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Transcript of Holding slide prior to starting show. Constraint Oriented Negotiation in Open Information Seeking...
Holding slide prior to starting show
Constraint Oriented Negotiation in Open Information Seeking
Environments for the Grid (CONOISE-G) Project
- V.Deora, W.A. Gray, J.Shao, G. Shercliff, P. J. Stockreisser -
Introduction
• Virtual Organisations
• Lifecycle
• Expectation Based QoS
• Moving Conoise to the grid
• Future Work
Conoise Aims and Objectives
• To develop models and techniques that will support the entire VO lifecycle
• Realising this vision requires the development of:– Autonomous agents to represent the different problem
solving entities. – Sophisticated interaction models that enable autonomous
agents to form and interact within groups – Rich knowledge representation and information inter-change
mechanisms
Group MembersAberdeen
Manage agent commitments and provide an intelligent decision making strategyPolicing and governing of Social Laws
CardiffService Discovery and Quality AssessmentQuality Policy & Service Discovery
SouthamptonNegotiation and Coalition Formation Trust and Reputation
Virtual Organisations (VO)
• Consist of semi-independent autonomous entities.
• Each entity has• A range of problem solving
capabilities
• And a range of resources.
Why create a VO?
• Because the virtual organisation is greater than its parts and there is mutual benefit for all participants.
Entity Behaviour
• The entities co-exist but…
• Can compete against one another in a virtual market place
• Can form VOs to exploit a gap in the market
• And attract custom through advertising the cost and quality of it’s services
VO Sequence
VO and Services
Coalition – Forming a partnership with a competitor
New Service – Forming a partnership with an entity with complementary expertise
VO vs Entity
The collection of independent entities will act as a single conceptual unit in the context of the proposed service
(Co-operating and Co-ordinating Activities)
Each entity retains its individual identity outside of this context. It may break a particular partnership if it decides it is in it’s own best interest.
Service Provider (SP) Decisions
Given a request for a service an SP will have several choices.
Several factors may influence the decision making process
Time Constraints
Prior Commitments
Availability of others
Conoise VO Lifecycle
FORMATION OPERATION DISSOLUTION
RESTRUCTURING
- Elimination
- Expansion
Formation
• An entity attempts to form a VO to meet user requirements (Requester Agent)
• Identifies current services (Yellow Pages Agent)• Issues a call for proposals • Needs to decide which proposals to accept to form VO
(QoS Agent)
Problems…• An RA may wish to break an existing commitment in
order to take part in a more lucrative contract.• When is it most profitable for an agent to initiate a VO?• How will RA deal with such decisions?
Operation/Restructure
• Needs to meet contractual Agreements
• Will have to adapt to market changes– New Service Provider– Decreased Utility
Dissolution
• Why would a VO need to disband?• Why is the organisation not formalised?
– To survive in a rapidly changing market environment (Trends, Competition etc…)
– Cost of formalisation– Limitations imposed by alliance may be too
restrictive to development– Mutual benefit may only be available once
A Example Scenario
Package required by customer:– Movie subscription.– News service.– >50 free text
messages per month.
30 free phone minutes per month.
SP Movies (pcm)
News (daily)
Text (#free)
Phone (#free)
SP1 10 24
SP2 72
SP3 120 30mins
SP4 5 30mins
Conoise Architecture
•Built on JADE Agent Platform using java
•Agents communication in ACL
JADE
MessagingLifecycleWhite &
Yellow Pages
SP RA QA SP SPYP
Container Container Container
QoS – What Is It?• As Functionality?
If one service offers more functionality than others, then it is considered to offer a better quality
• As Conformance?
If a service honours its “claims”, it is considered to offer good quality
• As Reputation?
Users’ perception of a service’s consistency over time
QoS as Conformance
• QoS(Ai) = f (Aia, Ai
d)
A measure of difference between delivered (Aid) and
advertised (Aia) qualities.
• QoS(S) = Σ wi QoS(Ai)
A weighted average of individual qualities
S
A1 A2 An….
Service
QoSAttributes
An Example
• QoS(fr) = f (fra=24, frd=22) = 22/24 = 0.92
• QoS(av) = f (ava=7, avd=7) = 7/7 = 1.00
• QoS(news) = 0.8QoS(fr) + 0.2QoS(av) = 0.94
News to PDA
UpdateFrequency Availability
A Real-World Example
How are they derived?
