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Network Research at Avaya Labs Research

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Network topology discovery. Heisenberg. Probes load the network at the ... Volume of data (GB per day) Discovery via the fusion of multiple data sources. SNMP ... – PowerPoint PPT presentation

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Title: Network Research at Avaya Labs Research


1
Network ResearchatAvaya Labs Research
  • The ExpertNet and Chatter Teams
  • Akshay Adhikari
  • Mark Bearden
  • Lorraine Denby
  • Bengi Karacali
  • Jean Meloche
  • Balaji Rao
  • David Stott
  • Among many others

2
Challenges
  • Network topology discovery
  • Heisenberg
  • Probes load the network at the worst time
  • Path or end to end performance but not both
  • Visualizations for paths
  • Topology and paths
  • Route changes
  • Synchronization and One way delay
  • Load balancing and packet loss
  • Missing values and clean up
  • Complexity of networks
  • Volume of data (GB per day)

3
Discovery via the fusion of multiple data sources
  • SNMP
  • IP RR
  • Traceroute
  • I/O octet count correlation
  • Delay distance
  • End to end shadow of network events

4
Synchronization
5
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6
Link level network delay via TTL flood
  • Multiple packets with IP RR for a range of TTL
    are sent between endpoints
  • ICMP messages are received from routers along the
    way
  • Results are used to estimate link level delays

7
Network Sampling Design
  • Where to place the probes
  • Minimize the number of probes needed
  • Minimize the degeneracy
  • Subject to many constraints
  • Where to measure QoS attributes and covariates
  • Minimize the footprint
  • Minimize the time to blame
  • Dynamic sampling programs

8
Chatter
  • Distributed network monitor
  • Lives on cheap 100 MHz box
  • Online root cause analysis
  • Seeks failure patterns among arbitrary covariates
    that are not specified a priori
  • Uses adaptive control charts on important QoS
    attributes to identify interesting conditions

9
Compression
  • Compression is needed to prevent the transmission
    and storage of uninteresting data
  • The data are highly dimensional
  • The collection is distributed
  • How should the compression be distributed

10
Localization
  • Data collection is distributed
  • What methods are local (analysis requiring only
    the local data) or near local (analysis requiring
    limited exchange of information between
    neighbors)?
  • Are there sampling design that affect
    localization?
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