1. Anirban Mondal (IIS, University of Tokyo, JAPAN)
CLEAR: An Efficient Context and Location-based Dynamic Replication Scheme for Mobile-P2P Networks
Anirban Mondal (IIS, University of Tokyo, JAPAN)
Sanjay K. Madria (University of Missouri-Rolla, USA)
Masaru Kitsuregawa (IIS, University of Tokyo, JAPAN)
Contact address:

2. PRESENTATION OUTLINE INTRODUCTION PROBLEM CONTEXT
REPLICA ALLOCATION ALGORITHM
PERFORMANCE EVALUATION
RELATED WORKS
CONCLUSION AND FUTURE WORK

3. Ever-increasing popularity and proliferation of mobile technology
INTRODUCTION
Ever-increasing popularity and proliferation of mobile technology

4. Ever-increasing popularity and proliferation of mobile technology
INTRODUCTION
Ever-increasing popularity and proliferation of mobile technology
Mobile user statistics for JAPAN Jan 31, 2006 (
DoCoMo
50,496,200
KDDI/au
21,791,900
Vodafone
15,134,300
TU-KA
3,010,600
TOTAL
90,433,000

5. A maze of mobile devices

6. A maze of mobile devices
Usually, these mobile devices interact via base stations
But sometimes, base station infrastructure does not exist

7. A maze of mobile devices
Usually, these mobile devices interact via base stations
But sometimes, base station infrastructure does not exist
M-P2P network: Mobile Hosts (MHs) interact in a P2P fashion

8. APPLICATION SCENARIOS
A remote area which has no base station infrastructure

9. APPLICATION SCENARIOS
A remote area which has no base station infrastructure
A group of doctors in an earthquake-devastated region
no. of injured people, no. of empty stretchers, no. of blood units
Moving salespersons in the same locality
sales profits, no. of units sold
A group of zoologists looking for data on some micro-organism

10. APPLICATION SCENARIOS
A remote area which has no base station infrastructure
A group of doctors in an earthquake-devastated region
no. of injured people, no. of empty stretchers, no. of blood units
Moving salespersons in the same locality
sales profits, no. of units sold
A group of zoologists looking for data on some micro-organism
Disaster control scenarios, collaborative sales and scientific studies

11. Problem with M-P2P networks
User movement
Users switching ON or OFF their devices

12. Problem with M-P2P networks
User movement
Users switching ON or OFF their devices
Frequent Network Partitioning

13. Problem with M-P2P networks
User movement
Users switching ON or OFF their devices
Frequent Network Partitioning
Reduced Data Availability in M-P2P networks

14. Problem with M-P2P networks
User movement
Users switching ON or OFF their devices
Frequent Network Partitioning
Reduced Data Availability in M-P2P networks
Dynamic replication is needed to improve data availability

15. Main contributions
We envisage the M-P2P network as a cluster of MHs with a cluster head (CH) for data validation and replica allocation
Proposal of CLEAR (Consistency and Load-based Efficient Allocation of Replicas) for improving M-P2P data availability
CLEAR considers replica consistency and load as criteria
CLEAR uses knowledge of moving users' schedules

16. PRESENTATION OUTLINE INTRODUCTION PROBLEM CONTEXT
REPLICA ALLOCATION ALGORITHM
PERFORMANCE EVALUATION
RELATED WORKS
CONCLUSION AND FUTURE WORK

17. Problem Context
We envisage the M-P2P network as a cluster with a cluster head (CH)
Data item sizes and bandwidth across MHs may vary
A data item can be updated ONLY by its owner
Each MH periodically sends to CH
access statistics, update logs (including timestamps) and load, list of data items at itself
LOAD of an MH
Number of queries in the MH’s job queue
Normalized w.r.t. bandwidth
Hybrid architecture which preserves MH autonomy and deploys CH for facilitating data validation and replica allocation