A Real-World Example (cont’d)
1 out of 5
Rating on a hotel room byMr Fussy
A Real-World Example (cont’d)Rating on a hotel room byMr Easy
5 out of 5
Observations
• What do the ratings mean?
• Current approaches do not differentiate individual users’ expectations on QoS
• Need a more user-centric QoS model
An Expectation Based Model
QoS(Ai) = f (Aia, Ai
d)
User rated quality of Ai
User perceived quality on Ai
QoS(Ai) = <Ru(Ai), Eu(Ai), Pu(Ai)>
User expected quality on Ai
An Example• Suppose that we have 3 providers (SP1, SP2
and SP3) who offer news services to PDA
• We wish to establish their qualities in terms of offering 24 updates per day (Frequency)
• Assume that we have had 6 users (U1, U2, U3, U4, U5 and U6) who have used the services
• We wish to determine which SP is the best
Conventional Approach
Users SP1
Frequency
SP2
Frequency
SP3
Frequency
U1 0.3 0.3
U2 0.8 0.9
U3 0.3 1.0
U4 0.8
U5 0.5 0.1
U6 0.6 0.3
Aggregate rating
0.50 0.67 0.47
Collect ratings only
SP2 is the best
Our Approach
Users SP1
<E(fr), P(fr), R(fr)>
SP2
<E(fr), P(fr), R(fr)>
SP3
<E(fr), P(fr), R(fr)>
U1 <0.9, 0.7, 0.3> <0.7, 0.5, 0.3>
U2 <0.4, 0.4, 0.8> <0.5, 0.5, 0.9>
U3 <0.8, 0.6, 0.3> <0.4, 0.5, 1.0>
U4 <0.6, 0.6, 0.8>
U5 <0.9, 0.7, 0.5> <0.9, 0.5, 0.1>
U6 <0.9, 0.7, 0.6> <0.7, 0.5, 0.3>
Collect expectation, perception and ratings
Our Approach
Users SP1
<E(fr), P(fr), R(fr)>
SP2
<E(fr), P(fr), R(fr)>
SP3
<E(fr), P(fr), R(fr)>
U1 <0.9, 0.7, 0.3> <0.7, 0.5, 0.3>
U2 <0.4, 0.4, 0.8> <0.5, 0.5, 0.9>
U3 <0.8, 0.6, 0.3> <0.4, 0.5, 1.0>
U4 <0.6, 0.6, 0.8>
U5 <0.9, 0.7, 0.5> <0.9, 0.5, 0.1>
U6 <0.9, 0.7, 0.6> <0.7, 0.5, 0.3>
Collect expectation, perception and ratings
You must give your expectation!
If Expectation is 0.8 …
Users SP1
<E(fr), P(fr), R(fr)>
SP2
<E(fr), P(fr), R(fr)>SP3
<E(fr), P(fr), R(fr)>
U1 <0.9, 0.7, 0.3> <0.7, 0.5, 0.3>
U2 <0.4, 0.4, 0.8> <0.5, 0.5, 0.9>
U3 <0.8, 0.6, 0.3> <0.4, 0.5, 1.0>
U4 <0.6, 0.6, 0.8>
U5 <0.9, 0.7, 0.5> <0.9, 0.5, 0.1>
U6 <0.9, 0.7, 0.6> <0.7, 0.5, 0.3>
Assume range = [E-0.1, E+0.1]
SP1 is the best - QoS(SP1) = 0.43
If Expectation is 0.5 …
Users SP1
<E(fr), P(fr), R(fr)>
SP2
<E(fr), P(fr), R(fr)>
SP3
<E(fr), P(fr), R(fr)>
U1 <0.9, 0.7, 0.3> <0.7, 0.5, 0.3>
U2 <0.4, 0.4, 0.8> <0.5, 0.5, 0.9>
U3 <0.8, 0.6, 0.3> <0.4, 0.5, 1.0>
U4 <0.6, 0.6, 0.8>
U5 <0.9, 0.7, 0.5> <0.9, 0.5, 0.1>
U6 <0.9, 0.7, 0.6> <0.7, 0.5, 0.3>
Assume range = [E-0.1, E+0.1]
SP3 is the best - QoS(SP3) = 1.0
If Expectation is unspecified
Users SP1
<E(fr), P(fr), R(fr)>
SP2
<E(fr), P(fr), R(fr)>
SP3
<E(fr), P(fr), R(fr)>
U1 <0.9, 0.7, 0.3> <0.7, 0.5, 0.3>
U2 <0.4, 0.4, 0.8> <0.5, 0.5, 0.9>
U3 <0.8, 0.6, 0.3> <0.4, 0.5, 1.0>
U4 <0.6, 0.6, 0.8>
U5 <0.9, 0.7, 0.5> <0.9, 0.5, 0.1>
U6 <0.9, 0.7, 0.6> <0.7, 0.5, 0.3>
Our approach falls back a conventional one
SP2 is the best - QoS(SP2) = 0.67
The QA’s Architecture
QoS
Calculator
QoS
Calculator
QoS CollectorQoS CollectorThe QA
E request for ratings E
result ratings matching E RPE
RPE
Ratings DB
ExpectationPerception
Rating+
Service Agreement
Service Monitoring
Summary of Our Approach Main Features
Attempt to calculate QoS in context Dynamically aggregate QoS ratings on a case-by-
case basis
Related Work Rating based QoS measurement QoS calculation in marketing research Collaborative filtering QoS taxonomies
Conoise-G Proposal• Trust & Reputation
– In an untrustworthy environment how reliable are the sources from which we obtain information and services?
