Incentive mechanisms for large collaborative resource sharing
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Objectives: Why Resource harnessing Examples of resource harnessing Grid computing P2P computing Resource sharing Assumptions Considerations What are incentives? Trust as a mechanism to provide incentives. Incentive Mechanisms for Large Collaborative Resource Sharing.

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Incentive Mechanisms for Large Collaborative Resource Sharing

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Incentive mechanisms for large collaborative resource sharing

Objectives:

Why Resource harnessing

Examples of resource harnessing

Grid computing

P2P computing

Resource sharing

Assumptions

Considerations

What are incentives?

Trust as a mechanism to provide incentives

Incentive Mechanisms for Large Collaborative Resource Sharing


Resource harnessing

Resource Harnessing

  • Huge interest in linking up resources

    • Grid computing, P2P computing, computing utilities, etc.

  • It is all about sharing

    • Quality of Service

    • Security

  • Participation versus Cost


Resource harnessing grid example

Resource Harnessing: Grid Example

  • Virtual Private Grids (PVG) is a framework for “renting” collection of resources

  • “Collection” is defined as follows:

    • able to deliver predefined performance metrics

    • performance delivered at predefined geographical locations

    • cost of provisioning is optimized or bounded


Resource harnessing grid example1

Grid

Resource

base

Resource Harnessing: Grid Example

VPGR

GR

GR

GR

multiplex

Grid

Domain

Grid

Resource

Grid

Resource

Grid

Resource


Resource harnessing grid example2

Resource Harnessing: Grid Example

SO

  • SO (service originator) presents the VPG Spec. via a VPG Manager (VPGM)

  • VPGM negotiates with different Grids via a MetaGrid Resolver (MGR)

  • Grids (GRs) bid for the VPG creation requests

  • VPGM selects the best bid

  • Location spec

  • QoS specs

  • Cost preference

VPGS

VPGM

Contract negotiation

Admission

Control

MGR

bid with (QoS/cost)

VPG

creation

request

Grid

Engineering

GR

GR

……

GR


Resource sharing

Resource Sharing

  • Assumptions

    • Resource owners have committed their resources

      • Honestly

      • To be used efficiently

      • To be used for the overall good of the community

  • Considerations

    • Free riding

    • Malicious entities

    • Non cooperative entities

      Incentives are needed for resources to cooperate honestly


Resource harnessing p2p example

Resource Harnessing: P2P Example

  • Since, we deal with public resources, we need to address the following

  • How can we encourage resources to cooperate

    • 70% of all users do not share files

    • 50% of all requests are satisfied by the top 1% sharing hosts

  • How can we deal with security

    • We do not want security to become an overhead!

  • Can we use “trust” as an incentive?


  • Trust considerations

    Trust Considerations

    • How can we define “trust” in an operational way? Who will evaluate trust?

    • Trust maintenance can result in an efficient process especially in a very large-scale system. Hence, our task is to come up with an efficient model for maintaining trust

    • Techniques for managing and evolving trust in a large-scale distributed system

    • Mechanisms for maintaining trust from ongoing transactions


    Overall trust model

    Overall Trust Model


    Trust terminology

    Trust Terminology

    • Identity trust

    • Behavior trust

    • Honesty

    • Accuracy

    • Set of recommenders

    • Set of trusted allies


    Trust model characteristics

    Trust Model Characteristics

    • To make the trust model efficient

      • the overall NC system is divided into NCDs

      • trust is a slow varying attribute

      • the number of contexts is limited to printing, storage, and computing


    Why behavior trust

    Why Behavior Trust


    Notation

    Notation

    • Let and represent recommenders set and trusted allies set, respectively

    • Let the honesty of recommender as observed by be denoted as

    • Let denote the recommendation for given by to at time for context

    • Let denote the recommendation for given by to where for the same and


    Computing honesty

    Computing Honesty

    • Let

    • The value of will be less than a small value if recommender is honest

    • Therefore, is computed as


    Computing accuracy

    Computing Accuracy

    • Let denote the true trust level of obtained by as a results of monitoring the transaction

    • Let

    • The value of will be an integer value ranging from 0 to 4

    • Therefore, is computed as


    Computing trust reputation

    Computing Trust & Reputation

    • Before can use the recommendation given by to calculate the reputation of , needs to be adjusted to reflect the accuracy of recommender

    • This shift is given by


    Computing trust reputation1

    Computing Trust & Reputation

    • Trust relationship expressed as

    • Direct trust relationship and the reputation of expressed as and ,respectively.

    • The decay function is expressed as

    • Let and


    Simulation setup

    Simulation Setup

    • A discrete event simulator was used

    • The transactions arrival process modeled using a Poisson random process

    • 30 NCDs were used in the simulation

    • The size of R is fixed and set to 4

    • The size of T is fixed and set to 3

    • The TL were randomly generated from [1-5]


    Performance measurement

    Performance Measurement

    • The measure of performance used is the ability of the trust model to correctly predict the trust that exists between two NCDs

    • This is quantified by determining the success ratio as follows:


    Performance evaluation

    Performance Evaluation

    • Using accuracy & honesty measures: Success ratio with 150 transactions per relation


    Performance evaluation1

    Performance Evaluation

    • Using the accuracy measure: Success ratio with 150 transactions per relation


    Performance evaluation2

    Performance Evaluation

    • Using Accuracy & honesty measures: Success ratio progress


    Case study trust modeling on p2p grids

    Case Study: Trust Modeling on P2P Grids

    • The P2P Grid is segmented into Grid domains (GDs)

    • Two virtual domains are associated with each GD

      • resource domain and client domain

    • Each resource domain has 3 attributes:

      • Ownership

      • Type of Activities (ToA) it supports

      • TL for each ToA

    • Similarly, each client domain has 3 attributes


    Case study trust modeling on p2p grids1

    Case Study: Trust Modeling on P2P Grids

    • Suppose that client from wanting to engage in activities and on resource at

    • Offered TL (OTL) = min(TL for , TL for )

    • There are two required TLS (RTLs)

      • one from the client domain

      • one from the resource domain

    • Expected trust supplement (ETS) = RTL - OTL


    Case study trust modeling on p2p grids2

    Case Study: Trust Modeling on P2P Grids

    • An example of the ETS table


    Case study trust modeling on p2p grids3

    Case Study: Trust Modeling on P2P Grids

    • A batch mode mapping heuristic called “Sufferage heuristic” was used


    Case study trust modeling on p2p grids4

    Case Study: Trust Modeling on P2P Grids

    • Two different classes of Expected Execution Cost (EEC) were used:

      • Consistent Low task low machine (LOLO) heterogeneity

        • models networks that have “related” machines which are “similar” in performance

      • Inconsistent Low task low machine (LOLO) heterogeneity

        • models networks were machines are not related


    Case study performance evaluation

    Case Study: Performance Evaluation


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