A Group Selection Pattern optimizing agent-based Virtual Organizations in Grids   <ul><ul><li>Oscar Ardaiz </li></ul></ul>...
Outline <ul><li>VOS  in the Grid </li></ul><ul><li>Group Formation mechanisms in MAS </li></ul><ul><li>Group Selection Pat...
The Grid (definitions) <ul><li>The Grid  consists in coordinated resource sharing and problem solving in dynamic, multi-in...
Example of Grid  Virtual Organization Org1 Org2 Org3 Virtual Organization2 Virtual Organization 1
VO management in Grids  <ul><li>Middleware toolkits </li></ul><ul><ul><li>GT4 [Foster, 2005 ]  provide several low level t...
Exploiting group structure in multiagent systems (MAS)  <ul><li>Coalitions </li></ul><ul><ul><li>formed by subsets of the ...
Group Selection <ul><li>Group Selection refers to a process of natural selection that favors characteristics in individual...
Other Group Selection apps <ul><li>Theoretical Biology: explaining altruism between non kin individuals in human societies...
Group Selection applied to the Grid <ul><li>The key idea is that biasing interaction between Grid nodes by arbitrary ident...
Example of group selection High coordination Agents interact & migrate based on group identifier Uncoordinated groups t= 1...
Algorithmic realization
Group Selection pattern <ul><li>Patterns in computer science have been used also coming from other disciplines </li></ul><...
Experiments Scenario <ul><li>For the experiments here we have implemented a collective interactions, corresponding to a  V...
VO policy alignment scenario   Groups with P1 and groups with P2: high utility Agents interact, migrate, mutate Groups wit...
Coordination metric <ul><li>The metric we employ to measure the alignment degree is the  Shannon Entropy Index .  </li></u...
Utility (coordination) N agents=100 ;  M policies =10; migration/mutation:0.3, 0.7   sim clock ticks m/m m/m low ratio mig...
Groups Distribution (I) low migration/mutation rate  (when higher utilities achieved) causes large number of groups m/m m/m
Groups Distribution (II) For a migration/mutation ratio, higher utilities with smaller groups m/m = 0.3 utility = 0.55 uti...
Conclusions <ul><li>Formalization of a  Group Selection pattern for Grid VOs coordination. </li></ul><ul><li>Experiments s...
References <ul><li>[Foster, 2001] I. Foster, C. Kesselman, S. Tuecke.  The Anatomy of the Grid: Enabling Scalable Virtual ...
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Group Selection Grid AWeSoMe07

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  • Group Selection Grid AWeSoMe07

    1. 1. A Group Selection Pattern optimizing agent-based Virtual Organizations in Grids <ul><ul><li>Oscar Ardaiz </li></ul></ul><ul><ul><li>Public University of Navarra, Spain </li></ul></ul><ul><ul><li>Isaac Chao & Ramon Sanguesa </li></ul></ul><ul><ul><li>UPC Barcelona, Spain </li></ul></ul>AWeSOMe'07 Vilamoura, Algarve, Portugal, Nov 26 - 27, 2007
    2. 2. Outline <ul><li>VOS in the Grid </li></ul><ul><li>Group Formation mechanisms in MAS </li></ul><ul><li>Group Selection Pattern </li></ul><ul><li>Optimizing Policy-based VO management </li></ul><ul><li>Simulation results </li></ul><ul><li>Conclusions </li></ul>
    3. 3. The Grid (definitions) <ul><li>The Grid consists in coordinated resource sharing and problem solving in dynamic, multi-institutional Virtual Organizations VOs [FOSTER01]. </li></ul><ul><li>VO : Virtual entity englobing many physical organizations sharig a common goal. </li></ul>The group structure already exists in Grids
    4. 4. Example of Grid Virtual Organization Org1 Org2 Org3 Virtual Organization2 Virtual Organization 1
    5. 5. VO management in Grids <ul><li>Middleware toolkits </li></ul><ul><ul><li>GT4 [Foster, 2005 ] provide several low level tools to aggregate resources and management access permissions </li></ul></ul><ul><ul><li>Extensions: CAS,VOMS, Akenti </li></ul></ul><ul><li>Problem: All focused on supporting large static communities and static resources </li></ul>Require policies as emergent property of the distributed policies of interacting agents
