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Monitoring, Policing and Trust for Grid-Based Virtual
Organisations
Luke TeacyI.A.M Group, ECS
University of Southampton, UK
Overview
Dynamic Virtual Organisations Concept Key Challenges
CONOISE-G Architecture Managing the VO Lifecycle Emphasis on Trust, Monitoring & Policing
Virtual Organisations (VOs) The Grid concept:
“coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organisations”
Foster et al 2001
Virtual Organisations: people, resources (hardware/software) Crossing geographical and organisational boundaries
Keyword: Dynamic VO membership & operation should be able to change to
meet changing circumstances
dynamic,
CONOISE-G
CONOISE-G aims to support robust and resilient VO formation and operation in open and competitive environments.
Lucy goes to the Olympics — Lucy wants Video clips News Ad-hoc services for her PDA
Dynamically bring together infrastructure and content to meet changing demand How do we do this?
Our Approach: Agents and the Grid Brawn
Existing Grid Infrastructure How to integrate services between organisations
Different policies and infrastructures Robust and Secure
Brains CONOISE-G: Multi-Agent System Which services and for what and when? Decision processes, Negotiation
Brain Meets Brawn: Why Grid and Agents Need Each Other — Foster, Jennings, Kesselman, 2004
Our Approach
Grid Infra-Structure
Agents
VO
CONOISE-G
Grid Services,Globus etc
Keys Challenges
VO Formation VO Operation• Who is available?• What QoS will be
provided?• Who should be
selected?
• Should contracts be honoured?
• Are there better services?
• What QoS is being provided?
Where Service Providers:• Can enter and leave the system• Can compete against one another for orders.• Cannot entirely be trusted to honour their promises.
System Architecture
VOM
SP1
SP2
SPn
CAYP
RB
QoSCQA
PA
YP – Yellow Pages
CA – Clearing Agent
RB – Reputation Broker
PA – Policing Agent
QoSC – QoS Consultant
QA – Quality Assessor
SP – Service Provider
VOM – VO Manager
VO Lifecycle
1) Discover services
2) Obtain bids
3) Select bids
4) Form the VO
5) Monitor Services
Market Demand
6) Perturbation
Service Discovery & Obtaining Bids
Discovering Services
• For a given service request, discover who can be a potential provider.
VOM YP
SP1 SPn
• Publish subscribe model so VOM is constantly informed of new agents
Obtaining Bids
VOM calls for Bids based on advertised services
An SP must decide whether/what to offer.
SP uses constraint reification in decision making
See references for details
Selecting Bids
Trust Assessment
Quality Assessment
Choosing Winning Bids
Selecting Bids (Overview)
Assessing the bids
SP1
SPk
CAChoose SP set offeringbest overall utility
Conducting an Auction
Establishing utility forproviders
SP1
SPk
QA TC
Utility Calculator
Price
Assessing Bids - QoS
Expectation Based Confidence Assessment of QoS Given a set of VOM QoS expectations (qi>x)*, how likely is
it that those expectations will be met?
Taking into account: past provision instances with similar expectation only the statistical relationship between QoS attributes
Operating under time constraints Tradeoff performance with accuracy Goal: dynamic, near-instantaneous assessment
Assessing Bids - Trust
Trust – How likely is an SP to fulfil its obligations?
Probabilistic Trust Model
Assess the trustworthiness of SPs using:
Internal Trust Component: Based on personal experience
Reputation: Based on opinions of other agents
Reputation Filtering Mechanism
Calculating Utility Estimate probability of successful contract
outcome with SP based on Quality & Trust
€
P(Outcome = sucess) = f (Quality,Trust )
€
EU(Outcome) = P(Outcome)[ServiceUtil − Pr ice]Outcome∈
{sucess, fail}
∑
Calculate Expected Utility
Allocate tasks to SPs using efficient polynomial time algorithm
VO Formation
Hiring Service Providers
Establishing Contracts
Forming VO
SP
SP
SP
SP
RAVOM
Setting up arrangements for service provision to be monitored.
