Artificial Neural Network support to monitoring of the evolutionary driven security aware scheduling in computational distributed environments

• We have developed ANN-based model for monitoring and supporting the grid schedulers.
• We have developed six security-aware genetic schedulers.
• Our model was evaluated under the heterogeneity and large-scale simulated system.
• Schedulers supported by ANN achieved better results.
• The number of the genetic epochs has been reduced.

Monitoring of the system performance in highly distributed computing environments is a wide research area. In cloud and grid computing, it is usually restricted to the utilization and reliability of the resources. However, in today’s Computational Grids (CGs) and Clouds (CCs), the end users may define the special personal requirements and preferences in the resource and service selection, service functionality and data access. Such requirements may refer to the special individual security conditions for the protection of the data and application codes. Therefore, solving the scheduling problems in modern distributed environments remains still challenging for most of the well known schedulers, and the general functionality of the monitoring systems must be improved to make them efficient as schedulers supporting modules.In this paper, we define a novel model of security-driven grid schedulers supported by an Artificial Neural Network (ANN). ANN module monitors the schedule executions and learns about secure task–machine mappings from the observed machine failures. Then, the metaheuristic grid schedulers (in our case—genetic-based schedulers) are supported by the ANN module through the integration of the sub-optimal schedules generated by the neural network, with the genetic populations of the schedules.The influence of the ANN support on the general schedulers’ performance is examined in the experiments conducted for four types of the grid networks (small, medium, large and very large grids), two security scheduling modes—risky and secure scenarios, and six genetic-based grid schedulers. The generated empirical results show the high effectiveness of such monitoring support in reducing the values of the major scheduling criteria (makespan and flowtime), the run times of the schedulers and the grid resource failures.