distributed maintenance of cache freshness in opportunistic mobile networks wei gao and guohong cao...
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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Data cached at remote locations may be out-of-date!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