sub-project iii 4: cresco-soc-cog second progress report (15 february 2007 - 14 june 2007)...
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Sub-Project III 4:
CRESCO-SOC-COG Second Progress Report
(15 February 2007 - 14 June 2007)
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-Massimiliano Caramia
Coordinator : Adam Maria Gadomski, ENEA
Second cresco coordination meeting, Roma, 6 July 2007
Contribution of the Dipartimento di Ingegneria dell’Impresa
Università di Roma “Tor Vergata”
Information, Preferences and Knowledge (IPK model)
- An Information is data describing a state (or a property) of an object or entity of interest.
- knowledge is every abstract property of a human or artificial agent which has the ability to process an information into another information.
- A preference is an ordered relation between two states (properties) of a domain of the activity of an agent. It indicates a property with higher utility for an agent.
Preference relations serve to establish an intervention goal of an agent.
Information, preferences and knowledege are essential components for every decision process.
DII - Università di Roma “Tor Vergata” Contribution
Introduction: Conceptualization Platform
The general framework
• Given a state (information) X of the system, a knowledge Kj transforms X into another state (information) Y.
• An intelligent agent (IA) has a set of knowledge K = (K1,..,Kn) to exploit.
• IA wants to choose the knowledge Kj* in K that allows the current system state X to be trasformed into the requested state (goal)
• Preferences allow the IA to compare the possible (expert based) outcomes of different knowledge and make a decision
DII - Università di Roma “Tor Vergata” Contribution
How it works
– Information I arrives from a domain of activities– It is transformed by the set of (model-) knowledge K
producing a set of new information– Information are confronted with preferences to
establish the goal, i.e., a state maximally preferred– Goal enables to choose the appropriate (operational-)
knowledge Kj* in K
– Kj* processes information: I'= Kj*(I)
– The new information indicates how to modify the domain of activity
DII - Università di Roma “Tor Vergata” Contribution
The IPK decision network
Different points of view. …. Different meta-levels…
Management is focused on first meta-level.
Managerial decisions level
DII - Università di Roma “Tor Vergata” Contribution
Universal Management Paradigm
Subjective socio-cognitive perspective: … Relation to the large organization structure …..
DII - Università di Roma “Tor Vergata” Contribution
I,P,K I,P,K I,P,K I,P,K
Supervisor Supervisor Supervisor
Manager Manager Manager Manager
Tasks, information
A distributed modeling framework of IPK and UMP
Tasks, information
Tasks, information
Tasks, information
Tasks, information
Tasks, information
DII - Università di Roma “Tor Vergata” Contribution
Top-view
The model proposal
• Implementing a distributed IPK and UMP model in a grid infrastructure
• An study example: Applying a market based model (intervention domain) to let actors/agents negotiate for the achievement of their intervention-task (from a supervisor)
• Experimentation on a set of verification & validation instances (syntetic)
• Application to the selected real test cases of the socio-technological network under high-risk decisions.
DII - Università di Roma “Tor Vergata” Contribution
1. Organization modeled as a computer network
2. How to mitigate organization vulnerability.
3. The first aspect refers to the the information exchange, communication and tasks distribution
4. The messages are carriers of IPK
5. What is managed by a manager?
DII - Università di Roma “Tor Vergata” Contribution
The Grid framework (Ranganathan and Foster, 2002)
Doamain of
activities
Supervisors
Manager
Economic Models for Grid Resource Management
• Provide a quantitative framework for resource allocation and
for regulating supply and demand in the Grid computing
environments
• They primarily charge the end users for services that they
consume on a demand-and-supply basis
• They optimize resource provider and consumer objective
functions through trading and brokering services
• A user is in competition with other users and a resource owner
with other resource owners
DII - Università di Roma “Tor Vergata” Contribution
In economy, high-risk decisional example:
The example study: Economic Models• Commodity Market Model
• Posted Price Model
• Resource Sharing Model via Negotiation
• Tendering/Contract-Net Model
• Auction Model
• Bargaining model
• Bid-based Proportional Resource Sharing Model
• Community/Coalition/Bartering Model
• Monopoly and Oligopoly
DII - Università di Roma “Tor Vergata” Contribution
All of them are based on the Domain-of-Activity attributes (for identification) and attributes of Intelligent Agent
Main Players in the Grid Market Place
• Grid Service Providers (GSPs) providing the role of producers. In the
TOGA context they are managers, informatives, and advisors
• Grid Resource Brokers (GRBs) representing consumers. In the TOGA
context they are supervisors
• Grid Market Directory (GMD) to mediate the interaction between
GRBs and GSPs. In the TOGA context they represent meta-knowledge
and meta-information
DII - Università di Roma “Tor Vergata” Contribution
Two decisional perspectives (different top-tasks): Tender-Contract Net Model
From the resource broker perspective:• The broker announces its requirements and invites bids from GSPs.• Interested GSPs evaluate the announcement and respond by submitting their bids.• The broker chooses the best offer and sign a contract to the most appropriate GSP.