Policing: Contract management
& evaluation Contract = Service
Level Agreement (SLA)
Market
QoSC
YP
Contracts Overview
Much work has been done on contracting languages formal approaches have clear semantics, but often
lack useful features. ad hoc approaches are hard to reason with, but
usually very descriptive. we try to take a middle road.
SWCL (semantic web contracting language) Based on RDF and SWRL (Semantic Web Rule
Language) Attempts to fulfil the above desiderata.
Existing Contract Languages WS-Agreement
Does not have a way of referring to other agents,contracts and clauses easily.
Expects one to embed an evaluation language within it.
Essentially, a wrapper for a contract outline LCR
Solid formal approach Difficulty representing many useful contracting
features.
Contract Language in CONOISE-G (SWCL) Can describe things such as
Which parties are involved Time constraints for agreement start and end times Representation of actions by agents Assignment of rewards and penalties Refer to other agents, contracts and clauses
Example (natural language): Contract effective from 00:00 31/12/05 for 24hrs SP1 to provide movie every 3hrs, each 1-2hrs long SP1 must pay £20 penalty for each 3hr period without
movie All Penalty fees due at contract end time.
VO Operation
QoS Monitoring
Policing
Perturbation
Operation Overview
Monitor EnvironmentAdapt VO membership/roles to
changing circumstances (Perturbation)Scenarios
New Service enters market place Current service fails / breaks contract
New Service Perturbation
SP registers/updates advertisement with YP
YP informs VOM of new service VOM obtains bid from SP
New ServiceAdvertised
VOM Considers Bid
Hire new SPFire old SP(s)
VOM calculates utility gain of hiring new SP New SP utility vs. current providers Penalty clauses in contract
Hire / Fire if necessary
Essential for: Tracking performance to ensure SLA is adhered to Triggering corrective action by VO Providing evidence for establishing trust
Challenges To handle continuous, potentially fast data input To handle ad-hoc, long-standing monitoring
requests To process the requests with real-time performance
Service Recovery Perturbation
Monitor Services
VOM calls for bids for failed service Excluding failed service provider
Utility assessed for received bids as before Taking on board quality, trust & price And penalties
Auction cleared Hire and fire messages sent
Service Recovery Perturbation
Monitor Services VOM re-formed
The ability to form and operate virtual organisations in grid is important.
We aim to support robust and resilient VO formation and operation.
We have developed technologies for: Decision making mechanisms during VO formation Assessing trust & reputation Policing within VOs QoS monitoring
Conclusions
Future Work
Real-time QoS Prediction Contracting
We can generate and evaluate simple contracts. We have not yet formalized its semantics.
Policing Investigate reasons behind failure
Who should take the blame? Argumentation-Based Negotiation Trust updated according to conclusion
References
W. T. L Teacy, J. Patel, N. R. Jennings and M. Luck, Coping with Inaccurate Reputation Sources: An Experimental Analysis of a Probabilistic Trust Model. In AAMAS’05, 2005
N. Oren, A. Preece and T. J. Norman, Service Level Agreements for Semantic Web Agents. 2005
S. Chalmers, A. Preece, T. J. Norman and P. M. D. Gray, Commitment Management Through Constraint Reification. In AAMAS’04, 2004
G. Shercliff, P. J. Stockreisser, J Shao, W. A. Gray and N. J. Fiddian, Supporting QoS Assessment and Monitoring in Virtual Organisations. 2005
All References available at:
http://www.conoise.org/
Alun PreeceTim NormanPeter GrayStuart ChalmersNir Oren
Constraint Oriented Negotiation in Open Information Seeking Environments for the Grid
Alex GrayNick FiddianJianhua ShaoGareth ShercliffPatrick Stockreisser
Nick Jennings Mike LuckLuke TeacyJigar Patel
Simon Thompson
http://www.conoise.org/
Demonstration: 10:30
Welsh e-Science Booth