Enabling effective Mobile Edge Computing using millimeterwave links

The Internet of Things (IoT) has recently advanced from an experimental technology to what will become the backbone of future customer value for both product and service sector businesses. This underscores the cardinal role of IoT on the journey towards the fifth generation (5G) of wireless communication systems. IoT technologies augmented with intelligent and big data analytics are expected to rapidly change the landscape of myriads of application domains ranging from health care to smart cities and industrial automations. The emergence of Multi-Access Edge Computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency access to radio network resources. IoT is identified as a key use case of MEC, given MEC's ability to provide cloud platform and gateway services at the network edge. MEC will inspire the development of myriads of applications and services with demand for ultra low latency and high Quality of Service (QoS) due to its dense geographical distribution and wide support for mobility. MEC is therefore an important enabler of IoT applications and services which require real-time operations. In this survey, we provide a holistic overview on the exploitation of MEC technology for the realization of IoT applications and their synergies. We further discuss the technical aspects of enabling MEC in IoT and provide some insight into various other integration technologies therein.

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“…Low latency and high reliability [112], [113] Use of mmWave spectrum for high data rate access to MEC servers and backhaul links.…”

Section: Technical Aspects Of Mec Enabled Iotmentioning

confidence: 99%

Survey on Multi-Access Edge Computing for Internet of Things Realization

Porambage

Okwuibe

Liyanage

et al. 2018

IEEE Commun. Surv. Tutorials

620

328

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“…The challenge coming from the use of mmWave links is that they are more prone to blocking events [21], which may jeopardize the benefits of computation offloading. A possible way to counteract blocking events in a MEC system using mmWave links was proposed in [22], [19].…”

Section: Multiple Users Served By a Single Ap/mec Pairmentioning

confidence: 99%

The Edge Cloud: A Holistic View of Communication, Computation, and Caching

Barbarossa

Sardellitti

Ceci

et al. 2018

Cooperative and Graph Signal Processing

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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.

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“…Due to the proxility of the mobile devices to the MEC server [2], this architecture has the potential to significantly reduce latency, avoid congestion and prolong the battery lifetime of mobile devices by running demanding applications and processing tasks at the network edge, where ample computational and storage resources remain available [3]. Recently, MEC has gained considerable interest within the research community [4]- [14]. In [4] and [5], the authors derived the optimal resource allocation solution for a singleuser MEC system engaged in multiple elastic tasks, aiming to minimize the average execution latency of all tasks under power constraints.…”

Section: Introductionmentioning

confidence: 99%

Efficient Resource Allocation for Relay-Assisted Computation Offloading in Mobile-Edge Computing

Chen

Shi

Zhao

et al. 2020

IEEE Internet Things J.

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In this article, we consider the problem of relay assisted computation offloading (RACO), in which user A aims to share the results of computational tasks with another user B through wireless exchange over a relay platform equipped with mobile edge computing capabilities, referred to as a mobile edge relay server (MERS). To support the computation offloading, we propose a hybrid relaying (HR) approach employing two orthogonal frequency bands, where the amplify-and-forward scheme is used in one band to exchange computational results, while the decode-and-forward scheme is used in the other band to transfer the unprocessed tasks. The motivation behind the proposed HR scheme for RACO is to adapt the allocation of computing and communication resources both to dynamic user requirements and to diverse computational tasks. Within this framework, we seek to minimize the weighted sum of the execution delay and the energy consumption in the RACO system by jointly optimizing the computation offloading ratio, the bandwidth allocation, the processor speeds, as well as the transmit power levels of both user A and the MERS, under practical constraints on the available computing and communication resources. The resultant problem is formulated as a non-differentiable and nonconvex optimization program with highly coupled constraints. By adopting a series of transformations and introducing auxiliary variables, we first convert this problem into a more tractable yet equivalent form. We then develop an efficient iterative algorithm for its solution based on the concave-convex procedure. By exploiting the special structure of this problem, we also propose a simplified algorithm based on the inexact block coordinate descent method, with reduced computational complexity. Finally, we present numerical results that illustrate the advantages of the proposed algorithms over state-of-the-art benchmark schemes.

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Enabling effective Mobile Edge Computing using millimeterwave links

Cited by 37 publications

References 11 publications

Survey on Multi-Access Edge Computing for Internet of Things Realization

Survey on Multi-Access Edge Computing for Internet of Things Realization

The Edge Cloud: A Holistic View of Communication, Computation, and Caching

Efficient Resource Allocation for Relay-Assisted Computation Offloading in Mobile-Edge Computing

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