Using Artificial Neural Network For Monitoring And Supporting The Grid Scheduler Performance
Daniel Grzonka, Joanna Kołodziej, Jie Tao · 2014
Task scheduling and resource allocations are the key issues for computational grids. Distributed resources usually work at different autonomous domains with their own access and security policies that impact successful job executions across the domain boundaries. In this paper, we propose an Artificial Neural Network (ANN) approach for supporting the security awareness of evolutionary driven grid schedulers. Making a prior analysis of the trust levels of resources and security demand parameters of tasks, the neural network monitors the scheduling and task execution processes. In the result produce the tasks-machines mapping “suggestions”, which can be then utilized by the scheduler to reduce the makespan or increase the system throughput. In this paper, we report the development of risk-resilient genetic-based schedulers and their integration with an ANN module of the HyperSim-G Grid Simulator to evaluate the proposed model under the heterogeneity and large-scale system dynamics. The simulation results showed a significant impact of the ANN support on enhancing the effectiveness of the genetic-based meta-heuristics in reducing the cost of security awareness in grid scheduling.