Deep learning is a powerful technology that enables intelligent data processing in the smart healthcare domain. Inspired by the tremendous processing power of cloud computing, the training process and the model repository of deep learning are moved to the cloud. Cloud-assisted deep learning applications enable smart mobile users to experience quick predictive results. Health professionals use smart mobile devices to convey recordings of the patient and to receive the best inference results. The mobility of these devices causes severe performance degradation as it increases the distance between its current location and the edge cloud where the virtual machines are provisioned. Therefore, mobility-based resource provisioning to identify a suitable server based on deadline constraints, available resources, and cost metrics is crucial. This paper proposes a proximity-based resource provisioning technique that guarantees minimal delay in obtaining inference results with a local mobile cloud system. The proposed technique comprises two algorithms (a) deadline-based initial resource provisioning and (b) resource migration and provisioning at suitable cloudlet during location change. The proposed technique is implemented in a mobile cloud platform running the inference method of a smart mobile healthcare application. The performance results show that the proposed technique outperforms the state-of-the-art techniques in terms of the response time, deadline meeting percentage, and system utilization.

中文翻译：

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智能医疗中深度学习应用的基于邻近的云资源配置

深度学习是一项强大的技术，可在智能医疗领域实现智能数据处理。受云计算强大处理能力的启发，深度学习的训练过程和模型库都迁移到了云端。云辅助深度学习应用程序使智能移动用户能够体验快速的预测结果。卫生专业人员使用智能移动设备来传达患者的记录并获得最佳推理结果。这些设备的移动性会导致严重的性能下降，因为它会增加其当前位置与配置虚拟机的边缘云之间的距离。因此，基于移动性的资源配置以根据截止日期限制、可用资源和成本指标来识别合适的服务器是至关重要的。本文提出了一种基于邻近度的资源供应技术，该技术可确保使用本地移动云系统获得推理结果的延迟最小。所提出的技术包括两种算法（a）基于期限的初始资源供应和（b）在位置变化期间在合适的小云处进行资源迁移和供应。所提出的技术在运行智能移动医疗保健应用程序的推理方法的移动云平台中实现。性能结果表明，所提出的技术在响应时间、截止日期满足百分比和系统利用率方面优于最先进的技术。所提出的技术包括两种算法（a）基于期限的初始资源供应和（b）在位置变化期间在合适的小云处进行资源迁移和供应。所提出的技术在运行智能移动医疗保健应用程序的推理方法的移动云平台中实现。性能结果表明，所提出的技术在响应时间、截止日期满足百分比和系统利用率方面优于最先进的技术。所提出的技术包括两种算法（a）基于期限的初始资源供应和（b）在位置变化期间在合适的小云处进行资源迁移和供应。所提出的技术在运行智能移动医疗保健应用程序的推理方法的移动云平台中实现。性能结果表明，所提出的技术在响应时间、截止日期满足百分比和系统利用率方面优于最先进的技术。