain, easily composed –objects exist both in C++ and Tcl Context –library handles object consistency

28 28 Work in Progress Adaptive Web Caching (LBNL, UCLA) Nam Improvements (USC, ISI) Simulator Scaling (USC, ISI) Simulator Addressing Hierarchy (USC, ISI) Protocol Robustness (USC, ISI) Emulation (LBNL, UCB) Quality of Service (Xerox PARC) Router-Based Congestion Control (LBNL) Topology and Scenario Generation

29 29 Router-Based Congestion Control Two main classes of traffic on Internet: –TCP (reduces sending rate in face of loss) –UDP (application decides when and how much to send) Internet stability due in large part to TCPs congestion response Danger with growing use of UDP-based applications –UDP will steal bandwidth from TCP –currently no incentives to prevent this behavior

30 30 Encouraging Congestion Control Combine RED Gateway with analysis and regulation RED (Random Early Detection) Gateways : –keep smoothed average queue size measure –when measure exceeds threshold, drop or mark packets with increasing probability –a flows fraction of the aggregate random packet drop rate is roughly equal to its fraction of the aggregate arrival rate Select candidate bad flows with high drop rate

31 31 Bad Flow Selection Criteria Flow is not TCP-friendly –throughput exceeds factor times analytic model: Flow is not responsive –does not alter arrival rate with increased packet drops Flow is high-bandwidth –uses more than its fair share

32 32 Flow Regulation Need bandwidth-regulating packet scheduler –CBQ –others Use good and bad scheduling partitions Bad partition gets allocation below current usage –decays over time with continued offered load –flows may be reclassified as ok if they adapt

33 33 Conclusion Simulating the Internet is difficult Simulation is useful, but must be used carefully The VINT project a common simulation framework that addresses many of the issues

34 34 Additional Information Web pages: – – – – NS Users Mailing list: subscribe ns-users

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