Service Computing Optimization Based on Generalized Particle Dynamics

This paper presents a novel generalized particle model (GPM) for parallel service optimization in distributed service systems. The proposed GPM transforms the allocation of service resources and the assignment of service jobs in distributed service systems into the kinematics and dynamics of massive particles in a force-field. The construction, dynamics and properties of the GPM approach and parallel algorithm GPMA are discussed. The GPM approach has many advantages in terms of the high-scale parallelism, multi-objective optimization, multi-type coordination, multi-degree autonomy, and the ability to deal complex phenomena randomly occurring in distributed service systems. Simulations have shown the effectiveness and suitability of the proposed GPM approach for service computing