Jir

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Introduction
INCITE

• Jirí Navrátil
• SLAC

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Project Partners and Researchers
INCITE Edge-based Traffic Processing and
Service Inference for High-Performance Networks
Richard Baraniuk, Rice University Les Cottrell,
SLAC Wu-chun Feng, LANL
Rice University Richard Baraniuk, Edward
Knightly, Robert Nowak, Rudolf Riedi Xin Wang,
Yolanda Tsang, Shriram Sarvotham, Vinay Ribeiro
Los Alamos National Lab (LANL) Wu-chun Feng,
Mark Gardner, Eric Weigle Stanford Linear
Accelerator Center (SLAC) Les Cottrell, Warren
Matthews, Jiri Navratil
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Project Goals
INCITE Edge-based Traffic Processing and
Service Inference for High-Performance Networks
Richard Baraniuk, Rice University Les Cottrell,
SLAC Wu-chun Feng, LANL

• Objectives
• scalable, edge-based tools for on-line network
  analysis, modeling, and measurement
• Based on
• advanced mathematical theory and methods
• Designeted for
• support high-performance computing
  infrastructures, such as computational grids,
• ESNET, Internet2 and other HPNetworking project

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Project Elements
INCITE Edge-based Traffic Processing and
Service Inference for High-Performance Networks
Richard Baraniuk, Rice University Les Cottrell,
SLAC Wu-chun Feng, LANL

• Advanced techniques
• from networking, supercomputing, statistical
  signal processing, applied mathematics
• Multiscale analysis and modeling
• understand causes of burstiness in network
  traffic
• realistic, yet analytically tractable,
  statistically robust, and computationally
  efficient modeling
• On-line inference algorithms
• characterize and map network performance as a
  function of space, time, application, and
  protocol
• Data collection tools and validation experiments

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Scheduled Accomplishments
INCITE Edge-based Traffic Processing and
Service Inference for High-Performance Networks
Richard Baraniuk, Rice University Les Cottrell,
SLAC Wu-chun Feng, LANL

• Multiscale traffic models and analysis techniques
• based on multifractals, cascades, wavelets
• study how large flows interact and cause bursts
• study adverse modulation of application-level
  traffic by TCP/IP
• Inference algorithms for paths, links, and
  routers
• multiscale end-to-end path modeling and probing
• network tomography (active and passive)
• Data collection tools
• add multiscale path, link inference to PingER
  suite
• integrate into ESnet NIMI infrastructure
• MAGNeT Monitor for Application-Generated
  Network Traffic
• TICKET Traffic Information-Collecting Kernel
  with Exact Timing

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Future Research Plans
INCITE Edge-based Traffic Processing and
Service Inference for High-Performance Networks
Richard Baraniuk, Rice University Les Cottrell,
SLAC Wu-chun Feng, LANL

• New, high-performance traffic models
• guide RD of next-generation protocols
• Application-generated network traffic repository
• enable grid and network researchers to test and
  evaluate new protocols with actual traffic
  demands of applications rather than modulated
  demands
• Multiclass service inference
• enable network clients to assess a system's
  multi-class mechanisms and parameters using only
  passive, external observations
• Predictable QoS via end-point control
• ensure minimum QoS levels to traffic flows
• exploit path and link inferences in real-time
  end-point admission control

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Surveyor
NIMI
Pinger
RIPE
There is no vacuum
Optivity
CiscoWorks
Spectrum
HpOpenview
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JNFLOW
Cisco-Netflows
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(From Papers to Practice)
FPP phase

• MWFS, TOMO, TOPO

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20 ms
300 ms
40 T for new set of values (12 sec)
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First results
BWe 9,875 Mbps for 10 Mbps Ethernet
CT-Graph
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What has been done

• Phase 1 - Remodeling
• - Code separation (BW and CT)
• - Find how to call MATLAB from another program
• - Analyze Results and data
• - Find optimal params for model
• Phase 2
• - Webing of BW estimate

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Data Dispersions from sunstats.cern.ch
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ccnsn07.in2p3.fr
sunstats.cern.ch
pcgiga.cern.ch
plato.cacr.caltech.edu
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pcgiga.cern.ch default WS BW 70Mbps
pcgiga.cern.ch WS 512K BW 100 Mbps
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Reaction to the network problems
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Problems ??? ? ? ?
network
software
licence
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After tuning
More optimistics results
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MF-CT Features and benefits

• No need access to routers !
• Current monitoring systems for Load of traffic
  are based on SNMP or Flows (needs access to
  routers)
• Low cost
• Allows permanent monitoring (20 pkts/sec
  overhead 10 Kbytes/sec)
• Can be used as data provider for ABW prediction
  (ABWBW-CT)
• Weak point for common use
• MATLAB code

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Future work on CT

• Verification model
• Define and setup verification model (SR)
• Measurements (S)
• Analyze results (SR)
• On-line running on selected sites
• Prepare code for automation and Webing (S)
• CT-Code modificaton ? (R)

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MF-CT verification model
UDP echo
SNMP counter
CERN
SNMP counter
internet
SNMP counter
IN2P3
SLAC
SNMP counter
MF-CT Simulator
UDP echo
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CT RE-ENGINEERING

• For practical monitoring would be necessary to do
  modification for using it in different modes
• Continuos mode for monitoring one site in Large
  time scale (hours)
• Accumulation mode (1 min, 5 min, ?) for running
  for more sites in parallel
• ? Solution without MATLAB ?

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2 NEW 2Ls
coming soon
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Rob Nowak (and CAIDA people) say
www.caida.org
This is internet
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Network Topology Identification
Ratnasamy McCanne (99) Duffield, et al
(00,01,02)
Bestavros, et al (01) Coates, et al (01)
Pairwise delay measurements reveal topology
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Network Tomography
source
router / node
link
receivers
Measure end-to-end (from source to
receiver) losses/delays Infer link-level (at
internal routers) loss rates and delay
distributions
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Unicast Network Tomography
Measure end-to-end losses of packets
Cannot isolate where losses occur !
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Packet Pair Measurements
cross-traffic
delay
measurement packet pair
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Delay Estimation
Measure end-to-end delays of packet-pairs
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Packet-pair measurements

• Key Assumptions
• fixed routes
• iid pair-measurements
• losses delays on
• each link are mutually
• independent
• packet-pair losses
• delays on shared links
• are nearly identical

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ns Simulation

• 40-byte packet-pair probes every 50 ms
• competing traffic comprised of
• on-off exponential (500 byte packets)
• TCP connections (1000 byte packets)

cross-traffic link 9
Kbytes/s
time (s)
Test network showing link bandwidths (Mb/s)
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Future work on TM and TP

• Model in frame of Internet (100 sites)
• Define verification model (SR)
• Deploy and install code on sites (S)
• First measurements (SR)
• Analyze results (form,speed,quantity) (SR)
• ? Code modificaton (R)
• Production model ?
• Compete with Pinger, RIPE, Surveyor, Nimi ?
• How to unify VIRTUAL structure with Real