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Measuring reputation in Testbeds
Chrysa Papagianni, Symeon PapavassiliouNational Technical University of Athens
Outline
Measuring Testbeds’ Reputation Motivation and Objectives Federated Trust and User Experience framework
FTUE Framework Overview FTUE Framework: Evaluation and Results
Integration into the Fed4FIRE federation and Information Flow
Motivation and Objectives
Trust is the subjective probability by which an entity, A, expects that another entity, B, performs a given action1
• Trust and Reputation has been widely used in P2P network
Reputation based Trust Management Systems
Measuring testbeds’ reputation• A Reputation Based Trust Management system can help
experimenters select resources based on a testbeds’ reputation
1 D. Gambetta, ”Can we trust trust?”, in Trust: Making and Breaking Cooperative Relations, D. Gambetta, Basil Blackwell, 2000, pp. 213-237
Entities rate each other
Opinions aggregated
Reputation Score
Federated Trust and User Experience (FTUE)
But how to build reputation scores for testbeds? Federated Trust & User Experience (FTUE) Framework1
Subjective: Experimenters’ QoE Objective: Monitoring information
What is a testbed service? Non technical/technical services
Experimenters evaluate
federated testbeds’ services
Added value service for
testbed owners and users
Empowering users to select
resources
1 Kapoukakis, A.; Kafetzoglou, S.; Androulidakis, G.; Papagianni, C.; Papavassiliou, S., "Reputation-Based Trust in federated testbeds utilizing user experience," Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 2014 IEEE 19th International Workshop on , vol., no., pp.56,60, 1-3 Dec. 2014
FTUE – Framework Overview (1)
Testbeds
Users
Trust Score Table
Monitoring Data
. . . .
.
.
.
Service Trust ScoreU1
SA1
SA1...
TA1
TAk
TB1
U2
UN
Reputation Service
C1
C2
CN
SA2 SAk
. . . .SB1 SB2 SBk
.
.
.
.
SAk
SB1...
SBk...
TBk
.
.
.
.
.
.
.
.
.
Services Advertised via the Reputation Service
e.g. SA1 is service of type S1 for tesbed A
Experimenter provides QoE
feedback (Opinion) for the Service
User’s Credibility is updated - comparison between the
opinion and monitoring dataOpinions are weighted with
Credibility and Quality values and aggregated to
form the Reputation Scores
FTUE – Framework Overview (2)
Scenario 4 At least one testbed has different
behavior
Truthful or malicious in
disguise
Increase Credibility
Scenario 3Conservative Opinions and high or
low quality of service
Truthful –Moderate
Increase Credibility
Scenario 2 Opinions differ from the
monitoring data for every testbedMalicious
DecreaseCredibility
Scenario 1 Opinions match the monitoring data
Truthful Increase Credibility
FTUE: Performance Evaluation
Goal: Adaptability of our framework in changing conditions.
Simulation Setup 100 Users/ 10 experiments each 2-4 Testbeds in the federation (A to D) Testbeds advertise 1 technical Service and 1 non technical service e.g. Overall Experience User Opinions / Monitoring Data: Uniformly Distributed [0,1] based on
1-500 experiments: Smooth Operation - 80% Truthful /20% Malicious 500-1000 experiments: Technical Problems for Testbed B - 20% Truthful (Moderate) /80% Truthful or
Malicious in disguise
User Classes
Scenario I Truthful
Scenario II Malicious
Scenario III Truthful (moderate)
Scenario IV Truthful or Malicious in disguise
Performance Evaluation: Adaptability in changing conditions
Successful constraint of malicious users Quick Adaptation
Reputation Service in Fed4FIRE
Aggregate Manager
• Retrieve Reputation Scores for each
testbed service from RS• Provide Ratings/Opinion and
Quality for experiment
Experimenter Tools Experimenter Tools
SFA
Reputation Service
• Ruby based implementation • Reputation Service
Repository
Reputation Service
Manifold
Data Broker
• Retrieve Monitoring data for experimentXML-RPC / REST
REST
Testbed Monitoring
OML server
OML
REST
• Update Credibility Values• Update Reputation Scores
THANK YOU!Questions: [email protected]
