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

0.7

0.5

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Data cached at remote locations may be out-of-date!Major challenges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