440 research outputs found
A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art
Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research
The edge cloud: A holistic view of communication, computation and caching
The evolution of communication networks shows a clear shift of focus from
just improving the communications aspects to enabling new important services,
from Industry 4.0 to automated driving, virtual/augmented reality, Internet of
Things (IoT), and so on. This trend is evident in the roadmap planned for the
deployment of the fifth generation (5G) communication networks. This ambitious
goal requires a paradigm shift towards a vision that looks at communication,
computation and caching (3C) resources as three components of a single holistic
system. The further step is to bring these 3C resources closer to the mobile
user, at the edge of the network, to enable very low latency and high
reliability services. The scope of this chapter is to show that signal
processing techniques can play a key role in this new vision. In particular, we
motivate the joint optimization of 3C resources. Then we show how graph-based
representations can play a key role in building effective learning methods and
devising innovative resource allocation techniques.Comment: to appear in the book "Cooperative and Graph Signal Pocessing:
Principles and Applications", P. Djuric and C. Richard Eds., Academic Press,
Elsevier, 201
5G Radio Resource Allocation for Communication and Computation Offloading
Edge computing is envisioned as a key enabler in future cellular networks by bringing the computing, networking and storage resources closer to the end users and enabling offloading for computation-intensive or latency-critical tasks coming from the emerging 5G/6G applications. Such technology also introduces additional challenges when it comes to deciding when to offload or not since the dynamic wireless environment plays a significant role in the overall communication and computation costs when offloading workload to the nearby edge nodes. In this paper, we focus on the communication cost in computation offloading via wireless channels, by formulating an α -fair utility-based radio resource allocation (RRA) problem tailored for offloading in a multi-user urban scenario where the uplink connection is the main focus. We begin by modeling the wireless channel with large- and small-scale fading at both lower and millimetre-wave frequencies, followed by data rate calculation based on 3GPP for a more realistic approach. Then, while assessing the fairness of the RRA, we simulate the resource allocation framework while taking into account both users who need to offload and users who are only interested in high downlink data rates. Simulation results show that the weighted proportional fairness method adapted for computation offloading can provide a good trade-off between fairness and performance compared to other benchmark schemes
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