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Edge computing

About: Edge computing is a research topic. Over the lifetime, 11657 publications have been published within this topic receiving 148533 citations.


Papers
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Journal ArticleDOI
TL;DR: An edge caching and computation management problem that jointly optimizes the service caching, the request scheduling, and the resource allocation strategies is formulated that achieves a close-to-optimal delay performance without relying on any prior knowledge of the future network information.
Abstract: Vehicular Edge Computing (VEC) is expected to be an effective solution to meet the ultra-low delay requirements of many emerging Internet of Vehicles (IoV) services by shifting the service caching and the computation capacities to the network edge. However, due to the constraints of the multidimensional (storage-computing-communication) resources capacities and the cost budgets of vehicles, there are two main issues need to be addressed: 1) How to collaboratively optimize the service caching decision among edge nodes to better reap the benefits of the storage resource and save the time-correlated service reconfiguration cost? 2) How to allocate resources among various vehicles and where vehicular requests are scheduled to improve the efficiency of the computing and communication resources utilization? In this paper, we formulate an edge caching and computation management problem that jointly optimizes the service caching, the request scheduling, and the resource allocation strategies. Our focus is to minimize the time-average service response delay of the random arriving service requests in a cost-efficient way. To cope with the dynamic and unpredictable challenges of IoVs, we leverage the combined power of Lyapunov optimization, matching theory, and consensus alternating direction method of multipliers to solve the problem in an online and distributed manner. Theoretical analysis shows that the developed approach achieves a close-to-optimal delay performance without relying on any prior knowledge of the future network information. Moreover, simulation results validate the theoretical analysis and demonstrate that our algorithm outperforms the baselines substantially.

96 citations

Journal ArticleDOI
TL;DR: A container scheduling system that enables serverless platforms to make efficient use of edge infrastructures and a method to automatically fine-tune the weights of scheduling constraints to optimize high-level operational objectives such as minimizing task execution time, uplink usage, or cloud execution cost is presented.

95 citations

Journal ArticleDOI
TL;DR: A distributed computation offloading strategy for a multi-device and multi-server system based on orthogonal frequency-division multiple access in SCNs is studied and it is proved that the proposed algorithm can effectively minimize the overhead of each MD compared with different other existing algorithms.
Abstract: Mobile edge computing is conceived as an appealing technology to enhance cloud computing capability of mobile devices (MDs) at the edge of the networks. Although some researchers use the technology to address the intensive tasks’ high computation needs of MDs in small-cell networks (SCNs), most of them ignore considering the interests interaction between small cells and MDs. In this paper, we study a distributed computation offloading strategy for a multi-device and multi-server system based on orthogonal frequency-division multiple access in SCNs. First, to satisfy the interest requirements of different MDs and analyze the interactions among multiple small cells, we formulate a distributed overhead minimization problem, aiming at jointly optimizing energy consumption and latency of each MD. Second, to ensure the individuals of different MDs, we formulate the proposed overhead minimization problem as a strategy game. Then, we prove the strategy game is a potential game by the feat of potential game theory. Moreover, the potential game-based offloading algorithm is proposed to reach a Nash equilibrium. In addition, to guarantee the performance of the designed algorithm, we consider the lower bound of iteration times to derive the worst case performance guarantee. Finally, the simulation results corroborate that the proposed algorithm can effectively minimize the overhead of each MD compared with different other existing algorithms.

95 citations

Journal ArticleDOI
TL;DR: The authors show how their converged cloud/fog paradigm not only helps solve the QS problem, but also meets the requirements of a growing number of decentralized services -- an area in which traditional cloud models fall short.
Abstract: In this article, the authors dissect the technical challenges that cities face when implementing smart city plans and outlines the design principles and lessons learned after they carried out a flagship initiative on fog computing in Barcelona. In particular, they analyze what they call the Quadruple Silo (QS) problem -- that is, four categories of silos that cities confront after deploying commercially available solutions. Those silo categories are: physical (hardware) silos, data silos, and service management silos, and the implications of the three silos in administrative silos. The authors show how their converged cloud/fog paradigm not only helps solve the QS problem, but also meets the requirements of a growing number of decentralized services -- an area in which traditional cloud models fall short. The article exposes cases in which fog computing is a must, and shows that the reasons for deploying fog are centered much more on operational requirements than on performance issues related to the cloud.

95 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a comprehensive analysis of the use of cutting-edge artificial intelligence-based classification and prediction techniques employed for edge intelligence for health data classification with the tracking and identification of vital signs using state of the art deep learning techniques.
Abstract: With the advent of new technologies and the fast pace of human life, patients today require a sophisticated and advanced smart healthcare framework that is tailored to suit their individual health requirements. Along with 5G and state-of-the-art smart Internet of Things (IoT) sensors, edge computing provides intelligent, real-time healthcare solutions that satisfy energy consumption and latency criteria. Earlier surveys on smart healthcare systems were centered on cloud and fog computing architectures, security, and authentication, and the types of sensors and devices used in edge computing frameworks. They did not focus on the healthcare IoT applications deployed within edge computing architectures. The first purpose of this study is to analyze the existing and evolving edge computing architectures and techniques for smart healthcare and recognize the demands and challenges of different application scenarios. We examine edge intelligence that targets health data classification with the tracking and identification of vital signs using state-of-the-art deep learning techniques. This study also presents a comprehensive analysis of the use of cutting-edge artificial intelligence-based classification and prediction techniques employed for edge intelligence. Even with its many advantages, edge intelligence poses challenges related to computational complexity and security. To offer a higher quality of life to patients, potential research recommendations for improving edge computing services for healthcare are identified in this study. This study also offers a brief overview of the general usage of IoT solutions in edge platforms for medical treatment and healthcare.

95 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,471
20223,274
20212,978
20203,397
20192,698
20181,649