Context-aware multi-user offloading in mobile edge computing: A federated learning-based approach

Mobile edge computing (MEC) provides aneffective solution to help the Internet of Things (IoT)devices with delay-sensitive and computation-intensivetasks by offering computing capabilities in the proximityof mobile device users. Most of the existing studies ignorecontext information of the application, requests, sensors,resources, and network. However, in practice, contextinformation has a significant impact on offloading decisions.In this paper, we consider context-aware offloadingin MEC with multi-user. The contexts are collected usingautonomous management as the MAPE loop in alloffloading processes. Also, federated learning (FL)-basedoffloading is presented. Our learning method in mobiledevices (MDs) is deep reinforcement learning (DRL). FLhelps us to use distributed capabilities of MEC with updatedweights between MDs and edge devices (Eds). Thesimulation results indicate our method is superior to localcomputing, offload, and FL without considering contextawarealgorithms in terms of energy consumption, executioncost, network usage, delay, and fairness

A review on green caching strategies for next generation communication networks

© 2020 IEEE. In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching