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 Networks

Consist of hand-held personal mobile devicesLaptops, PDAs, Smartphones

Opportunistic 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 data

Publish/SubscribeBrokers forward data to users according to their

subscriptions

CachingDetermining appropriate caching location/policyThe freshness of cached data is generally ignored

Our Focus

Maintaining the freshness of cached dataData may be periodically refreshed by the source

Daily news, weather report

Data cached at remote locations may be out-of-date!

Major challengesObtaining 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

Models

Network modelPairwise inter-contact time: exponentially distributed

Cache freshness model

Probabilistic model determined by and p

Data 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 Scenario

Query and responseRequester locally stores the query, which is satisfied

when the requester contacts some node caching dataAfterwards, requester caches data locally

Data Access Tree (DAT) Each node only has knowledge

about data cached at its children

Basic Idea

Distributed and hierarchical refreshingIntentional 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

Datasets

Categorized web news from multiple websites11 RSS feeds from CNN, New York Times, BBC,

Google News, etc3-week period over 7 categories of news

Distribution of Inter-Refreshing Time

Aggregate distributionMixture of exponential and power-law distributionsDistinct boundary

Distribution of Inter-Refreshing Time

Distributions of individual RSS feedsSimilar characteristics with that of aggregate

distributionHeterogeneous boundaries

Temporal Variations

Temporal 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 Refreshing

Analytically ensure that the freshness requirement of cached data can be satisfiedCalculating the utility of data updatesOpportunistic replication of data updates

Utility of Data Updates

B updates its children D in DAT:

The probability to satisfy D’s freshness requirement

Utility of Data Updates

Exponential 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 deliver

the data update on time from S to B

Opportunistic Refreshing

Opportunistically 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 cached

data 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 Settings

Realistic 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 Parameters

Varying the parameter

Smaller is more difficult to be satisfied, and incurs higher overhead

Temporal Variations

DieselNet trace, hours,

query time constraint T = 10 hours

Transient performance of maintaining cache freshness expressed significant heterogeneity

Summary

Maintaining 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