1. AOSE’2010, Sheraton Centre Toronto Hotel, 10 May 2010 Organizations Partitioning Optimization Ammar Lahlouhi e-mail: ammar.lahlouhi@gmail.comammar.lahlouhi@gmail.com MASA-Group, Department of Computer Science University of Batna, Algeria

2. Ammar LAHLOUHISheraton Centre Toronto Hotel, may 10, 20102 Outline  Methodological Societies  Organizations Partitioning  OP as Graph Partitioning  GP in Parallel Computing  Particles Approach  Adaptation of PA to OP  Conclusion

3. Ammar LAHLOUHISheraton Centre Toronto Hotel, may 10, 20103 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

4. Ammar LAHLOUHISheraton Centre Toronto Hotel, may 10, 20104 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

5. Ammar LAHLOUHISheraton Centre Toronto Hotel, may 10, 20105 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

6. Ammar LAHLOUHISheraton Centre Toronto Hotel, may 10, 20106 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

7. Ammar LAHLOUHISheraton Centre Toronto Hotel, may 10, 20107 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

8. Ammar LAHLOUHISheraton Centre Toronto Hotel, may 10, 20108 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 titi

9. Ammar LAHLOUHISheraton Centre Toronto Hotel, may 10, 20109 Particles Approach (3)  For the criteria  load balancing (lb)  communication cost (com)  migration cost (friction fric, here) we have a resultant (res) function F titi

10. Ammar LAHLOUHISheraton Centre Toronto Hotel, may 10, 201010 Particles approach (3) Functions evaluations Choosing candidate node Conditional migration

11. Ammar LAHLOUHISheraton Centre Toronto Hotel, may 10, 201011 PA Adaptation to OP (2)

12. Ammar LAHLOUHISheraton Centre Toronto Hotel, may 10, 201012 PA Adaptation to OP (3)

13. Ammar LAHLOUHISheraton Centre Toronto Hotel, may 10, 201013 Conclusion  Advantages of PA’s use: continually evolving organization  Some originalities The organization definition's extension 1.partitioning criteria and constraints 2.Improvement of MAS quality and automation The adaptation: 1.adopting multi-criteria optimization, and 2.making explicit the partitioning constraints and criteria, 3.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