On the scale and performance of cooperative Web proxy caching

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On the scale and performanceof cooperative Web
proxy caching

• 2/3/06

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Related Work

• Static Analysis
• request rate,
• number of requests
• diversity of population
• Trace driven cache simulation
• Temporal locality of proxy traces
• Web page requests follow Ziph-like distribution
• the number of requests to the ith
• most popular document is proportional to 1/i_at_
  for some constant _at_

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Related Work

• Hit ratio for a Web proxy grows logarithmically
  with the
• Client population of the proxy and the number of
  requests seen by the proxy

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Coop Web Caching Work

• reduce access latency
• Bandwidth consumption
• Hierachical
• Directory based
• Multicast based
• Upshot cooperation requires cache manager to
  determine when to cooperate

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WebDoc Sharing and Caching

• 4 questions
• 1. What is the best performance one could achieve
• perfect cooperative caching?
• 2. For what range of client populations can
  cooperative
• caching work effectively?
• 3. Does the way in which clients are assigned to
• matter?
• 4. What cache hit rates are necessary to achieve
  worthwhile decreases in document access latency?

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WebDoc Sharing and Caching

• Two sites analyzed
• UW
• 200 organizations
• Microsoft Campus
• Big corporation large population, number of
  proxies

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WebDoc Sharing and Caching

• Work specifically not done
• Did not investigate the effects of cooperative
  caching on server load
• General
• Hot spot conditions

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• First results

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Trace Collection

• Existing traces inadequate
• New design
• UW has few proxies
• Traces are anonymized
• Grouped based on organizational membership
• Track organizations across the campus
• Table 1 shows that 7 days gave 108 million
  requests by 60,000 clients to 360,000 servers in
  the Microsoft trace.

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Simulation methodology

• Real caches will incur misses due to capacity
  limitations that we do not model.
• Capacity misses are rarely the bottleneck for Web
  caches.
• For example, only 3 of the requests to the MS
  Web proxies missed due to the finite capacity of
  the proxies (which have 9GB of RAM and 180GB of
  disk capacity).

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Simulation Methodology

• Practical cache
• models the cacheability of documents according to
  the algorithms in the Squid V2 implementation
• Ideal cache
• All documents are cacheable i.e. equal
  cacheability for all
• Upper bound of improvement on workloads due to
  improvements in internet protocols

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Population size

• In a cooperative-caching scheme, a proxy forwards
  a missing request to other proxies to determine
  if
• Another proxy holds the requested document
• The document can be returned faster than a
  request to the server.

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Population size

• A collection of cooperating caches will achieve
  the hit-rate of a single proxy acting over the
  combined population of all the proxies.
• Proxies will pay the overheads of inter-proxy
  communication latency.
• Examining a single, top-level proxy thus gives us
  an upper on cooperative-caching performance.

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Population size
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Population size
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Hit rate vs. latency and bandwidth

• Latency, not hit rate is crucial to clients
• To ISPs hit-rate bandwidth savings improving
  congestion

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Hit rate vs. latency and bandwidth
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Hit rate vs. latency and bandwidth
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Proxies and organizations

• If high localily small population
• Then achieve max hit rate

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Question

• What benefit would clients in real organizations
  see if their proxies were to cooperate with other
  real organizational proxies?
• UW environment is an attempted answer
• each org acts like business, with own proxy
  sitting on its connection to the Internet.
• Each org categorized into 1 of 200 UW
  organizations

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Proxies and organizations
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• Question is grouping of clients to proxies, for
  example, one based on each clients document
  interests, better?
• Soln
• Clustering algo used optimize intracluster
  sharing
• randomly assigned clusters have a consistently
  lower hit rate than the optimally clustered
  organizations.

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Proxies and organizations
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Impact of larger population size

• Recall cooperative caching can increase hit rate
• This indicates that there is little correlation
  between sharing and the cacheability of documents
  for the UW population.
• cooperative caching among populations larger than
  2.4 million does not increase the hit rate to
  cacheable documents

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Impact of larger population size

• Experiment have MS and UW cooperate
• Results
• When scaled by equal factors, MS gains more
  benefit by cooperating with the UW population
  than the UW population gains by cooperating with
  MS.
• Unpopular documents are universally unpopular ?
  a request in UW or MS will not find either proxy
• 1/500 (first access) has hit rate increase
  regardless of popularity

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Proxies and organizations
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Impact of larger population size
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Docs and Proxy Sharing Summary

• 1. The behavior of cooperative caching is
  characterized
• by two different regions of the hit rate vs.
  population
• curve. For smaller populations, hit rate
  increases
• rapidly with population it is in this region
  that cooperative
• caching can be used effectively. However, these
• population sizes can be handled by a single
  proxy.
• Cooperative caching is only necessary to adapt to
  proxy assignments made for political or
  geographical reasons.