    6. 6. Exploiting group structure in multiagent systems (MAS) <ul><li>Coalitions </li></ul><ul><ul><li>formed by subsets of the population </li></ul></ul><ul><ul><li>goal oriented and short-lived </li></ul></ul><ul><ul><li>major limitations </li></ul></ul><ul><ul><ul><li>high computational complexity </li></ul></ul></ul><ul><ul><ul><li>unrealistic assumptions regarding the availability of information [Shehory, 2004]. </li></ul></ul></ul><ul><li>Congregations [Brooks, 2002] </li></ul><ul><ul><li>release the full autonomy to agents </li></ul></ul><ul><ul><li>major limitations: Groups are static </li></ul></ul><ul><li>Group Selection [this work] : </li></ul><ul><ul><li>study dynamics and evolution of groups in poputations. </li></ul></ul>
    7. 7. Group Selection <ul><li>Group Selection refers to a process of natural selection that favors characteristics in individuals that increase the fitness of the group the individuals belong relative to other groups </li></ul><ul><ul><li>every member of the group depends on a group characteristic that is not isolated in a single individual [Wilson, 1975] </li></ul></ul><ul><li>Group Selection can lead to the spread of group beneficial characteristics in many different grouped settings of agent’s populations [Boyd, 2002] </li></ul>
    8. 8. Other Group Selection apps <ul><li>Theoretical Biology: explaining altruism between non kin individuals in human societies [BOYD03], [BOWLES04], [HENRICH04]), </li></ul><ul><li>Economics: firm’s co-evolution trough inter-firm competition at the group level [CORDES06], [HODGSON04]. </li></ul><ul><li>Engineering: free-riding in P2P networks [HALES05] (bitorrent success?) & leader elections in groups in A-life [KNOESTER07]. </li></ul>
    9. 9. Group Selection applied to the Grid <ul><li>The key idea is that biasing interaction between Grid nodes by arbitrary identifiers enables efficient grouping of agents. </li></ul><ul><ul><li>Further group’s evolution through Group Selection optimizes groups in performance. </li></ul></ul><ul><ul><li>it is adaptive, self-organized, decentralized and highly scalable. can be used to automatically manage Grid VOs lifecycle in the Grid, </li></ul></ul>
    10. 10. Example of group selection High coordination Agents interact & migrate based on group identifier Uncoordinated groups t= 100 t= 40 t=1
    11. 11. Algorithmic realization
    12. 12. Group Selection pattern <ul><li>Patterns in computer science have been used also coming from other disciplines </li></ul><ul><ul><li>A relevant case is for bio-inspired computing, see Babaoglu et al. [Babaoglu, 2006]. </li></ul></ul><ul><ul><li>Another inspiring filed is sociology [Edmonds, 2005]. </li></ul></ul><ul><li>We provide algorithmic approach to the pattern </li></ul><ul><ul><li>can be instantiated in different “flavours” by simple variation of Interaction and Migration rules. </li></ul></ul><ul><ul><li>synchronous algorithmic realization does not prevent for application in a realistic, asynchronous environment, no synchronization step required to update agent’s strategies and group membership. </li></ul></ul>
    13. 13. Experiments Scenario <ul><li>For the experiments here we have implemented a collective interactions, corresponding to a VO policy alignment scenario </li></ul><ul><ul><li>The payoff is calculated on the </li></ul></ul><ul><ul><li>alignment level over the whole VO </li></ul></ul><ul><ul><li>and payoffs are shared collectively. </li></ul></ul><ul><ul><li>As for the migration phase, the agents compare their performance against their own past performance (internal learning). Migration to a group implies the copying the policy of a random agent in target group. </li></ul></ul>Experiments conducted in an open source, agent-based Grid simulator [AgentGridSim, 2007]. p1,p2 U ↑ U ↓ U ↓ U ↑
