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