• Policing– How do we detect when a contract is broken and what reaction do
we take?
• Quality
• Align Conoise services with a Grid-enabled environment– Determine how ontology and resource discovery mechanisms can
employ services offered within the Grid – Determine required Grid structure to support Conoise work
Conoise as a Grid
• Virtual Organisations present in grid
• Difference is in implementation rather than concept
• Research areas within conoise/conoise-G contribute to grid research
JADE
MessagingLifecycleWhite &
Yellow Pages
Container Container Container Container
Globus
Core
GSI
RLS
GRAM
MDS
Possible Implementations
• Grid enable Agent Platform
• Agent Platform itself would make use of grid services
• Requires no reimplementation of agents
Globus
JADE
MessagingLifecycleWhite &
Yellow Pages
SP RA QA SP SPYP
Container Container Container
RA
Container
CA
Core
GSI
RLS
GRAM
MDS
Possible Implementations
• reimplement agents as grid services
• long development period
• requires additional support services
Globus
Core
GSI
RLS
GRAM
MDS
SP RA QA SP SPYP
CA
Possible Implementations
• Provide grid-interface for core agents
• Can make use of grid services
• Possibly use bridge to allow external grid services to interact with conoise agents
Globus
JADE
MessagingLifecycleWhite &
Yellow Pages
SP RA QA SP SPYP
Container Container Container
RA
Container
CA
Core
GSI
RLS
GRAM
MDS
Possible Implementations
• Allows phased implementation
• Both core agents and service providers can make use of grid services
• Eventually remove agent platform
Globus
JADE
MessagingLifecycleWhite &
Yellow Pages
SP RA QA SP SPYP
Container Container Container
RA
Container
CA
Core
GSI
RLS
GRAM
MDS
Problem Issues
• Progression of grid technology– Proposal written 3 years ago
• Integration of Agents and Grid– How will agents interact with external grid
services and software?– How can we utilise evolving grid standards
for core functions such as security and resource discovery?
Future Work @Cardiff
• Continue research into QoS assessment– QoS attribute aggregation– QoS of composite services– Monitoring of QoS degradation
QoS Attribute Aggregation
• Current model values each quality metric as equal
Avail=0.8
Reliab=0.6
Accur=0.4
0.6
FrmRt=0.3
Reliab=0.3
Accur=1.0
~0.5
• Simply averaging the QoS components does not present an accurate QoS value for the service
• Need a model which produces a more meaningful value
SP
SP
QoS of Composite Services • Consider a composite service A,
composed of services B and C
• Can we use the QoS attributes we already have for B and C to meaningfully assess the QoS of A
A
B C
QoS=0.3 QoS=0.9
QoS=??
• Current model assumes all QoS attributes are present
• Current model assumes 100% confidence in QoS values
• Service Providers may actually be composite services
• How do we determine at which point in the chain a QoS degradation is taking place?
• Based on observation can we predict a QoS degradation before it occurs?
QoS Degradation
Conclusions
• Achievements of conoise– CLP for decision making– Negotiation for VO formation– Expectation Based QoS assessment
• Future Work– VO operation and disbandment– Trust & Policing– Grid implementation