1. Scalability and Accuracy in a Large- Scale Network Emulator Amin Vahdat, Ken Yocum, Kevin Walsh, Priya Mahadevan, Dejan Kostic, Jeff Chase, and David Becker Presented by Stacy Patterson

2. Outline Motivation ModelNet Design Evaluation Conclusion

3. Motivation Need a way to test large-scale Internet services Peer-to-peer, overlay networks, wide area replication Testing in the real world Results not reproducible or predictable Difficult to deploy and administer research software Simulation tools Allows control over test environment May miss important system interactions Emulation Network emulators can subject application traffic to end- to-end bandwidth constraints, latency, and loss rate of user specified topology Previous implementations not scalable

4. ModelNet A scalable, cluster-based, comprehensive network emulation environment

5. Design User run configurable number of instances of application on Edge Nodes within dedicated server cluster Each instance is a Virtual Edge Node (VN) Each VN has a unique IP address Edge nodes route traffic through cluster of Core Routers Equipped with large memories and modified FreeBSD kernels Can emulate configured target network traffic Core routers route traffic through emulated links or “pipes” each with its own packet queue and queuing discipline

6. ModelNet Phases  (1) Create Generates a network topology in GML - Graph with vertices: clients, stubs, transits and edges: network links Can be generated from Internet traces, BGP dumps, synthetic topology generators, etc. Users can annotate graph with packet loss rates, failure distributions, etc (2) Distillation Transforms GMLgraph into pipe topology

7. ModelNet Phases (3) Assignment Maps pipe topology to core nodes, distributing emulation load across core nodes Finding ideal mapping is NP-complete problem. Depends on routing, link properties and traffic load. ModelNet uses greedy k-clusters assignment For k core nodes, randomly select k nodes in distilled topology. Greedily select links from connected component in round robin.

8. ModelNet Phases (4) Binding Multiplex multiple VNs to each physical edge nodes Bind each physical edge node to a core router Generate shortest path routes between all VNs and install in core routing tables (5) Run Executes target application code on edge nodes

9. Inside the Core Route traffic through emulated “pipes” Each route is an ordered list of pipes Packets move through pipes by reference Routing table requires O(n 2 ) space Packet Scheduling When packet arrives, put at tail of first pipe in its route. Scheduler stores heap of pipes sorted by earliest deadline - exit time for first packet in its queue Once every clock tick Traverse pipes in heap for packets that are ready to exit Move packets to tail of next pipe or schedule for delivery Calculate new deadlines Multi-core Configuration Next pipe in route may be on different machine If so, core node tunnels packet descriptor to next node

10. Scalability Issues Traffic traversing core is limited by cluster’s physical internal bandwidth ModelNet must buffer up to full bandwidth- delay product of target network. 250 MB of packet buffer space to carry flows at aggregate bandwidth of 10 GB/s with 200 ms roundtrip latency. Assumes perfect routing protocol

11. Evaluation Core routers - 1.4 Ghz Pentium III, 1 GB memory Connected using 1 GB switch Edge nodes - 1 Ghz Pentium III 256 MB memory Connected using 100 MB/s

12. Baseline Accuracy Want to insure that under load, packets are subject to correct end-to-end delays Used kernel logging to track ModelNet performance and accuracy Results show that by running ModelNet scheduler at highest kernel priority Packets are delivered within 1ms of target end-to- end value Accuracy is maintained up to 100% CPU usage

13. Capacity Quantify capacity of ModelNet as function of load and number of emulated hops Tested 1-5 edge nodes Each edge node hosts up to 24 netperf senders and 24 netperf receivers Topology connects each sender to a receiver

14. Capacity

15. Scalability Additional Cores Adding core routers allows ModelNet to deliver higher throughput Communication between core routers introduces overhead. Higher cross-core communication results in less throughput benefit VN Multiplexing Higher degrees of multiplexing enable larger network emulation Inaccuracies introduced due to context switching, scheduling, resource contention, etc

16. Accuracy vs. Scalability Reduce overhead by deviating from target network requirements Changes should minimally impact application behavior Ideally, system reports degree and nature of emulation inaccuracy

17. Distillation Pure hop-by-hop emulation Distilled topology is isomorphic to target network High per packet overhead End-to-end distillation Remove all interior nodes in network Collapse each path into single pipe Latency = sum of latencies along path Reliability = product of link reliabilities along path Low per packet overhead Does not emulate link contention along path

18. Distillation - continued Walk-In Preserve the first walk-in links from edges Interior links replaced with full mesh Does not model contention in interior Walk-out Extension to walk-in to support interior link contention Preserves set of links in interior Collapses paths between walk-out and walk-in sets

19. Evaluating Distillation Ring topology with 20 routers interconnected at 20 MB/s each Each router as 20 VNs Routers partitioned into generator and reciever sets 419 pipes shared between 400 VNs End-to-end distallation contains 79,800 pipes Last mile distillation preserves 400 edge links Test distribution of bandwidth between nodes

20. Evaluating Distillation

21. Changing Network Statistics ModelNet allows users to modify pipe parameters while emulation is in progress User can change bandwidth, delay and loss rate of set of links Also support for modeling node and link failures

22. Case Studies Able to evaluate a 10,000 node network of unmodified Gnutella clients 100 edge nodes with 100 VNs each Extensions support emulation of ad hoc wireless networks Broadcast communication and node mobility CFS Able to reproduce CFS implementation running on RON testbed ModelNet results closely match CFS/RON in all cases

23. Case Studies Replicated Web Services Need to investigate replica placement and routing policies under realistic wide-area conditions Study effects of replication on client latencies using 2.5 minute trace of requests to www.ibm.com Adaptive Overlays ACDC, an adaptive overlay system that tries to build routes that deliver better cost, delay or both. 600 nodes in topology, 120 of them in overlay network Test the behavior of the system to increasing delays between links Results very similar to experiment performed under ns2

24. Conclusion ModelNet provides an emulation environment that allows Testing of unmodified applications Reproducible results Experimentation using broad range of network topologies and characteristics Large scale experiments (thousands of nodes and gigabits of cross traffic)