عنوان انگلیسی مقاله:
A Survey on the Computation Offloading Approaches in Mobile Edge Computing: A Machine Learning-based Perspective
ترجمه فارسی عنوان مقاله:
بررسی رویکردهای بارگیری محاسبات در محاسبات لبه موبایل: دیدگاه مبتنی بر یادگیری ماشین
Sciencedirect - Elsevier - Computer Networks, 182 (2020) 107496. doi:10.1016/j.comnet.2020.107496
Ali Shakarami, Mostafa Ghobaei-Arani *, Ali Shahidinejad
With the rapid developments in emerging mobile technologies, utilizing resource-hungry mobile applications
such as media processing, online Gaming, Augmented Reality (AR), and Virtual Reality (VR) play an essential
role in both businesses and entertainments. To soften the burden of such complexities incurred by fast developments
of such serving technologies, distributed Mobile Edge Computing (MEC) has been developed, aimed
at bringing the computation environments near the end-users, usually in one hop, to reach predefined requirements.
In the literature, offloading approaches are developed to connect the computation environments to
mobile devices by transferring resource-hungry tasks to the near servers. Because of some rising problems such as
inherent software and hardware heterogeneity, restrictions, dynamism, and stochastic behavior of the ecosystem,
the computation offloading issues consider as the essential challenging problems in the MEC environment.
However, to the best of the author’s knowledge, in spite of its significance, in machine learning-based (ML-based)
computation offloading mechanisms, there is not any systematic, comprehensive, and detailed survey in the MEC
environment. In this paper, we provide a review on the ML-based computation offloading mechanisms in the
MEC environment in the form of a classical taxonomy to identify the contemporary mechanisms on this crucial
topic and to offer open issues as well. The proposed taxonomy is classified into three main fields: Reinforcement
learning-based mechanisms, supervised learning-based mechanisms, and unsupervised learning-based mechanisms.
Next, these classes are compared with each other based on the essential features such as performance
metrics, case studies, utilized techniques, and evaluation tools, and their advantages and weaknesses are discussed,
as well. Finally, open issues and uncovered or inadequately covered future research challenges are
argued, and the survey is concluded.
Keywords: Computation offloading | Mobile edge computing | Machine learning | Reinforcement learning | Supervised learning | Unsupervised learning