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An Economic Incentive Model for Dynamic Replication in Mobile-P2P networks

An Economic Incentive Model for Dynamic Replication in 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 Email address: anirban@tkl.iis.u-tokyo.ac.jp.

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An Economic Incentive Model for Dynamic Replication in Mobile-P2P networks

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  1. An Economic Incentive Model for Dynamic Replication in 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 Email address: anirban@tkl.iis.u-tokyo.ac.jp

  2. INTRODUCTION Ever-increasing popularity and proliferation of mobile technology Mobile user statistics for JAPAN Jan 31, 2006 (http://www.wirelesswatch.jp/)

  3. M-P2P network: Mobile Hosts (MHs) interact in a P2P fashion • Sometimes, base station infrastructure does not exist • Scalability: P2P interactions are more scalable • Problem with M-P2P networks: Low data availability due to frequent network partitioning  Data replication is necessary • Difference from traditional replication: • Frequent network partitioning • Limited resources of MHs • Nodes may not be willing to store replicas  incentive model

  4. APPLICATION SCENARIOS • Customers in a shopping mall sharing information • about the cheapest available `Levis' jeans • Mobile users could share information about the cheapest price of steak across nearby restaurants • Visitors to a museum could request images/video-clips of different rooms of the museum • Tourists in different sight-seeing buses could share • touristic information (e.g., images of castles) with • each other. (inter-vehicular communication) Super-peer architecture is possible in these applications e.g., shopping mall administrative staff, tourist guides could be super-peers.

  5. Main contributions Proposal of a dynamic economic incentive replica allocation algorithm for M-P2P networks • Fairness in replica allocation by considering the origin of queries for data items • Discouraging free-riding due to the incentive nature of the model

  6. Core idea • Every data item has a price (virtual currency) • The user accessing the data item pays the price of the data item to the user who provides it. • Incentive for users to provide service to the system • Price of data item d depends on • access frequency • number of MHs served by d • number of existing replicas of d • (replica) consistency of d • average response time for queries on d. • Revenue of an MH is how much currency it has

  7. Core idea (Cont.) • We consider load, bandwidth heterogeneity, service capacity heterogeneity. • We deploy a super-peer architecture. • The algorithms provide revenue and load-balance • Revenue-balance avoids starvation of MHs and encourages MH participation in the network • Load-balance reduces query response times

  8. REPLICATION ISSUES • Replica allocation is performed by super-peer. • Key idea: Assign higher-priced data items to MHs with either low revenue or low load (spectrum of algorithms with different weights for revenue and load). • Replica allocation criteria • Revenue • Load • k-hop neighbours of MH which access the data max number of times • Available memory space • Probability of MH availability • Query redirection to replicas is based on • Revenue • Load • Probability of MH availability

  9. Replica allocation algorithm

  10. Performance Study • Metrics • Average Response time ART • Data Availability • Traffic (hop-count) during replica allocation

  11. Performance of our scheme Effect of variations in the workload skew

  12. Effect of variations in the replica allocation period Effect of variations in the number of mobile devices

  13. Our related works on incentive M-P2P model • Collaborative replica allocation as well as deallocation in M-P2P networks (to be presented at IDEAS 2006 conference) • Useful especially for relatively larger-sized data items • A bid-based model for M-P2P service (data and computational power sharing) • Relay MHs provide value-added routing services by maintaining indexes and earn a broker’s commission • Distributed indexing by brokers • Price of services is dynamically decided by bidding • In the absence of incentives, MHs do not have any incentive to cache query paths for frequent queries and to index data items at other MHs. • Convertibility between virtual currency and real currency • Relates to the feasibility of an incentive M-P2P business model • Practical deployment issues of M-P2P incentive models • Micro-economic transaction processing • Trust issues in P2P including ephemeral M-P2P environments (Presented as invited talk in PDMST 06 workshop) • Royalty model for M-P2P networks • An MH could ask another MH to store its data item in lieu of a royalty payment.

  14. SUMMARY • A mobile peer needs incentives to provide services to other mobile peers • Incentives are likely to improve participation of mobile peers  higher available bandwidth, larger pool of memory space, multiple paths to answer a query etc • Our works aim at enticing non-cooperative peers to provide service in M-P2P networks

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