A Personal Assistant for Web Database Caching

Presentation given at CAiSE 2000, 12th International Conference on Advanced Information Systems Engineering, Stockholm, Sweden, June 2000

ABSTRACT: To improve the performance of web database access for regular users, we have developed a client caching agent, referred to as a personal assistant. In addition to caching strategies based on data characteristics and user specification, the personal assistant dynamically prefetches information based on previously monitored user access patterns. It is part of an overall multi-layered caching scheme where cache coherency is ensured through cooperation with a server-side database caching agent. The personal assistant has been implemented in Java and integrated into the web architecture for the OMS Pro database management system.

Research paper: https://beatsigner.com/publications/a-personal-assistant-for-web-database-caching.pdf

Transcript

1. A Personal Assistant for Web
   Database Caching
   Beat Signer, signer@inf.ethz.ch
   Institute for Information Systems
   ETH Zurich
   

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2. Overview
   • Motivation
   • Internet OMS architecture
   • Personal Assistant
   • Cache architecture
   • Performance measurements
   • Conclusion
   

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3. Motivation
   • Lack of most browsers to cache dynamically
   generated web pages
   • Cooperative client, middle layer and server caches
   to improve performance
   • Reduction of query response times by better
   usage of the available resources
   

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4. Internet OMS Architecture
   Front-End Agent
   Personal Assistant
   Session Cache
   Active Cache
   Global Cache
   HTTP Server Database Agent
   Web Browser
   OMS
   Database
     
   Internet
   

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5. Cache Requirements
   • Reduction of response times (latency)
   • Reduction of idle times (e.g. modem connection)
   • Cache consistency
   • Persistence
   • Flexibility (different user skills)
   • Active caching component (local prefetching)
   • Small overhead to maintain cache
   • Optimal usage of available cache resource
   

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6. Personal Assistant Cache Structure
   Personal Assistant Cache
   query results images
   Session Cache
   query results images
   Personal Cache
   query results images
   Prefetching Cache
   Persistent Client Cache
   Short-Term Cache
   ▪ passive
   ▪ priority 3
   Explicit User Specified Objects
   ▪ permanent
   ▪ passive
   ▪ priority 1
   Long-Term Cache
   ▪ user profile
   ▪ active
   ▪ priority 2
   disjoint
   

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7. Prefetching Cache Statistics
   LRUB-Cache (fixed size)
   LRUB-Cache
   (fixed size)
   query 1 query 2 query n
   statistic
   next query 1
   next query 2
   next query n
   next query 3
   . . . .
   statistic
   next query 1
   next query 2
   next query n
   next query 3
   . . . .
   statistic
   next query 1
   next query 2
   next query n
   next query 3
   . . . .
   

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8. Cache Replacement Strategy
   • Least recently used (LRU)
   • Introduce bonus for entries often used in the past
   but not accessed recently → LRUB
   1
   0
   ,
   )
   1
   (
   weight
   i
   
   
   −
   +
   = 
   
   
   i
   i
   T
   H
   i
   H : Number of hits for cache entry i
   i
   T : Time since last access of cache entry i
   • Weight for cache entry i defined as follows:
   

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9. q1 q3
   q5
   q9
   q2
   q6
   q4
   q7
   q8
   Advantages of Adaptive Prefetching
   • Assumption: global cache of
   size 4 for most frequently
   used queries → which of the
   6 “hot queries” should be
   cached?
   q1 q3
   q5
   q9
   q2
   q6
   q4
   q7
   q8
   q1 q3
   q5
   q9
   q2
   q6
   q4
   q7
   q8
   • Adaptive prefetching →
   sliding window mechanism,
   i.e. cache of size 2 is
   sufficient!
   

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10. Cache Administration Tools
    

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11. Average Response Times
    Average Time Worst Case
    0
    500
    1000
    1500
    2000
    2500
    3000
    3500
    4000
    4500
    5000
    Total Time Query Time Image Time
    time[ms]
    Without Personal Assistant With Personal Assistant
    Average Time Best Case
    0
    500
    1000
    1500
    2000
    2500
    3000
    3500
    4000
    4500
    5000
    Total Time Query Time Image Time
    time[ms]
    Without Personal Assistant With Personal Assistant
    

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12. Image Response Times
    Average Image Time Worst Case
    0
    500
    1000
    1500
    2000
    2500
    3000
    3500
    4000
    4500
    5000
    Without Personal Assistant With Personal Assistant
    time[ms]
    User 1 User 2 User 3 User 4
    Average Image Time Best Case
    0
    500
    1000
    1500
    2000
    2500
    3000
    3500
    4000
    4500
    5000
    Without Personal Assistant With Personal Assistant
    time[ms]
    User 1 User 2 User 3 User 4
    

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13. Cache Hit Rates
    • Increased hit rate even in worst case scenario
    (due to larger caches)
    • Front-end agent profits from locality of queries in
    best case scenario
    Front-End Agent
    Personal Assistant
    queries images queries images
    Worst Case Best Case
    13% 4% 33% 29%
    29% 28% 75% 68%
    

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14. Conclusion
    • Advantages
    ▪ increased cache hit rate
    ▪ reduced response times
    ▪ adaptation to individual users
    ▪ easy to use (transparent)
    ▪ prefetching statistic always up to date
    • Costs
    ▪ database agent eventually has to handle an
    increased number of queries (prefetching) →
    increased server loads
    ▪ overhead to maintain cache consistency
    

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15. Future Work
    • Use similiar prefetching techniques to build an
    HTTP caching proxy
    • First prototype running at gordon.inf.ethz.ch:9090
    

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