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

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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

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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

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Grid

Resource

GRGR

Grid Grid

ResourceResourceGridGrid

ResourceResource

GRGRmultiplexmultiplexGRGR

GridGrid

ResourceResource

GridGrid

DomainDomain

base

base

VPGRVPGR

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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

SO

VPGS

VPGM

Location spec QoS specs Cost preference

GR GR GR……

MGR

Contract negotiation

bid with (QoS/cost)VPG

creation request

Grid Engineeri

ng

Admission

Control

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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

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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?

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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

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Overall Trust Model

SourceNCD

RD CD

TargetNCD

TA

RD CD

Reco

mm

end

atio

n

Directrelationship

TA

TATATA

NetworkComputing

Domain

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Trust Terminology

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

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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

Trust Level (TL) Equivalent numerical value

Description

A 1 very low TL

B 2 low TL

C 3 medium TL

D 4 high TL

E 5 very high TL

Trust Model Characteristics

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Why Behavior Trust

Trust Attributes

Identity Behavior

Importance foundation layer

Cost fixed variable

Changeability very seldom yes

Nature given gained

Replacement yes no

Propagation immediate with time

Perception exists learned

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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

z

R T

SD ),( zSDH),,,( ctTDzRESD

TD z SD t c),,,( ctTDzREk

TD z Tkt c

k

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Computing Honesty Let The value of will be less than a small

value if recommender is honest Therefore, is computed as

),( zSDH

T

ctTDzREctTDzRE Tk k

SDRE

),,,(),,,(

RE REz

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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

),,(),,,( ctTDTTLctTDzRE SDSDRE

TTL TDSD

RE

),,,( ctzSDA

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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

TDSD z

),,,( ctTDzRESD

z

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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

( , , , )SD TD t c

( , , )TD t cTD

( , , , )SD TD t c

( )sdt t

( , , , ) ( , , , ) ( , , )SD TD t c SD TD t c TD t c

1

( , , , ) ( , , , ) ( )sdSD TD t c TL SD TD t c t t

, 0

1

1( , , ) ( , ( , , , ) ( ) ,

i

n

RE SD i SD TD ii

TD t c S RE z TD t c t t z Rn

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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]

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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:

1

1( ) (det 1) (det 1) 100

ng bk k

k

SR t NCD ected NCD ectedn

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Performance Evaluation Using accuracy & honesty measures: Success ratio

with 150 transactions per relation

MonitorFrequency value

Number of malicious domains

0 10 20

1 1.0 100% 100% 100%

0.5 100% 100% 100%

0.0 100% 100% 100%

10 1.0 98.39% 92.76% 91.95%

0.5 100% 97.24% 98.51%

0.0 100% 98.04% 99.54%

20 1.0 93.45% 82.98% 81.38%

0.5 99.77% 82.99% 81.72%

0.0 100% 79.54% 78.74%

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Performance Evaluation Using the accuracy measure: Success ratio with 150

transactions per relation

MonitorFrequency value

Number of malicious domains

0 10 20

1 1.0 100% 100% 100%

0.5 100% 100% 100%

0.0 100% 100% 100%

10 1.0 98.62% 93.22% 92.30%

0.5 100% 95.86% 92.53%

0.0 100% 96.09% 91.72%

20 1.0 94.37% 82.18% 80.22%

0.5 99.66% 78.62% 71.03%

0.0 100% 62.41% 47.13%

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Performance Evaluation Using Accuracy & honesty measures: Success

ratio progress

Malicious NCDs #

MonitorFreq. value

Number of iterations per relation

5 10 25 50 150

0 20 1.0 62.07% 65.06% 71.26% 80.69% 93.45%

0.5 80.69% 83.45% 87.93% 93.56% 99.77%

0.0 92.76% 96.09% 98.51% 100% 100%

Malicious NCDs #

MonitorFreq. value

Number of iterations per relation

5 10 25 50 150

10 20 1.0 51.26% 53.68% 59.20% 65.40% 82.99%

0.5 49.89% 52.87% 55.63% 61.38% 82.99%

0.0 49.77% 49.77% 50.11% 52.64% 79.54

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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

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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

X iCDpA qA Y jRD

pA qA

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Offered Trust Level (OTL) Requested Trust Level (RTL) A B C D E

A 0 0 0 0 0

B B - A 0 0 0 0

C C - A C - B 0 0 0

D D- A D - B D - C 0 0

E E - A E - B E - C E - D 0

F F F F F F

An example of the ETS table

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

machine one machine two

task one 30 35

task two 35 50

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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

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Case Study: Performance Evaluation

incentive mechanisms for large collaborative resource sharing

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