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Sub-Project III 4 : CRESCO-SOC-COG Second Progress Report ( 15 February 2007 - 14 June 2007)

Second cresco coordination meeting, Roma, 6 July 2007. Sub-Project III 4 : CRESCO-SOC-COG Second Progress Report ( 15 February 2007 - 14 June 2007). Contribution of the Dipartimento di Ingegneria dell’Impresa Università di Roma “Tor Vergata”.

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Sub-Project III 4 : CRESCO-SOC-COG Second Progress Report ( 15 February 2007 - 14 June 2007)

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  1. Second cresco coordination meeting, Roma, 6 July 2007 Sub-Project III 4:CRESCO-SOC-COGSecond Progress Report(15 February 2007 - 14 June 2007) Contribution of the Dipartimento di Ingegneria dell’Impresa Università di Roma “Tor Vergata” ------------------------------------------------------------------------------------------------------------------------Massimiliano Caramia Coordinator : Adam Maria Gadomski, ENEA

  2. DII - Università di Roma “Tor Vergata” Contribution Introduction: Conceptualization Platform • 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.

  3. DII - Università di Roma “Tor Vergata” Contribution The general framework • Given a state (information) X of the system, a knowledgeKjtransformsX 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

  4. 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

  5. DII - Università di Roma “Tor Vergata” Contribution The IPK decision network Managerial decisions level Different points of view. …. Different meta-levels… Management is focused on first meta-level.

  6. DII - Università di Roma “Tor Vergata” Contribution Universal Management Paradigm Subjective socio-cognitive perspective: … Relation to the large organization structure …..

  7. DII - Università di Roma “Tor Vergata” Contribution A distributed modeling framework of IPK and UMP Top-view Supervisor Supervisor Supervisor Tasks, information Tasks, information Tasks, information Tasks, information Tasks, information Tasks, information Manager Manager Manager Manager I,P,K I,P,K I,P,K I,P,K

  8. DII - Università di Roma “Tor Vergata” Contribution 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.

  9. DII - Università di Roma “Tor Vergata” Contribution • 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?

  10. The Grid framework (Ranganathan and Foster, 2002) Doamain of activities Supervisors Manager

  11. Economic Models for Grid Resource Management DII - Università di Roma “Tor Vergata” Contribution In economy, high-risk decisional example: • Providea 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

  12. The example study: Economic Models DII - Università di Roma “Tor Vergata” Contribution • 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 All of them are based on the Domain-of-Activity attributes (for identification) and attributes of Intelligent Agent

  13. Main Players in the Grid Market Place DII - Università di Roma “Tor Vergata” Contribution • 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

  14. Two decisional perspectives (different top-tasks): Tender-Contract Net Model DII - Università di Roma “Tor Vergata” Contribution Buyya et al., 2002 • 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.

  15. The Simulation Study DII - Università di Roma “Tor Vergata” Contribution • Two different scenarios for the Grid system: • 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. • 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.

  16. The possibilities of data processing: The Data Set DII - Università di Roma “Tor Vergata” Contribution Grid1: |M| = 10 identical clusters, with 10 machines each Grid2: |M| = 11 clusters with different number of machines according to WWG Testbed, Buyya et al. (2002). • 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$

  17. 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

  18. Average Load (Grid 1)

  19. Average penalty of processed tasks (Grid 1)

  20. Average utility of processed tasks (Grid 1)

  21. Average Load (Grid 2)

  22. Average penalty of processed tasks (Grid 2)

  23. Average utility of processed tasks (Grid 2)

  24. Case study: future work

  25. Conclusions: • - 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 Some references Gadomski 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.1994 Gadomski 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, Rome Gadomski 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.

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