The increasing deployments of 5G mobile communication system is expected to bring more processing power and storage supplements to Internet of Things (IoT) and mobile devices. It is foreseeable the billions of devices will be connected and it is extremely likely that these devices receive compute supplements from Clouds and upload data to the back-end datacentres for execution. Increasing number of workloads at the Cloud datacentres demand better and efficient strategies of resource management in such a way to boost the socio-economic benefits of the service providers. To this end, this paper proposes an intelligent prediction framework named IGRU-SD (Improved Gated Recurrent Unit with Stragglers Detection) based on state-of-art data analytics and Artificial Intelligence (AI) techniques, aimed at predicting the anticipated level of resource requests over a period of time into the future. Our proposed prediction framework exploits an improved GRU neural network integrated with a resource straggler detection module to classify tasks based on their resource intensity, and further predicts the expected level of resource requests. Performance evaluations conducted on real-world Cloud trace logs demonstrate that the proposed IGRU-SD prediction framework outperforms the existing predicting models based on ARIMA, RNN and LSTM in terms of the achieved prediction accuracy.

中文翻译：

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基于 GRU 的 5G 时代云数据中心智能资源管理预测框架

5G 移动通信系统的部署不断增加，预计将为物联网 (IoT) 和移动设备带来更多的处理能力和存储补充。可以预见，数十亿台设备将被连接起来，这些设备极有可能从云端接收计算补充，并将数据上传到后端数据中心进行执行。云数据中心越来越多的工作负载需要更好、更有效的资源管理策略，以提高服务提供商的社会经济效益。为此，本文基于最先进的数据分析和人工智能 (AI) 技术，提出了一种名为 IGRU-SD（Improved Gated Recurrent Unit with Stragglers Detection）的智能预测框架，旨在预测未来一段时间内资源请求的预期水平。我们提出的预测框架利用改进的 GRU 神经网络与资源落后者检测模块集成，根据资源强度对任务进行分类，并进一步预测资源请求的预期水平。在真实世界的云跟踪日志上进行的性能评估表明，所提出的 IGRU-SD 预测框架在实现的预测精度方面优于基于 ARIMA、RNN 和 LSTM 的现有预测模型。并进一步预测资源请求的预期水平。在真实世界的云跟踪日志上进行的性能评估表明，所提出的 IGRU-SD 预测框架在实现的预测精度方面优于基于 ARIMA、RNN 和 LSTM 的现有预测模型。并进一步预测资源请求的预期水平。在真实世界的云跟踪日志上进行的性能评估表明，所提出的 IGRU-SD 预测框架在实现的预测精度方面优于基于 ARIMA、RNN 和 LSTM 的现有预测模型。