sleepers & workaholics caching strategies in mobile computing dr. daniel barbará dr. tomasz...
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Sleepers & Workaholics
Caching Strategies in Mobile ComputingDr. 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 & WorkaholicsCaching 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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199416 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:
1. COMPUTATIONAL RESOURCES
2. BATTERY LIFE
3. 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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AnalysisCalculate 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
€
=LW − BC
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AnalysisT = 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
€
=T(1− h)(bq + ba )
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Throughput
€
T(1− h)(bq + ba ) = LW − BC
€
T =LW − BC
(1− h)(bq + ba )
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Effectiveness of a Strategy
€
e =T
Tmax
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Maximal ThroughputServer knows:-What units are in the cell-What those units have in their cachesServer can:
-instantaneously notify units when an item changes
€
BC = 0
€
h = MaximalHitRatio
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Maximal Hit Ratio
The Hit Ratio achieved in ideal conditions:
€
MHR = λe−λτ e−μτ dτ0
∝
∫
€
MHR =λ
λ + μ
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Maximal Throughput
€
Tmax =LW
(1− M .H.R.)(bq + ba )€
BC = 0
€
h = MaximalHitRatio
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No Caching-No invalidation report
-No intervals
€
BC = 0
€
h = 0
€
Tnc =LW
(bq + ba )
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Timestamps
€
TTS =LW − nc (log(n) + bT )
(bq + ba )(1− hts)
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Amnesic Terminals
€
TAT =LW − nL log(n)
(bq + ba )(1− hat )
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Signatures
Consider the probability of false diagnosis: Probability of a false positive Probability of a false negative
€
TSIG =LW − 6g( f +1)(ln(
1
δ) + ln(n))
(bq + ba )(1− hsig )
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Asymptotic AnalysisAnalyze throughput in extreme cases: As probability of sleeping s0, s1
Analyze throughput as system parameters vary: Database size Update frequency Bandwidth Etc.
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WorkaholicsUnit sleeps less and less: s0 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
€
pnf
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SleepersUnit sleeps more and more: s1 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 Today’s most common “sleeper” condition --
explicitly excluded from definition in S&W 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