The fast growth of under developing internet-based technologies has been leading to propose promising methods to handle the heterogeneous massive volume of data produced by pervasive smart equipments such as handy mobile devices. Thanks to the mentioned technologies, these mobile devices can run critical business/entertainment applications such as Augmented Reality, Virtual Reality, vehicular networks, and media streaming. However, due to such devices' inherent limitations, some emerging computation environments such as Mobile Edge Computing have been introduced to achieve some essential requirements such as low latency, low energy consumption, and low cost. In the literature, offloading is a technique to transfer the burden of the mobile devices' work incurred by running applications' requests to these computation environments. On the other hand, exploring the computation environment to find the most efficient place to execute such requests is challenging work to achieve. In addition, different researches have been proposed to cope with the management problems of the offloading criterion. In this paper, an autonomous computation offloading framework is proposed to address some challenges related to time-intensive and resource-intensive applications. However, to the best of the authors’ knowledge, the proposed autonomous framework has not been explored as a control model for self-management in the computation offloading criterion. Besides, to cope with the large dimension of the offloading decision-making problem, different simulations including Deep Neural Networks, multiple linear regression, hybrid model, and Hidden Markov Model as the planning module of the mentioned autonomous methodology have been conducted. Simulation results show that the proposed hybrid model can appropriately fit the problem with near-optimal accuracy regarding the offloading decision-making, the latency, and the energy consumption predictions in the proposed self-management framework.

正在发展中的基于互联网的技术的快速增长已导致提出有前途的方法，以处理由诸如手持式移动设备之类的普及智能设备所产生的异构海量数据。由于提到的技术，这些移动设备可以运行关键的业务/娱乐应用程序，例如增强现实，虚拟现实，车辆网络和媒体流。但是，由于此类设备的固有局限性，一些新兴的计算环境（例如移动边缘计算）已被引入以实现一些基本要求，例如低延迟，低能耗和低成本。在文献中，卸载是一种将运行应用程序的请求所引起的移动设备工作负担转移到这些计算环境的技术。另一方面，探索计算环境以找到最有效的位置来执行此类请求是一项艰巨的工作。另外，已经提出了不同的研究来应对卸载标准的管理问题。在本文中，提出了一种自主计算分流框架，以解决与时间密集型和资源密集型应用程序相关的一些挑战。但是，据作者所知，在计算分流标准中，尚未探索提出的自治框架作为自我管理的控制模型。此外，为了应对卸载决策问题的大范围需求，我们提供了不同的模拟功能，包括深度神经网络，多元线性回归，混合模型，隐马尔可夫模型作为上述自主方法的计划模块已经进行。仿真结果表明，所提出的混合模型可以在自我管理框架中，以减负决策，等待时间和能耗预测为准，以近乎最佳的精度解决问题。