Internet-Scale Research at Universities

1
Internet-Scale Research at Universities

• Panel Session
• SAHARA Retreat, Jan 2002
• Prof. Randy H. Katz,
• Bhaskaran Raman,
• Z. Morley Mao,
• Yan Chen

2
Problem Statement

• Overlay network for service composition
• Want to study recovery algorithms
• Lots of client sessions
• Methodology for evaluation of design?
• Simulation?
• Slow, does not scale with nodes, client
  sessions
• Does not bring out processing bottlenecks
• Real testbed?
• Cannot be large setup and management problems
• Non-repeatable, not good for controlled design
  study

3
Our approach so far

• Emulation platform
• Real implementation of software, but emulation of
  n/w parameters
• Inspired by NistNET
• Developed our own user-level implementation
• Gave us better control
• Runs on the Millennium cluster of workstations
• Central bottleneck 20,000 pkts/sec

4
Parameters modeled

• Overlay topology
• Generate 6,510-node physical network using GT-ITM
• Choose subset of nodes for overlay network
• Latency modeling
• Base latency according to edge weight
• Variation in accordance with RTT spikes are
  isolated
• Outage period
• Using traces
• Collected UDP-based measurements across 12 host
  pairs
• Berkeley, Stanford, UNSW (Australia), UIUC,
  TU-Berlin (Germany), CMU
• CDF of outage periods, used to model outage
  periods

5
My experience in Internet measurement

• Goal
• collect client-Local DNS server associations
• to evaluate DNS-based server selection
• Built a measurement infrastructure
• Three components
• 1x1 pixel embedded transparent GIF image
• ltimg srchttp//xxx.rd.example.com/tr.gif
  height1 width1gt
• A specialized authoritative DNS server
• Allows hostnames to be wild-carded
• An HTTP redirector
• Always responds with 302 Moved Temporarily
• Redirect to a URL with client IP address embedded

6
My experience in Internet measurement
7
My lessons

• Common myths about Internet measurements
• Measurements done from University sites are
  representative of the Internet
• The following are good proximity metrics
• AS hop count
• Router hop count
• I can just quote some measurement results from
  previous papers
• W/o carefully considering its applicability
• A scalable measurement methodology helps ease of
  adoption

8
Content Distribution Network (CDN)

• Dynamic clustering for efficient Web contents
  replication
• Use greedy algorithm for replica placement to
  reduce the response latency of end users
• Trace-driven simulation to find optimal
  granularity of replication
• Network Topology
• Pure-random transit-Stub models from GT-ITM
• A real AS-level topology from 7 widely-dispersed
  BGP peers
• Real world traces
• -- Cluster MSNBC Web clients with BGP prefix
• - BGP tables from a BBNPlanet router on
  01/24/2001
• - 10K clusters left, chooses top 10 covering
  gt70 of requests
• -- Cluster NASA Web clients with domain names

Web Site Period Duration Total Requests Requests/day
MSNBC 8-10/1999 1011am 10,284,735 1,469,248 (1 hr)
NASA 7/1995 All day 3,461,612 56,748
WorldCup 5-7/1998 All day 1,352,804,107 15,372,774
9
Wide-area Network Distance Estimation

• Problem formulation
• Given N end hosts that belong to different
  administrative domains, how to select a subset of
  them to be probes and build an overlay distance
  estimation service without knowing the underlying
  topology?
• Solution Internet Iso-bar
• Cluster of hosts that perceive similar
  performance to Internet select a monitor for
  each cluster for active and continuous probing
• Clustering with congestion/path outage
  correlation
• Evaluate the prediction accuracy and stability
• Evaluation Methodology (I)
• NLANR AMP data set
• 119 sites on US (106 after filtering out most off
  sites)
• Traceroute between every pair of hosts every
  minute
• Clustering uses daily geometric mean of
  round-trip time (RTT)
• Raw data 6/24/00 12/3/01

10
Evaluation Methodology (II)

• Keynote Website Perspective benchmarking
• Measure Web site performance from more than 100
  agents
• Heterogeneous core network various ISPs
• Heterogeneous access network
• Dial up 56K, DSL and high-bandwidth business
  connections
• Agents locations
• America (including Canada, Mexico) 67 agents in
  29 cities from 15 ISPs
• Europe 25 agents in 12 cities from 16 ISPs
• Asia 8 agents in 6 cities from 8 ISPs
• Australia 3 agents in 3 cities from 3 ISPs
• 40 most popular Web servers for benchmarking
• Side problem how to reduce the number of agents
  and/or servers, but still represent the majority
  of end-user performance for reasonable long
  period?

11
Discussion Difficulties of Internet measurement

• Results vary greatly depending on your
  measurement methodology
• The number and identity of sites you measure
• Commercial vs. educational sites
• Your measurement location
• Well-connected site vs. dialup site
• Backbone vs. access network, server vs. client
• Time when measurement is taken
• Time of day, day of year
• Transient effects
• E.g., Network congestion, flash crowd
• Frequency of measurements (for correlation
  studies)
• Intrusiveness of the measurement
• Does the measurement affect what you are
  measuring

12
Discussion Issues with Emulation

• Emulation platform modeling correlations in n/w
  behavior
• What happens in one part of the Internet may have
  non-zero correlation with behavior of another
  part
• Scale of topology
• We have O(100) machines in department
• O(1500) machines on campus
• Is this believable?