Distributed Maintenance of Cache Freshness in Opportunistic Mobile Networks
Wei Gao and Guohong CaoDept. of Computer Science and EngineeringPennsylvania State University
Mudhakar Srivatsa and Arun IyengarIBM T. J. Watson Research Center
Outline
IntroductionRefreshing Patterns of Web ContentsCache Refreshing SchemesPerformance EvaluationSummary & Future Work
Opportunistic Mobile NetworksConsist of hand-held personal mobile devices
Laptops, PDAs, SmartphonesOpportunistic and intermittent network
connectivityResult of node mobility, device power outage, or
malicious attacksHard to maintain end-to-end communication links
Data transmission via opportunistic contactsCommunication opportunity upon physical proximity
Methodology of Data Transmission
Carry-and-ForwardMobile nodes physically carry data as relaysForwarding data opportunistically upon contactsMajor problem: appropriate relay selection
B
A C
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Providing Data Access to Mobile Users
Active data disseminationData source actively push data to users being
interested in the dataPublish/Subscribe
Brokers forward data to users according to their subscriptions
CachingDetermining appropriate caching location/policyThe freshness of cached data is generally ignored
Our FocusMaintaining the freshness of cached data
Data may be periodically refreshed by the source Daily news, weather report
Data cached at remote locations may be out-of-date!Major challenges
Obtaining information of cached data Where data is cached? What is the current version of cached data?
Timeliness of refreshing cached data Uncertainty of opportunistic data transmission
ModelsNetwork model
Pairwise inter-contact time: exponentially distributedCache freshness model
Probabilistic model determined by and pData update model
Version of data cached at node j at time t
Version of source data in the past
Difference between data version i and j
Version i of the data
Caching ScenarioQuery and response
Requester locally stores the query, which is satisfied when the requester contacts some node caching data
Afterwards, requester caches data locallyData Access Tree (DAT)
Each node only has knowledgeabout data cached at its children
Basic IdeaDistributed and hierarchical refreshing
Intentional refreshing A node only refreshes data cached at its children in the DAT Appropriate data updates are applied
Opportunistic refreshing A node refreshes any cached data
with old versions upon contact Complete data is transmitted
Outline
IntroductionRefreshing Patterns of Web ContentsCache Refreshing SchemesPerformance EvaluationSummary & Future Work
DatasetsCategorized web news from multiple websites
11 RSS feeds from CNN, New York Times, BBC, Google News, etc
3-week period over 7 categories of news
Distribution of Inter-Refreshing TimeAggregate distribution
Mixture of exponential and power-law distributionsDistinct boundary
Distribution of Inter-Refreshing TimeDistributions of individual RSS feeds
Similar characteristics with that of aggregate distribution
Heterogeneous boundaries
Temporal VariationsTemporal distribution of news updates over
different hours in a dayHeterogeneity over different RSS feedsSignificant heterogeneity
Outline
IntroductionRefreshing Patterns of Web ContentsCache Refreshing SchemesPerformance EvaluationSummary & Future Work
Intentional RefreshingAnalytically ensure that the freshness requirement
of cached data can be satisfiedCalculating the utility of data updatesOpportunistic replication of data updates
Utility of Data UpdatesB updates its children D in DAT:
The probability to satisfy D’s freshness requirement
Utility of Data UpdatesExponential distribution
Pareto distribution
The last time B contacts D
The minimum value of data inter-refreshing time
Incomplete Gamma function
Opportunistic Replication of Data Updates
Replicate data updates to non-DAT relaysThe k selected relays satisfy:
At least one relay could deliverthe data update on time from S to B
Opportunistic RefreshingOpportunistically update data with old versions
upon contactFurther improve freshness of cached data
Probabilistic decisionComplete data needs to be transmittedData is only refreshed if the required freshness cannot
be satisfied by intentional refreshingThe probability for opportunistic refreshing:
Opportunistic refreshing Intentional refreshing
Side-Effect of Opportunistic Refreshing
May hinder intentional refreshing in the futureInconsistency among different cached data copiesA updates D’s cached data from
d1 to d3
B cannot update D’s cacheddata to d4 using u14
Node A estimates chance of side-effect A newer version of data has already arrived B
Outline
IntroductionRefreshing Patterns of Web ContentsCache Refreshing SchemesPerformance EvaluationSummary & Future Work
Experimental SettingsRealistic mobile network traces
Data generation4 realistic RSS feeds, random nodes as data sources
Query generationRandomly generated at all nodesFollows Zipf distribution over the 4 RSS feeds
Performance of Maintaining Cache Freshness
Infocom trace, hours, query time constraint T = 5 hours
Our hierarchical refreshing scheme achieves higher refreshing ratio, shorter refreshing delay, and less refreshing overhead
Variation of ParametersVarying the parameter
Smaller is more difficult to be satisfied, and incurs higher overhead
Temporal VariationsDieselNet trace, hours,
query time constraint T = 10 hours
Transient performance of maintaining cache freshness expressed significant heterogeneity
SummaryMaintaining cache freshness in opportunistic
mobile networksProbabilistic cache freshness modelExperimental investigation on refreshing patterns of
realistic web contentsApproach to hierarchical and distributed maintenance
Future workExploitation of temporal variations of data refreshing
patterns
Thank you!
http://mcn.cse.psu.edu
The paper and slides are also available at:http://www.cse.psu.edu/~wxg139