18. Determination of future user access patterns
The objective of replica allocation is to replicate objects of an MH M's interest at MH(s) that would be near to M's future location at the time when M would access the objects
Each MH moves according to some schedule
Each MH sends its schedule to CH
Schedule contains the MH’s location at different points of time and when the MH will access which objects

19. Replica Consistency Absolute replica consistency may not be critical
Desired replica consistency is application-dependent
Ease of replica consistency maintenance should be measured by the percentage change in value of the updated attribute and not from the number of updates
NQDC: Number of Queries (NQ) answered with Desired Consistency (DC)
NQDC = NQ * C if C >= DC, otherwise 0
The value of C is looked up from a pre-existing table

20. PRESENTATION OUTLINE INTRODUCTION PROBLEM CONTEXT
REPLICA ALLOCATION ALGORITHM
PERFORMANCE EVALUATION
RELATED WORKS
CONCLUSION AND FUTURE WORK

21. CLEAR REPLICA ALLOCATION ALGORITHM
The algorithm considers
User mobility patterns
Load
Replica consistency
Available memory space
Cost-benefit of replication

22. Query redirection to replicas
Select a set of underloaded MHs containing the replica
Direct the query to the MH with best NQDC

23. PRESENTATION OUTLINE INTRODUCTION PROBLEM CONTEXT
REPLICA ALLOCATION ALGORITHM
PERFORMANCE EVALUATION
RELATED WORKS
CONCLUSION AND FUTURE WORK

24. Performance Study Parameters used for the performance study
Performance metrics
Average response time (ART) of a query
Percentage Success Ratio (SR) (based on desired consistency
Traffic (i.e., total hop-count) during replica allocation
Reference approaches: E-DCG+ and NoRep

25. Performance of CLEAR CLEAR provides better ART than E-DCG+
Consideration of MH mobility patterns
Load-aware replica allocation
CLEAR provides higher SR than E-DCG+
Consideration of consistency issues (NQDC)
Quicker Updates due to load-aware replica allocation
CLEAR avoids broadcast storm during replica allocation due to SP

26. Effect of variations in workload skew
As the workload skew increases, the effect of CLEAR’s load-based replica allocation becomes more pronounced.

27. Effect of variations in the replica allocation periods
Trade-off between freshness of data and communication cost

28. Effect of variations in write probability
Replica consistency becomes difficult to maintain when
there are more writes

29. Effect of variations in the no. of MHs
More MHs means more opportunities for replication

30. PRESENTATION OUTLINE INTRODUCTION PROBLEM CONTEXT
REPLICA ALLOCATION ALGORITHM
PERFORMANCE EVALUATION
RELATED WORKS
CONCLUSION AND FUTURE WORK

31. Related Works [Fife:03] discusses replication issues in MANETs.
[Pitoura:96] discusses replication in distributed systems with partial, weak and variant connectivity
Existing systems in this area
ROAM
Clique
Rumor
[Hara,Madria] discuss replica allocation in MANETs with periodic and aperiodic updates, and considers
limited MH memory space
access frequencies of data items
network topology

32. Differences from existing works
Traditional replication techniques do not address
frequent network partitioning issues
limited resources of MHs
Mobile Replication techniques mostly assume stationary networks
P2P replication services are not `mobile-ready' as current P2P systems have mostly ignored data transformation, relationships and network characteristics.
[Hara:02] considers frequent network partitioning w.r.t. replication in mobile ad-hoc networks (MANETs)
Differences of our work from [Hara:02]
We use load as a replication criterion
We address different levels of replica consistency
We consider unequal-sized data items.

33. Conclusion and future work
We have proposed the CLEAR scheme for dynamic replication to improve M-P2P data availability
Exploits user mobility patterns
Deploys a super-peer architecture that facilitates replica allocation, while maintaining P2P autonomy.
Considers different levels of replica consistency and load as replica allocation criteria.
Avoids broadcast storm during replica allocation and broadcast-based querying.
Our experiments demonstrate that CLEAR improves M-P2P data availability
Future work
Economic replication in M-P2P networks (incentive schemes)
Top-k query processing in M-P2P networks