A Framework for Highly-Available Cascaded Real

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Transcript A Framework for Highly-Available Cascaded Real

Internet-Scale Research at
Panel Session
SAHARA Retreat, Jan 2002
Prof. Randy H. Katz,
Bhaskaran Raman,
Z. Morley Mao,
Yan Chen
Problem Statement
perf. info.
Service cluster: compute
cluster capable of running
• Overlay network for service
• Want to study recovery
• Lots of client sessions
• Methodology for evaluation of
– 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
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
Node 1
Rule for 12
Rule for 13
Rule for 34
Node 2
Rule for 43
Node 3
Node 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
My experience in Internet
• 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
• <img src=http://xxx.rd.example.com/tr.gif height=1
– 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
My experience in Internet
1. HTTP GET request for the image
2. HTTP redirect to
Redirector for
Content server for the image
4. Request to resolve IP10-0-0-1.cs.example.com
Local DNS server
5. Reply: IP address of content server
Name server for
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
• W/o carefully considering its applicability
• A scalable measurement methodology helps ease
of adoption
Content Distribution Network (CDN)
Dynamic clustering for efficient Web contents replication
Network Topology:
Use greedy algorithm for replica placement to reduce the
response latency of end users
Trace-driven simulation to find optimal granularity of replication
Pure-random & transit-Stub models from GT-ITM
A real AS-level topology from 7 widely-dispersed BGP peers
Real world traces:
Web Site
Total Requests
1,469,248 (1 hr)
All day
All day
-- Cluster MSNBC Web clients with BGP prefix
- BGP tables from a BBNPlanet router on 01/24/2001
- 10K clusters left, chooses top 10% covering >70% of requests
-- Cluster NASA Web clients with domain names
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
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?
Discussion: Difficulties of Internet
• Results vary greatly depending on your measurement
– 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
Discussion: Issues with Emulation
• Emulation platform: modeling correlations in n/w
– What happens in one part of the Internet may have nonzero correlation with behavior of another part
• Scale of topology
– We have O(100) machines in department
– O(1500) machines on campus
– Is this believable?