Abstract
Mobile Edge Computing (MEC) is an innovative technique, which can provide cloud-computing near mobile devices on the edge of networks. Based on the MEC architecture, this paper proposes an ARIMA-BP-based Selective Offloading (ABSO) strategy, which minimizes the energy consumption of mobile devices while meeting the delay requirements. In ABSO, we exploit an ARIMA-BP model for estimating computation capacity of the edge cloud, and then design a Selective Offloading Algorithm for obtaining offloading strategy. Simulation results reveal that the ABSO can apparently decrease the energy consumption of mobile devices in comparison with other offloading methods.
Highlights
With the popularity of mobile devices, a growing number of mobile applications are striving for computation capacity to provide various services
In this paper,we focus on the problem of computation capacity of the edge cloud, and propose an Auto-Regressive Integrated Moving Average (ARIMA)-Back Propagation (BP)-based Selective Offloading strategy
We develop an ARIMA-BP model to estimate the usage of the edge cloud and calculate the computation capacity
Summary
With the popularity of mobile devices, a growing number of mobile applications are striving for computation capacity to provide various services. Mobile Edge Computing (MEC) is envisioned as an emerging technique to handle this challenge It provides cloud-computing service at the mobile edge network close to mobile devices [4]. In this paper,we focus on the problem of computation capacity of the edge cloud, and propose an ARIMA-BP-based Selective Offloading strategy. We propose a multi-device framework for task offloading in MEC networks, and we formulate an optimization problem which minimizes the energy consumption and concurrently meets the delay constraints. To solve this problem, we devise an efficient strategy, called ABSO
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