Sleepers

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Sleepers Workaholics

• Caching Strategies in Mobile Computing
• Dr. Daniel Barbará
• Dr. Tomasz Imielinski

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About Me

• Peter Rosegger
• 5th year Computer Science
• Specialization Databases
• Graduation December 2007

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Sleepers Workaholics

• Caching Strategies in Mobile Computing
• Dr. Daniel Barbará
• Professor at George Mason University
• Several patents associated with mobile caching
• Dr. Tomasz Imielinski
• Professor at Rutgers University
• Senior VP Search Technology at Ask.com

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1994

• 16 million cellular subscribers in US

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1994
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The Future of Mobile Computing

• Use Habits
• Large of users
• Check weather, stocks, scores, etc.
• Mobile between cells ( wireless networks)
• Hardware
• Low-powered palmtop machines
• Poor battery life
• Narrow bandwidth

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The Future of Mobile Computing

• Query complex databases, but
• Frequently powered off to save battery
• Frequently changing cells
• Network traffic must be minimized to conserve
  bandwidth

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Why Caching is Important

• Conserve
• COMPUTATIONAL RESOURCES
• BATTERY LIFE
• BANDWIDTH

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Traditional Strategies Fail

• Server lacks knowledge of
• Which units are in its cell
• Which units are powered ON
• Client caches cannot be tracked

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The Solution

• Purpose of Sleepers Workaholics
• "to propose a taxonomy of different cache
  invalidation strategies and study the impact of
  clients' disconnection times on their
  performance."

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Strategies

• Timestamps (TS)
• Amnesic Terminals (AT)
• Signatures (SIG)
• Control Strategy
• No Cache (NC)

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Timestamps

• -Cache entries have timestamps
• -Synchronous, history based, uncompressed reports
• SERVER
• Notify clients of identifiers of items changed
  within last w seconds
• CLIENT
• For each item in cache
• If in report, purge from cache
• If NOT in report, update timestamp to current
  time

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Amnesic Terminals

• -Cache entries have identifiers
• -Synchronous, history based, uncompressed reports
• SERVER
• Notify clients of identifiers of items changed
  within last w seconds
• CLIENT
• For each item in cache
• If in report, purge from cache
• If NOT in report, do nothing

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Signatures

• -Checksums calculated over value of data to form
  Signature
• -Signatures combined using XOR
• -Synchronous, state based, compressed reports
• SERVER
• Server broadcasts the set of combined signatures
• CLIENT
• Item in cache is declared invalid if it belongs
  to too many unmatching signatures (suspected of
  being out of date)

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Analysis

• Calculate THROUGHPUT for each strategy
• L time between invalidation report
  broadcasts
• W bandwidth
• B bits in the broadcast
  (invalidation reports)
• bits available for answering queries (cache
  misses)

C
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Analysis

• T THROUGHPUT queries per interval handled by
  the system
• h cache hit rate, expressed 0, 1
• b bits for a query
• b bits to answer a query
• Traffic (in bits) due to cache misses

q
a
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Throughput
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Effectiveness of a Strategy
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Maximal Throughput

• Server knows
• -What units are in the cell
• -What those units have in their caches
• Server can
• -instantaneously notify units when an item
  changes

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Maximal Hit Ratio
The Hit Ratio achieved in ideal conditions
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Maximal Throughput
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No Caching

• -No invalidation report
• -No intervals

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Timestamps
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Amnesic Terminals
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Signatures

• Consider the probability of false diagnosis
• Probability of a false positive
• Probability of a false negative

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Asymptotic Analysis

• Analyze throughput in extreme cases
• As probability of sleeping s?0, s?1
• Analyze throughput as system parameters vary
• Database size
• Update frequency
• Bandwidth
• Etc.

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Workaholics

• Unit sleeps less and less s?0
• All hit ratios approach the same value
• SIG lags behind TS and AT by a factor of
• BEST THROUGHPUT
• AT, because its report is the shortest

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Sleepers

• Unit sleeps more and more s?1
• All hit ratios approach 0
• BEST THROUGHPUT
• No Caching eventually wins as s becomes very
  large
• For practical purposes, SIG is the best choice

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Infrequent Updates

• Effectiveness as s ranges from 0 to 1

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Increase Database Size Bandwidth

• Effectiveness as s ranges from 0 to 1

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Update Intensive

• Effectiveness as s ranges from 0 to 1

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Increase Database Size Bandwidth

• Effectiveness as s ranges from 0 to 1

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Conclusions on Effectiveness

• Strategy depends on circumstances
• SIG is best for sleepers
• TS is best for query-intensive scenarios, but
• AT is best for workaholics
• How can we improve effectiveness?

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Relax Consistency of the Cache

• Depending on data type, data may not need to be
  exact
• EX stocks, weather, etc.
• Makes shorter invalidation reports possible

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How Do We Decide to Update?

• - Consider cached copies to be quasi-copies
• - Each quasi-copy has a coherency condition
  attached to it
• Coherency Conditions
• Delay Condition - updated based on time
• Arithmetic Condition - updated based on
  difference between data and quasi-copy

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Adaptive Invalidation Reports

• -Start with TS strategy
• Use algorithms to optimize strategy.
• Examples
• If an item is queried very often by units that
  sleep a lot, include it in reports for longer
• If an item changes frequently, do not bother
  caching

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Criticism

• Units rarely powered down
• Battery life better than predicted
• Battery life does not dictate use
• Units still lose reception frequently
• Todays most common sleeper condition --
  explicitly excluded from definition in SW
• Bandwidth better than predicted

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However

• Adjust sleeper to include lost reception
• Caching is still important
• Endless demand for computational resources
• Endless demand for battery life
• Endless demand for more bandwidth