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International Journal of Internet of Things and its Applications

Volume 2, No. 2, 2018, pp 1-6
http://dx.doi.org/10.21742/ijiota.2018.2.2.01

Abstract



Performance Estimation Method on IoT-Cloud Environments Using Hybrid Deep Neural Network



    Yunsik Son1, Seman Oh2 ,Yangsun Lee3*
    13Dept. of Computer Science and Engineering, Dongguk University, 3-26 Pil-dong, Jung-gu, Seoul 100-715, Korea
    2Dept. of Computer Engineering, Seokyeong University, 16-1 Jungneung-Dong, Sungbuk-Ku, Seoul 136-704, Korea
    *corresponding author,
    1sonbug@dongguk.edu, 2smoh@dongguk.edu, 3yslee@skuniv.ac.kr

    Abstract

    The IoT-Cloud virtual machine system is a virtual machine-based solution for low-performance IoT (Internet of Things) devices. It uses an offloading method that delegates tasks with high computational complexity to a high-performance cloud server. The offloading technique can reduce the execution performance depending on the workload of the IoT devices and the clouds. Therefore, it is necessary to decide offloading execution considering the workload of the IoT devices and the clouds.
    In this paper, CPU utilization trend, which is one of the workload indices, is predicted through deep learning in order to decide offloading execution considering the workload of the IoT devices and clouds. The predicted CPU utilization trend is indicative of future CPU utilization information and is therefore an indicator for offloading execution decisions.


 

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