Sleepers & Workaholics Caching Strategies in Mobile Computing Dr. Daniel Barbará Dr. Tomasz Imielinski
About Me Peter Rosegger 5th year Computer Science Specialization: Databases Graduation: December 2007
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
million cellular subscribers in US
1994
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
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
Why Caching is Important Conserve: 1.COMPUTATIONAL RESOURCES 2.BATTERY LIFE 3.BANDWIDTH
Traditional Strategies Fail Server lacks knowledge of: Which units are in its cell Which units are powered ON Client caches cannot be tracked
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."
Strategies Timestamps (TS) Amnesic Terminals (AT) Signatures (SIG) Control Strategy: No Cache (NC)
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
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
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CLIENT: Item in cache is declared invalid if it belongs to “too many” unmatching signatures (suspected of being out of date)
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
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
Throughput
Effectiveness of a Strategy
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
Maximal Hit Ratio The Hit Ratio achieved in ideal conditions:
Maximal Throughput
No Caching -No invalidation report -No intervals
Timestamps
Amnesic Terminals
Signatures Consider the probability of false diagnosis: Probability of a false positive Probability of a false negative
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.
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
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
Infrequent Updates Effectiveness as s ranges from 0 to 1
Increase Database Size & Bandwidth Effectiveness as s ranges from 0 to 1
Update Intensive Effectiveness as s ranges from 0 to 1
Increase Database Size & Bandwidth Effectiveness as s ranges from 0 to 1
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?
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
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
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
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
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