Dynamic task allocation models for large distributed computing systems

Dynamic task allocation for distributed computing systems (DCS) is an important goal to be achieved for engineering applications. The purpose of dynamic task allocation is to increase the system throughput in a dynamic environment, which can be done by balancing the utilization of computing resources and minimizing communication between processors during run time. In this paper, we propose two dynamic task allocation models which are: 1) the clustering simulated annealing model (CSAM); and 2) the mean field annealing model (MFAM). Both of these models combine characteristics of statistical and deterministic approaches. These models provide the rapid convergence characteristic of the deterministic approaches while preserving the solution quality afforded by simulated annealing. Simulation results of the CSAM and MFAM provide a stable and balanced system with 50% and 10% of the convergence time needed by simulated annealing, respectively. The results of this research are important in that it presents the feasibility of applying statistically based task allocation models on large DCSs in a dynamic environment. Solutions of these models depend on the annealing process instead of the structures of the input data, providing the possibility of obtaining better solutions by using more efficient computing hardware