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EcoRep: An Economic Incentive Model for Mobile-P2P networks. Anirban Mondal (University of Tokyo, JAPAN) Sanjay K. Madria (University of Missouri-Rolla, USA) Masaru Kitsuregawa (University of Tokyo, JAPAN). Contact Email address: anirban@tkl.iis.u-tokyo.ac.jp. INTRODUCTION.

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ecorep an economic incentive model for mobile p2p networks

EcoRep: An Economic Incentive Model for Mobile-P2P networks

Anirban Mondal (University of Tokyo, JAPAN)

Sanjay K. Madria (University of Missouri-Rolla, USA)

Masaru Kitsuregawa (University of Tokyo, JAPAN)

Contact Email address: anirban@tkl.iis.u-tokyo.ac.jp

slide2

INTRODUCTION

Ever-increasing popularity and proliferation of mobile technology

Mobile user statistics for JAPAN Jan 31, 2006 (http://www.wirelesswatch.jp/)

slide4

Proliferation of mobile devices

+

Popularity of the P2P paradigm e.g., Kazaa

M-P2P Paradigm

slide5

Proliferation of mobile devices

+

Popularity of the P2P paradigm e.g., Kazaa

M-P2P Paradigm

slide6

Proliferation of mobile devices

+

Popularity of the P2P paradigm e.g., Kazaa

M-P2P Paradigm

  • M-P2P network: Mobile Hosts (MHs) interact in a P2P fashion
  • Sometimes, base station infrastructure does not exist
  • Current infrastructures are beginning to support P2P interactions among mobile devices e.g., Microsoft’s Zune
slide8

M-P2P APPLICATION SCENARIOS

Find the cheapest Levis Jeans in a shopping district

slide9

M-P2P APPLICATION SCENARIOS

Find the cheapest steak restaurant nearby me

slide11

M-P2P APPLICATION SCENARIOS

Which museum room do I visit next?

slide13

M-P2P APPLICATION SCENARIOS

What are the traffic conditions a few miles ahead?

challenges in m p2p networks
Challenges in M-P2P networks

Low data availability

  • frequent network partitioning due to mobility
challenges in m p2p networks1
Challenges in M-P2P networks

Dynamic data replication

Low data availability

  • frequent network partitioning due to mobility
challenges in m p2p networks2
Challenges in M-P2P networks

Dynamic data replication

Low data availability

  • frequent network partitioning due to mobility

Free-riding (limited resources of MHs)

challenges in m p2p networks3
Challenges in M-P2P networks

Dynamic data replication

Low data availability

  • frequent network partitioning due to mobility

Free-riding (limited resources of MHs)

Economic Incentive model

challenges in m p2p networks4
Challenges in M-P2P networks

Dynamic data replication

Low data availability

  • frequent network partitioning due to mobility

Free-riding (limited resources of MHs)

Economic Incentive model

This motivates us to investigate an economic incentive model for dynamic replication in Mobile-P2P networks.

main contributions
Main contributions
  • An economic model for M-P2P networks
    • A query issuing mobile peer pays the price of the service to the query serving mobile peer
    • Virtual currency model
    • Discourages free-riding
  • Fairness in replica allocation
    • by considering the origin of queries for data items
related works
Related Works
  • Economic models have been discussed primarily for resource allocation in distributed systems.
    • They do not address fairness in replica allocation and P2P concerns such as free-riding.
    • They do not address M-P2P issues such as frequent network partitioning and mobile resource constraints.
  • [Ouri:04] has proposed an M-P2P economic model
    • [Ouri:04] aims at data dissemination, while we consider on-demand services.
    • [Ouri:04] does not consider replication.
  • Works on free-riding discuss utility functions to capture user contributions and trust issues
    • These works are completely orthogonal to replication issues associated with free-riding.
  • Existing P2P replication protocols are not adequate for M-P2P due to mobility issues.
  • [Hara:05] presents M-P2P replica allocation methods with periodic and aperiodic updates
    • [Hara:05] does not consider economic issues, load sharing and tolerance to weaker consistency.
architecture of ecorep
ARCHITECTURE OF EcoRep
  • EcoRep considers a hybrid super-peer architecture
    • some of the MHs act as the ‘Super-peers’ (SPs).
  • SPs have high processing capacity, high available bandwidth and high energy.
  • Neighbouring SPs periodically exchange their regional information concerning MH characteristics (e.g., load, energy) to facilitate replication.
  • In case of SP failures, neighbouring GNs could take over the responsibility of the failed GN.
  • SPs can also collaborate for search and replication across different regions.
query processing in ecorep
QUERY PROCESSING IN EcoRep
  • When an MH enters a region R, it registers with the SP S in R.
    • S provides the MH with the list of data items currently available in R.
  • EachMH periodicallysends its list of data items and replicas

to its corresponding SP.