    14. 14. VO policy alignment scenario Groups with P1 and groups with P2: high utility Agents interact, migrate, mutate Groups with P1 and P2 : low utility t= 100 t= 40 t=1
    15. 15. Coordination metric <ul><li>The metric we employ to measure the alignment degree is the Shannon Entropy Index . </li></ul><ul><li>The goal is to minimize diversity (entropy) within each VO, achieving the highest policy alignment possible inside each VO. </li></ul>
    16. 16. Utility (coordination) N agents=100 ; M policies =10; migration/mutation:0.3, 0.7 sim clock ticks m/m m/m low ratio migration/mutation rate achieves better performance
    17. 17. Groups Distribution (I) low migration/mutation rate (when higher utilities achieved) causes large number of groups m/m m/m
    18. 18. Groups Distribution (II) For a migration/mutation ratio, higher utilities with smaller groups m/m = 0.3 utility = 0.55 utility = 0.72
    19. 19. Conclusions <ul><li>Formalization of a Group Selection pattern for Grid VOs coordination. </li></ul><ul><li>Experiments show:“ Group selection evolves large number of small and dynamic VOs into optimized outcomes in a VO policy alignment scenario” </li></ul><ul><li>Future Work: </li></ul><ul><ul><li>Is the mechanism truly relevant to realistic Grids applications? </li></ul></ul><ul><ul><li>If yes, is it possible an implementation in realistic Grid settings ? </li></ul></ul>
    20. 20. References <ul><li>[Foster, 2001] I. Foster, C. Kesselman, S. Tuecke. The Anatomy of the Grid: Enabling Scalable Virtual Organizations. International J. Supercomputer Applications, 15(3), 2001 </li></ul><ul><li>[Foster. 2005] Globus Toolkit Version 4: Software for Service-Oriented Systems. I. Foster. IFIP International Conference on Network and Parallel Computing, Springer Verlag LNCS 3779, pp 2-13, 2005 </li></ul><ul><li>[Babaoglu, 2006] O. Babaoglu, G. Canright, A. Deutsch, G. Di Caro, F. Ducatelle, L. Gambardella, N. Ganguly, M. Jelasity, R. Montemanni, A. Montresor and T. Urnes. Design Patterns from Biology for Distributed Computing . In ACM Transactions on Autonomous and Adaptive Systems, vol. 1, no. 1, 26--66, September 2006. </li></ul><ul><li>[Edmonds, 2005] Edmonds, B., Gilbert, N., Gustafson, S., Hales, D. and Krasnogor, N. (eds.) (2005) Socially Inspired Computing. Proceedings of the Joint Symposium on Socially Inspired Computing , University of Hertfordshire, Hatfield, UK 12 - 15 April 2005, Published by AISB </li></ul><ul><li>[HODGSON04] Geoffrey M. Hodgson and Thorbjorn Knudsen, &quot;The firm as an interactor: firms as vehicles for habits and routines&quot;, Journal of Evolutionary Economics 14 (2004): 281—307 </li></ul><ul><li>[GOWDY03] Jonh Gowdy and Irmi Seidl. Economic Man and Selfish Genes: The Relevance of Group Selection to Economic Policy,” Journal of Socio-Economics 33(3), 2004, 343-358. </li></ul><ul><li>[BOWLES04]Bowles S., Gintis H. (2004). The Evolution of Strong Reciprocity, Theoretical Population Biology 65, 2004, 17-28. </li></ul><ul><li>[BOYD03] R. Boyd, H. Gintis, S. Bowles, and P. J. Richerson. The Evolution of Altruistic Punishment. Proceedings of the National Academy of Sciences (USA) 100: 3531–3535, 2003 </li></ul><ul><li>[KNOESTER07] David B. Knoester, Philip K. McKinley, Charles Ofria: Using group selection to evolve leadership in populations of self-replicating digital organisms. GECCO 2007: 293-300 </li></ul><ul><li>[HALES05] Hales, D. & Patarin, S. (2005) Feature: Computational Sociology for Systems &quot;In the Wild&quot;: The Case of BitTorrent. IEEE Distributed Systems Online, vol. 6, no. 7, 2005 </li></ul>
    21. 21. End Questions?

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