Hypertuming GRU Neural Networks for Edge Resource Usage Prediction

John Violos, Stylianos Tsanakas, T. Theodoropoulos, Aris Leivadeas, K. Tserpes, T. Varvarigou
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引用次数: 3

Abstract

The proliferation of Internet of Things (IoT) and edge devices constitute important an efficient orchestration of the edge computing infrastructures, calling the providers to rethink their decision making methods. The resource usage prediction can be a prominent source of information for adaptive resource allocation and task offloading. In this research, we propose a Gated Recurrent Neural Network multi-output regression model that leverage time series resource usage metrics. The edge computing infrastructures are characterized as dynamical and heterogeneous environments. This motivated us to propose the innovative Hybrid Bayesian Evolutionary Strategy (HBES) algorithm for automated adaptation of the resource usage models in order to to enhance the generality of our approach. The proposed resource usage prediction mechanism has been experimentally evaluated and compared with other state of the art methods with significant improvements in terms of RMSE and MAE.
边缘资源使用预测的超调GRU神经网络
物联网(IoT)和边缘设备的激增构成了边缘计算基础设施的重要高效编排,要求提供商重新思考其决策方法。资源使用预测可以作为自适应资源分配和任务卸载的重要信息来源。在这项研究中,我们提出了一种利用时间序列资源使用指标的门控递归神经网络多输出回归模型。边缘计算基础设施具有动态和异构环境的特点。这促使我们提出了创新的混合贝叶斯进化策略(HBES)算法,用于自动适应资源使用模型,以提高我们方法的通用性。所提出的资源利用预测机制已经过实验评估,并与其他最先进的方法进行了比较,在RMSE和MAE方面有了显着改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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