Distributed Maintenance of
Cache Freshness in
Opportunistic Mobile Networks
Wei Gao and Guohong Cao
Dept. of Computer Science and Engineering
Pennsylvania State University
Mudhakar Srivatsa and Arun Iyengar
IBM T. J. Watson Research Center
Outline
 Introduction
 Refreshing Patterns of Web Contents
 Cache Refreshing Schemes
 Performance Evaluation
 Summary & Future Work
Opportunistic Mobile Networks
 Consist of hand-held personal mobile devices
Laptops, PDAs, Smartphones
 Opportunistic and intermittent network
connectivity
Result of node mobility, device power outage, or
malicious attacks
Hard to maintain end-to-end communication links
 Data transmission via opportunistic contacts
Communication opportunity upon physical proximity
Methodology of Data Transmission
 Carry-and-Forward
 Mobile nodes physically carry data as relays
 Forwarding data opportunistically upon contacts
 Major problem: appropriate relay selection
B
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A
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C
Providing Data Access to Mobile Users
 Active data dissemination
Data source actively push data to users being
interested in the data
 Publish/Subscribe
Brokers forward data to users according to their
subscriptions
 Caching
Determining appropriate caching location/policy
The freshness of cached data is generally ignored
Our Focus
 Maintaining 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Data cached at remote locations may be out-of-date!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
Models
 Network model
Pairwise inter-contact time: exponentially distributed
 Cache freshness model

Version of source data in the past
Version of data cached at node j at time t
Probabilistic model determined by
 Data update model


and p
Version i of the data
Difference between
data version i and j
Caching Scenario
 Query and response
Requester locally stores the query, which is satisfied
when the requester contacts some node caching data
Afterwards, requester caches data locally
 Data Access Tree (DAT)
Each node only has knowledge
about data cached at its children
Basic Idea
 Distributed 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
 Introduction
 Refreshing Patterns of Web Contents
 Cache Refreshing Schemes
 Performance Evaluation
 Summary & Future Work
Datasets
 Categorized 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 Time
 Aggregate distribution
Mixture of exponential and power-law distributions
Distinct boundary
Distribution of Inter-Refreshing Time
 Distributions of individual RSS feeds
Similar characteristics with that of aggregate
distribution
Heterogeneous boundaries
Temporal Variations
 Temporal distribution of news updates over
different hours in a day
Heterogeneity over different RSS feeds
Significant heterogeneity
Outline
 Introduction
 Refreshing Patterns of Web Contents
 Cache Refreshing Schemes
 Performance Evaluation
 Summary & Future Work
Intentional Refreshing
 Analytically ensure that the freshness requirement
of cached data can be satisfied
Calculating the utility of data updates
Opportunistic 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
The last time B
contacts D
 Pareto distribution
The minimum value of data
inter-refreshing time
Incomplete Gamma function
Opportunistic Replication of Data Updates
 Replicate data updates to non-DAT relays
The 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 contact
Further improve freshness of cached data
 Probabilistic decision
Complete data needs to be transmitted
Data is only refreshed if the required freshness cannot
be satisfied by intentional refreshing
The probability for opportunistic refreshing:
Opportunistic refreshing
Intentional refreshing
Side-Effect of Opportunistic Refreshing
 May hinder intentional refreshing in the future
Inconsistency among different cached data copies
A 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
 Introduction
 Refreshing Patterns of Web Contents
 Cache Refreshing Schemes
 Performance Evaluation
 Summary & Future Work
Experimental Settings
 Realistic mobile network traces
 Data generation
4 realistic RSS feeds, random nodes as data sources
 Query generation
Randomly generated at all nodes
Follows 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 networks
Probabilistic cache freshness model
Experimental investigation on refreshing patterns of
realistic web contents
Approach to hierarchical and distributed maintenance
 Future work
Exploitation 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