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Ubiquitous Instrumentation

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Host-based measurement Anemone. Instrumented stack, kernel structures ... Access to Anemone flow/packet tables. Built over Pastry, highly scalable (106 nodes ... – PowerPoint PPT presentation

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Title: Ubiquitous Instrumentation


1
Ubiquitous Instrumentation
  • Richard Mortier
  • MSR Cambridge
  • mort_at_microsoft.com

2
Measuring networks
  • Active
  • traceroute/ping/etc (ICMP)
  • SNMP MIBs
  • Non-critical functionality ? buggy
  • Passive
  • NetFlow, IPFIX, sFlow
  • Port spanning, VLAN spanning
  • Router/switch only ? poor visibility, scalability

3
Claims
  • Routers are just specialized hosts
  • Hardware, protocols, configuration
  • Hosts are part of the network as well
  • Transmit, receive, forward data

4
Claims
  • Routers are just specialized hosts
  • Hardware, protocols, configuration
  • Hosts are part of the network as well
  • Transmit, receive, forward data
  • The strong distinction that has grown between
    them makes understanding network behaviour
    difficult
  • Traffic is opaque to routers (and becoming more
    so)
  • Network is black box service to hosts (and
    becoming more so)
  • Hosts provide inputs
  • we should use them to understand demands
  • Routers provide resource
  • their configuration implements constraints

5
Networking measurements
  • Host-based measurement Anemone
  • Instrumented stack, kernel structures
  • Per-EXE per-packet live network stats
  • Unify flows with routing topology
  • Distributed query system Seaweed
  • Access to Anemone flow/packet tables
  • Built over Pastry, highly scalable (106 nodes)

6
Measure for measure
  • Ubiquitous network measurement
  • Undercarriage of the Knowledge Plane
  • Infrastructure for autonomic-foo?
  • Concretely
  • Combining host network data SeaStar
  • Single trust domain how to expand?
  • User-visible diagnosis Constellation
  • What do users want to know about the network?

7
Measure for measure
  • Data gathering and management
  • Existing tools primitive tend toward manual
    analysis
  • Need performant, type-safe, distributed
    processing
  • Theoretical aspects of temporal, spatial sampling
  • Unifying many different datasets
  • Routing and configuration data
  • Pcap and netflow and SNMP and data
  • Mining structure, relationships from data
  • Machine learning techniques seem a very rich vein
  • Robust automated processing techniques
  • Distributed trust, provenance, privacy
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