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

机译：人工神经网络支持在计算分布式环境中监视进化驱动的安全意识调度

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摘要

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 j 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 exper-iments 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.
机译：在高度分布式的计算环境中监视系统性能是一个广泛的研究领域。在云和网格计算中，通常只限于资源的利用率和可靠性。但是，在当今的计算网格（CG）和云（CC）中，最终用户可以在资源和服务选择，服务功能和j数据访问中定义特殊的个人要求和偏好。此类要求可以参考特殊的个人安全条件来保护数据和应用程序代码。因此，对于大多数众所周知的调度器而言，解决现代分布式环境中的调度问题仍然具有挑战性，并且必须改进监视系统的通用功能以使其高效地用作调度器支持模块。在本文中，我们定义了由人工神经网络（ANN）支持的安全性驱动的网格调度程序的新模型。 ANN模块监视计划执行，并从观察到的机器故障中了解安全的任务机器映射。然后，通过将神经网络生成的次优进度表与进度表的遗传种群进行整合，ANN模块就可以支持元启发式网格进度表（在我们基于案例的遗传算法中）。在针对四种类型的网格网络（小型，中型，大型和超大型网格），两种安全调度模式（风险和安全方案）进行的实验中，研究了ANN支持对总调度程序性能的影响。六个基于遗传的网格调度程序。生成的经验结果表明，这种监视支持在降低主要调度标准（makespan和flowtime），调度程序的运行时间以及网格资源故障的值方面具有很高的效率。

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