Storage Allocation in Prefetching Techniques of Web Caches

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Storage Allocation in Prefetching Techniques of
Web Caches

• D. Zeng, F. Wang, S. Ram
• Appeared in proceedings of ACM conference in
  Electronic commerce (EC03) San Diego June 9-12,
  2003
• Presented by Laura D. Goadrich

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The Web

• Large-scale distributed information system where
  data Objects are published and accessible by
  users
• Problems caused by the demand of increased web
  capacity
• Network traffic congestion
• Web server overloads
• Solution web caching

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Web caching

• Benefits
• Improves web performance (reduces access latency)
• Increases web capacity
• Alleviate traffic congestion (reducing network
  bandwidth consumption)
• Reducing number of client requests (workload)
• Possibly improve failure tolerance and robustness
  of Web (maintaining cached copies of web objects
  for unreachable networks)
• Prefetching
• Anticipate users future needs
• This research
• Focuses on making cache-related storage capacity
  decisions (storage capacity limits the number of
  prefetched web objects)
• Therefore allocate cache storage in prefetching
• The authors state this focus has not been
  researched

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Ideas

• Current research
• Predict user web accesses without considering
  cache storage limit
• This research optimization based models
• Maximize hit rate
• Maximize byte hit rate
• Minimize access latency
• (first 2 are primary goals of web caching
  maximize)
• Benefit of this research guide the operations of
  a prefetching system

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Web prefetching techniques

• Client-initiated policies
• User A is likely to access URL U2 right after URL
  U1
• Patterns learned via Markov algorithms
• Server-initiated policies
• Anticipate future requests based on server logs
  and proactively send the corresponding Web
  objects to participating cache servers or client
  browsers
• Top-n algorithm
• Hybrid policies
• Combine user access patterns from clients and
  general statistics from servers to improve the
  quality of prediction
• Failing of policies how to make decisions of
  which Web objects to prefetch considering storage
  capacity

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Assumptions/Notation
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Hit Rate (HR) Model
(1) (2) (3)
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Byte Hit Rate (BHR) Model
(4) (2) (3)
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Byte Hit Rate (BHR) Model
(7) (2) (3)
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Transforming HR, BHR AL into the Knapsack
problem

• Benefits of Knapsack problem
• Well studied
• easiest NP-hard problem
• Can solve optimally by a pseudo-polynomial
  algorithm based on dynamic programming
• A fully polynomial approximation is possible
• Focus on greedy algorithm (due to paper length
  limits)

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Greedy Algorithm

• Sort all URLs into a sequence
• Determine a threshold k defined as
• Prefetch Web objects referred to by URLs

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Other Allocation Policies Tested

• Optimal policy using CPLEX
• Disadvantages
• Complex
• Increased implementation time
• Difficult to implement
• Top-n
• Developed for Web usage prediction
• Used to regulate storage allocations by
  appropriately setting n
• Equivalent to Greedy BHR relying only on Pi

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Simulations
a. b.
LN(µ,s) lognormal distribution with mean eµ and
shape s
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Performance Comparison
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Results

• Greedy algorithms and Top-n in general achieve
  reasonable performance
• Greedy algorithms outperform Top-n with respect
  to hit rate and access latency
• There exists a relatively large performance gap
  between an optimal approach and fast heuristic
  methods when Web objects vary greatly in size
• Suggests the need for developing more
  sophisticated allocation policies such as a
  dynamic programming-based approach

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Contributions

• Focus stress importance of effective storage
  allocation in prefetching
• Paper contributions
• Provide new formulations for prefetching storage
  allocation
• Create computationally efficient allocation
  policies based on storage allocations solved by
  the knapsack problem
• Models created lead to more precise understanding
  of the applicability and effectiveness of Top-n
  policy

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Future Work

• Trace-based simulation
• Actual web access logs
• More realistic environment
• Modeling
• Integrate allocation models with caching storage
  management models
• i.e. Cache replacement

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Changes- Recommendations

• Not renaming the same constraints
• More resources (5 articles, 2 books)
• Discuss feasible solve times (opt)
• Test/Hypothesize implementation strategies for
  real application