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CLIENT: Item in cache is declared invalid if it belongs to “too many” unmatching signatures (suspected of being out of date)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