1-1 Incentive Mechanisms for Large Collaborative Resource Sharing Objectives: Why Resource...
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1-1
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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Resource Harnessing: Grid Example
Grid
Resource
GRGR
Grid Grid
ResourceResourceGridGrid
ResourceResource
GRGRmultiplexmultiplexGRGR
GridGrid
ResourceResource
GridGrid
DomainDomain
base
base
VPGRVPGR
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Resource Harnessing: Grid Example
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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Case Study: Trust Modeling on P2P Grids
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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Case Study: Trust Modeling on P2P Grids
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