  • SP periodically broadcasts the list of available items within its region to the MHs in its region.
    • A query issuing MH M can distinguish whether its query is local or global.
  • EcoRep supports both local and remotequerying.
    • Local queries: Broadcast mechanism (need not pass via SP)
    • Remote queries: SP forwards query to its neighbouring SPs.
core idea
Core idea
  • Services
    • providing data
    • providing computational power e.g., convert to PDF
    • message relay services
  • Every service has a price
    • Service-requestor pays the price of the service to the service-provider.
  • Revenue of an MH is how much currency it has
    • MH spends currency on obtaining services
    • MH earns currency by providing services
computation of data item price
Computation of data item price
  • Price of data item d depends on
    • access frequency
computation of data item price1
Computation of data item price
  • Price of data item d depends on
    • access frequency
    • number of MHs served by d (fairness issue)
computation of data item price2
Computation of data item price
  • Price of data item d depends on
    • access frequency
    • number of MHs served by d (fairness issue)
    • number of existing replicas of d
computation of data item price3
Computation of data item price
  • Price of data item d depends on
    • access frequency
    • number of MHs served by d (fairness issue)
    • number of existing replicas of d
    • (replica) consistency of d
computation of data item price4
Computation of data item price
  • Price of data item d depends on
    • access frequency
    • number of MHs served by d (fairness issue)
    • number of existing replicas of d
    • (replica) consistency of d
    • average response time for queries on d
computation of data item price5
Computation of data item price
  • Price of data item d depends on
    • access frequency
    • number of MHs served by d (fairness issue)
    • number of existing replicas of d
    • (replica) consistency of d
    • average response time for queries on d
interaction between revenue and load
Interaction between revenue and load
  • MH M could have high revenue but low load due to
    • serving only a few requests for some high-priced data items, but not issuing any queries
  • M could have low revenue but high load due to
    • serving a large number of access requests for low-priced data items
  • Even if M earns high amounts of virtual currency, M’s revenue could still be low if M issues several queries for high-priced data items.
interaction between revenue and load1
Interaction between revenue and load
  • MH M could have high revenue but low load due to
    • serving only a few requests for some high-priced data items, but not issuing any queries
  • M could have low revenue but high load due to
    • serving a large number of access requests for low-priced data items
  • Even if M earns high amounts of virtual currency, M’s revenue could still be low if M issues several queries for high-priced data items.

There is no direct correlation between the revenue and load of an MH.

revenue and load in ecorep
Revenue and Load in EcoRep
  • We use a parameter ג that can be tweaked to adjust the relative importance of revenue and load during replica allocation.
  • We use normalized values of revenue and load to correctly reflect the relative weights of revenue and load.
  • We consider three cases:
    • Revenue and load are both assigned equal weights: ג = R + L
    • Revenue is assigned higher weight than load: ג = 2R + L
    • Revenue is assigned lower weight than load: ג = R + 2L
ecorep replica allocation
EcoRep replica allocation
  • Each SP performs replica allocation within the region that it covers.
  • Periodically, each MH sends to its SP
    • current (x,y) coordinates
    • revenue value
    • the prices of items stored at itself
    • load
    • energy
    • available memory space status
  • SP collates the (x,y) coordinate information of all the MHs in its region to estimate the network topology during the time of replica allocation.
  • 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
ecorep replica allocation cont
EcoRep Replica Allocation (CONT.)
  • 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
slide36

Replica allocation algorithm

Higher-priced data items are given preference

slide37

Replica allocation algorithm

{

Bringing the data nearer to the origin of most of the requests for the data

slide38

Replica allocation algorithm

{

Consideration of memory space, energy, load and probability of availability of MHs

slide39

Replica allocation algorithm

Revenue-balance and load-balance

slide40

Replica allocation algorithm

Recomputing the price of data items after replica allocation as price depends upon no. of existing replicas

performance study
Performance Study
  • Metrics
  • Average Response time ART
  • Data Availability
  • Traffic (hop-count) during replica allocation
practical deployment issues
Practical deployment issues
  • What should be the exchange rate between virtual money and real money?
    • 1000 units of virtual currency = ? Yen
  • How to ensure collection of payments?
    • Escrow method??
  • Should real money be used?
    • High cost of micro-economic transactions
  • Virtual money should work as long as it is of value to M-P2P users
    • Example: MTV could give Bob 50 units of virtual money if he agrees to stream a video-clip in a busy market-place 25 times on a Sunday. Bob could buy some MTV products using the 50 units he obtains.
summary
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