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Scalability and Accuracy in a LargeScale Network Emulator
Amin Vahdat, Ken Yocum, Kevin Walsh, Priya
Mahadevan, Dejan Kostic, Jeff Chase, and David
Becker
Presented by Stacy Patterson
Outline
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Motivation
ModelNet Design
Evaluation
Conclusion
Motivation
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Need a way to test large-scale Internet services
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Testing in the real world
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Results not reproducible or predictable
Difficult to deploy and administer research software
Simulation tools
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Peer-to-peer, overlay networks, wide area replication
Allows control over test environment
May miss important system interactions
Emulation
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Network emulators can subject application traffic to endto-end bandwidth constraints, latency, and loss rate of
user specified topology
Previous implementations not scalable
ModelNet
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A scalable, cluster-based, comprehensive network
emulation environment
Design
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User run configurable number of instances of
application on Edge Nodes within dedicated server
cluster
Each instance is a Virtual Edge Node (VN)
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Edge nodes route traffic through cluster of Core
Routers
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Each VN has a unique IP address
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
ModelNet Phases
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(1) Create
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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
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Transforms GMLgraph into pipe topology
ModelNet Phases
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(3) Assignment
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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.
ModelNet Phases
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(4) Binding
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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
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Executes target application code on edge nodes
Inside the Core
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Route traffic through emulated “pipes”
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Each route is an ordered list of pipes
Packets move through pipes by reference
Routing table requires O(n2) space
Packet Scheduling
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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
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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
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Next pipe in route may be on different machine
If so, core node tunnels packet descriptor to next node
Scalability Issues
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Traffic traversing core is limited by cluster’s
physical internal bandwidth
ModelNet must buffer up to full bandwidthdelay product of target network.
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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
Evaluation
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Core routers - 1.4 Ghz Pentium III, 1 GB
memory
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Connected using 1 GB switch
Edge nodes - 1 Ghz Pentium III 256 MB
memory
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Connected using 100 MB/s
Baseline Accuracy
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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
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Packets are delivered within 1ms of target end-toend value
Accuracy is maintained up to 100% CPU usage
Capacity
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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
Capacity
Scalability
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Additional Cores
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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
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Higher degrees of multiplexing enable larger network
emulation
Inaccuracies introduced due to context switching,
scheduling, resource contention, etc
Accuracy vs. Scalability
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Reduce overhead by deviating from target
network requirements
Changes should minimally impact application
behavior
Ideally, system reports degree and nature of
emulation inaccuracy
Distillation
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Pure hop-by-hop emulation
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Distilled topology is isomorphic to target network
High per packet overhead
End-to-end distillation
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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
Distillation - continued
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Walk-In
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Preserve the first walk-in links from edges
Interior links replaced with full mesh
Does not model contention in interior
Walk-out
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Extension to walk-in to support interior link
contention
Preserves set of links in interior
Collapses paths between walk-out and walk-in
sets
Evaluating Distillation
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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
Evaluating Distillation
Changing Network Statistics
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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
Case Studies
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Able to evaluate a 10,000 node network of
unmodified Gnutella clients
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Extensions support emulation of ad hoc wireless
networks
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100 edge nodes with 100 VNs each
Broadcast communication and node mobility
CFS
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Able to reproduce CFS implementation running on RON
testbed
ModelNet results closely match CFS/RON in all cases
Case Studies
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Replicated Web Services
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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
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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
Conclusion
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ModelNet provides an emulation environment
that allows
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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)