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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

- 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

- 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

Grid

Resource

base

VPGR

GR

GR

GR

multiplex

Grid

Domain

Grid

Resource

Grid

Resource

Grid

Resource

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

- Assumptions
- Resource owners have committed their resources
- Honestly
- To be used efficiently
- To be used for the overall good of the community

- Resource owners have committed their resources
- Considerations
- Free riding
- Malicious entities
- Non cooperative entities
Incentives are needed for resources to cooperate honestly

- 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

- We do not want security to become an overhead!

- 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

- Identity trust
- Behavior trust
- Honesty
- Accuracy
- Set of recommenders
- Set of trusted allies

- 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

- 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

- Let
- The value of will be less than a small value if recommender is honest
- Therefore, is computed as

- 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

- 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

- Trust relationship expressed as
- Direct trust relationship and the reputation of expressed as and ,respectively.
- The decay function is expressed as
- Let and

- 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]

- 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:

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

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

- Using Accuracy & honesty measures: Success ratio progress

- 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

- 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

- An example of the ETS table

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

- 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

- Consistent Low task low machine (LOLO) heterogeneity