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Organizations Partitioning Optimization

Organizations Partitioning Optimization. Ammar Lahlouhi e-mail: ammar.lahlouhi@gmail.com MASA-Group, Department of Computer Science University of Batna, Algeria. Outline. Methodological Societies Organizations Partitioning OP as Graph Partitioning GP in Parallel Computing

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Organizations Partitioning Optimization

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  1. Organizations Partitioning Optimization Ammar Lahlouhi e-mail: ammar.lahlouhi@gmail.com MASA-Group, Department of Computer Science University of Batna, Algeria

  2. Outline • Methodological Societies • Organizations Partitioning • OP as Graph Partitioning • GP in Parallel Computing • Particles Approach • Adaptation of PA to OP • Conclusion Sheraton Centre Toronto Hotel, may 10, 2010

  3. Methodological Societies • A challenge is to view Self-* systems as system able • to develop solutions and • to deploy them • autonomously • Such systems carry out a methodology • We call them methodological systems • Their aim is to tackle some questions that current adaptive systems do not provide satisfactory answers • Our objective is the engineering of methodological systems Sheraton Centre Toronto Hotel, may 10, 2010

  4. Organizations Partitioning • A multi-agent realization of the methodological systems integrates application agents but also developers agents • The behavior of the developers is directed by a methodology • They implement its stages • In a role based organizational methodology, a key step is an association of roles and agents (Organization Partitioning) • We propose an OP based on a coherent optimization Sheraton Centre Toronto Hotel, may 10, 2010

  5. OP as Graph Partitioning • Abstractly, we can view an organization as a graph of roles • OP can be then partitioned as we partition a graph • GP is a significant problem with extensive applications to many areas • It is well known as NP-hard • GP can be formulated as follows • Given a graph G = (Vertices V, Edges E) • split V into k (k > 1) partitions V1, V2 … Vn covering V • i.e., V1 U V2 U … U Vn = V • The goal is to minimize the edge-cut • i.e., the number of edges of E whose incident vertices belong to different partitions Sheraton Centre Toronto Hotel, may 10, 2010

  6. GP in Parallel computing • Representation: • The nodes represent tasks • The edges represent communications between tasks • Goal: • Partition the tasks in k • k is the processors number • equilibrated partitions • i.e., number of tasks by partition is approximately equal • while minimizing the edge-cut • Solutions: • Three classes of solutions Sheraton Centre Toronto Hotel, may 10, 2010

  7. Particles Approach (1) • Heiss proposed the Particles Approach to GP for parallel computing • it combines the benefits of the three classes of solutions • while avoiding local minima • PA is based on a physical model using the notion of forces • The forces correspond to independent optimization goals (criteria) • PA is inspired from flat container containing viscous fluids • It considers the parallel computations as fluids • with tasks as particles Sheraton Centre Toronto Hotel, may 10, 2010

  8. ti Particles Approach (2) • The load potential at each node can be used to define a gravitational force • Communication relations along with their intensities are associated with the cohesion forces in direction and magnitude • Costs of tasks migration act as frictional resistance and are also working counter to load balancing Sheraton Centre Toronto Hotel, may 10, 2010

  9. ti Particles Approach (3) • For the criteria • load balancing (lb) • communication cost (com) • migration cost (friction fric, here) • we have a resultant (res) function F Sheraton Centre Toronto Hotel, may 10, 2010

  10. Particles approach (3) The direct application of PA to OP poses some difficulties Then we adapted PA to OP Functions evaluations Choosing candidate node Conditional migration Sheraton Centre Toronto Hotel, may 10, 2010

  11. PA Adaptation to OP (2) Multi-criteria functions evaluations Functions evaluation Searching non dominated node verifying the preventive constraints Choosing candidate node if chosen node is not the source s, migrate and re-establish coherence Conditional migration Sheraton Centre Toronto Hotel, may 10, 2010

  12. PA Adaptation to OP (3) Multi-criteria functions evaluations Searching non dominating node verifying the preventive constraints if chosen node is not the source s, migrate and re-establish coherence corrective constraints Sheraton Centre Toronto Hotel, may 10, 2010

  13. Conclusion • Advantages of PA’s use: continually evolving organization • Some originalities • The organization definition's extension • partitioning criteria and constraints • Improvement of MAS quality and automation • The adaptation: • adopting multi-criteria optimization, and • making explicit the partitioning constraints and criteria, • managing the evolution's coherence (preventive and corrective management of the coherence) • Unlimited number of constraints and criteria which can be conflicting • can be defined at the development time • Further work is needed to reach the objectives of the methodological societies Sheraton Centre Toronto Hotel, may 10, 2010

  14. Thank you

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