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Docs and Proxy Sharing Summary

• 2. Larger populations (beyond the knee of the
  population vs. hit rate curve), cooperative
  caching is unlikely to provide significant
  benefit.
• Simultaneous traces of the MS and UW populations
  show that via cooperative caching
• UW 4x increase of population via cooperative
  caching netted only a 2.7 increase in cacheable
  hit rate.

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Docs and Proxy Sharing Summary

• 3. MS and others show clustering does occur but
  that cooperative caching specialized to interest
  groups is unlikely to be effective.

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Docs and Proxy Sharing Summary

• 4. Previous work has hinted at the general
  trends, but CC end conclusions have not been show
  yet

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An analytic model of Web accesses

• Steady-state performance
• The model
• Model parameters
• Performance of large scale proxy caching
• Summary

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Steady-state performance
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The Model

• Population has N clients
• n total documents
• The important characteristic of a Zipf-like
  distribution is that it is heavy-tailed a
  significant fraction of the probability mass is
  concentrated in the tail, which in this case
  means that a significant fraction of requests go
  to the relatively unpopular documents.

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The Model

• Ziph Distribution Cotd
• Popularity of document is proportional to 1 / i_at_
• As _at_ increases, the distribution becomes less
  heavy-tailed, and a larger fraction of the
  probability mass is concentrated on the most
  popular documents

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The Model

• The probability that a requested document is
  cacheable is pc.
• Avg document size is E(S). Document size is
  independent of document popularity, latency, and
  change rate
• The last-byte latency to the server that houses
  that document has average value E(L). Last-byte
  latency is independent of document popularity and
  document change rate.

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The Model

• Performance Characteristics

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The Model

• Performance characteristics continued
• The expected last-byte latency to serve a request
  is given by
• average bandwidth savings per request due to
  proxy caching

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The Model

• Differences between new and previous work
• we consider the steady state behavior of caching
  systems rather than caching behavior based on a
  finite request sequence
• Incorporate document change rate into the model
  rather than assuming that documents are static
• Goal use our model to understand the performance
  of large-scale, cooperative-caching schemes in
  terms of hit rate, latency, bandwidth savings,
  and storage consumed.

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Model parameters

• UW Trace

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Model parameters
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Performance of large scale proxy caching

• Hit rate, latency, and bandwidth
• Document rate of change
• Client request rate
• Document popularity and size of the Web

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Performance of large scale proxy caching
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Document popularity and size of the Web

• Zipf parameter _at_
• documents n
• Increasing _at_ (alpha) skews the distribution
  towards popular documents significantly
  increasing hit rates for slower rates of change
• Slight increase in hit rates for faster rates of
  change.
• Increase the number of documents n shifts the
  curves for slow and fast rates of change to
  larger populations
• This population shift is in proportion to the
  increase in n
• n3.2 billion ? the slow curve reaches a 90 hit
  rate at a population of 250,000
• n32 billion ? the slow curve reaches a 90 hit
  rate at a population of 25 million
• n320 billion ? the slow curve reaches a 90 hit
  rate at a population of 250 million.

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Model Summary

• analytic model used to examine the steady-state
  performance of cooperative caching schemes.
• small populations achieve most of the performance
  benefits of cooperative caching.

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Wrap Up

• 1. Without client behavior changes
• little point in continuing design and evaluation
  of highly scalable, cooperative-caching schemes
• Cooperative caching makes sense up to the level
  of a medium-sized city

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Wrap Up

• The largest benefit for cooperative caching is
  achieved for relatively small populations.
• Analysis of cooperation among small organizations
  within the university environment.
• Traces of UW and MS confirmed marginal benefit of
  cooperative caching among organizations with
  populations of 20K clients or more. (large)
• Scale this big only in in very high-bandwidth,
  low-latency environments.

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Wrap Up

• Performance of cooperative caching limited by
  document cacheability.
• Increasing cacheability of documents is the main
  challenge for future Web cache behavior research

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Wrap Up

• Cluster-based analysis of client access patterns
  indicate
• cooperative-caching organizations based on mutual
  interest offer no obvious advantages over
  randomly assigned or organization-based
  groupings.

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Wrap Up

• Fundamentally, the usefulness of cooperative Web
  proxy caching depends upon the scale at which it
  is being applied.
• Whether or not they use cooperative caching
  locally, large organizations should use proxy
  caching for their user populations.
• Concern cooperative caching only marginally
  helpful

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Wrap Up

• Results shown are on static data
• Shift in user workflow will change i.e.
  streaming media
• Average size is magnitudes larger
• Reveal better utilization of network resources
  necessary
• Size and time of transfer for streaming objects
  shows that multicast methods might be good

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Assumptions

• Static objects like web pages, documents
• User populations not too large
• Network latency, performance medium

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Questions?