From the GSP perspective:• It receives announcements.• It evaluates the service capability.• It responds with a bid.• It delivers service if bid is accepted.• It reports results and bill the broker.
Buyya et al., 2002
DII - Università di Roma “Tor Vergata” Contribution
The Simulation StudyTwo different scenarios for the Grid system:
1. Scenario ECO1: tasks are mono-thematic applications and
their requests are submitted to the same ES (GRB) that
interacts with the LSs (GSPs) of clusters dedicated to that
kind of applications.
2. Scenario ECO2: tasks are heterogeneous and there are as
many GRBs as many tasks.
• In Scenario ECO2 the GSP of a cluster may receive awards
from many GRBs, and it will respond with an acceptance only
to the award related to the most useful announcement for the
cluster, and with a refusal to the other awards.
DII - Università di Roma “Tor Vergata” Contribution
The possibilities of data processing: The Data Set
• Tasks arrive according to a Poisson arrival process, where is
the average # of tasks per t.u.
• 45% of arriving tasks are background tasks, and they have
priority with respect to external tasks.
• Average task size Oj = 10000 MI
• 1000 t.u.
• Average task budget Bj = 250 G$
Grid1: |M| = 10 identical clusters, with 10 machines eachGrid2: |M| = 11 clusters with different number of machines
according to WWG Testbed, Buyya et al. (2002).
DII - Università di Roma “Tor Vergata” Contribution
DII - Università di Roma “Tor Vergata” Contribution
- We compared ECO1 and ECO2 with Round Robin protocol
- We analysed: load goal function, utility goal function, penalty goal function
- Load and utility are the two main goals in the system: the former refers to the manager, the latter to the supervisor
Average Load (Grid 1)
0102030405060708090
100
1 2 3 4 5 6 7 8 9 10
bkg load
RR
ECO1
ECO2
Average penalty of processed tasks (Grid 1)
0
50
100
150
200
250
1 2 3 4 5 6 7 8 9 10
RR
ECO1
ECO2
Average utility of processed tasks (Grid 1)
0
20
40
60
80
100
120
140
1 2 3 4 5 6 7 8 9 10
RR
ECO1
ECO2
Average Load (Grid 2)
0102030405060708090
100
1 2 3 4 5 6 7 8 9 10
bkg load RRECO1 ECO2
Average penalty of processed tasks (Grid 2)
0
40
80
120
160
200
240
1 2 3 4 5 6 7 8 9 10
RR
ECO1
ECO2
Average utility of processed tasks (Grid 2)
0
20
40
60
80
100
120
140
160
1 2 3 4 5 6 7 8 9 10
RR
ECO1
ECO2
Case study: future work
- State of the art analysis
- Distributed model proposal
- Mapping between TOGA and Computer network
- Implementation and testing of the model
- Preliminary results on validation instances
- Future work: tests on case study
Conclusions:
Some referencesGadomski A.M. (1994), TOGA: A Methodological and Conceptual Pattern for Modeling of Abstract Intelligent Agent. In Proceedings of the "First International Round-Table on Abstract Intelligent Agent". A.M. Gadomski (editor), 25-27 Gen., Rome, 1993, Publisher ENEA, Feb.1994Gadomski A. M., S. Bologna, G.Di Costanzo, A.Perini, M. Schaerf. (2001), “Towards Intelligent Decision Support Systems for Emergency Managers: The IDA Approach”. International Journal of Risk Assessment and Management.Gadomski A. M., (2003), Socio-Cognitive Engineering Foundations and Applications: From Humans to Nations, Preprints of SCEF2003 ( First International Workshop on Socio-Cognitive Engineering Foundations and Third Abstract Intelligent Agent International Round-Tables Initiative), Rome, 30 Sep. 2003.Gadomski A.M. , A. Salvatore, A. Di Giulio (2003) Case Study Analysis of Disturbs in Spatial Cognition: Unified TOGA Approach, 2nd International Conference on Spatial Cognition, RomeGadomski A.M. (2006), Socio-Cognitive Scenarios for Business Intelligence Reinforcement: TOGA Approach, The paper preliminary accepted for publication in Cognitive Processing, International Quarterly of Cognitive Science, Springer Verlag.