计算机科学
计算卸载
边缘计算
计算
移动边缘计算
GSM演进的增强数据速率
建筑
移动设备
人工智能
分布式计算
万维网
算法
艺术
视觉艺术
作者
Alok Choudhury,Manojit Ghose,Akhirul Islam,Yogita Thakran
标识
DOI:10.1016/j.sysarc.2024.103090
摘要
The advancement of technology towards the realization of the evolving mobile computing paradigm brings a rapid paradigm shift in its usage, especially in the Internet, computation, and communications, that has a profound impact on businesses, services, and users. With the rise in resource-intensive or edge-based mobile applications such as autonomous driving, Amazon Go, virtual and augmented reality, and healthcare-related applications, countless challenges in computation and communication parameters like latency, bandwidth, and energy consumption are evolving. As a result, the multi-access edge computing (MEC) paradigm receives enormous attention where some portions of the user applications are offloaded to powerful machines for their efficient execution to optimize different evaluation metrics or to achieve performance goals. While a few survey works are available in this direction, none of them focuses explicitly on the emerging machine learning (ML) based computation offloading techniques and various associated sub-problems together. This paper aims to provide a detailed but precise overview of the research on using ML techniques for MEC environments. In this survey, we focus on how authors and researchers utilize the ML models in computation offloading problems on MEC architecture. We extend our study by considering several edge architectures, offloading parameters, ML approaches, and problem formulation strategies concerning computation offloading. Additionally, this paper discusses the potential challenges in the direction of computation offloading on MEC architecture.
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