Edge Computing Perspectives: Architectures, Technologies, and Open Security Issues

Caprolu, Maurantonio; Pietro, Roberto Di; Lombardi, Flavio; Raponi, Simone

doi:10.1109/edge.2019.00035

Cited by 159 publications

(68 citation statements)

References 15 publications

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“…It integrates network, computing, storage and the core capabilities of applications, providing the nearest service nearby, and generating faster network service responses [25]. Edge Computing is a dispatched computing mode that can address some inherent issues in cloud computing like resource centralization by withdrawing computing and storage resources from centralized points, which in other words, can be viewed as pushing applications, data, and services closer to those request senders geographically, perfectly overcoming intolerable transmission delay [26].…”

Section: Edge Computingmentioning

confidence: 99%

Dynamic resource provisioning for cyber-physical systems in cloud-fog-edge computing

Zhang

et al. 2020

J Cloud Comp

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Cyber-Physical Systems(CPS) serves as an interdisciplinary effort that incorporates cyber vector as well as physical vector. The latter can generate exponentially growing amounts of data. How to process CPS big data systematically and efficiently is the key to the breakthrough of offering prospective and personalized services for each individual user and entity involved. In recent years, much research has been proactively conducted on advancing specific scenario or algorithm. However, few surveys value their integration. For good measure, the synthesis remains a fundamental challenge. Subsequently, we fill the gap in the literature by constructing cloud computing, fog computing and edge computing as a whole to inspire on new architectures and cross utilizations. Moreover, bringing the enthusiasm of traditionally solitude entities into play is crucial. In this exploratory study, we examine definitions of CPS as well as the three aforementioned computing paradigms and then shed new light on comprehensively established frameworks. We also survey on the application level of Cloud-Fog-Edge Computing in CPS respectively and dive into diversified algorithms and strategies to embed big data applications into a more intelligent and convenient society with current deficiencies and future research directions followed.

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Section: Edge Computingmentioning

confidence: 99%

Dynamic resource provisioning for cyber-physical systems in cloud-fog-edge computing

Zhang

et al. 2020

J Cloud Comp

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“…Containerization, Orchestration, 5G, AI & Machine Learning, etc. are the technologies that are being used currently, and in these scenarios, it is just adaptation of these technologies in edge computing framework [2]. Accordingly, there is a vast set of opensource frameworks of these technologies.…”

Section: A Overviewmentioning

confidence: 99%

“…Other surveys have studied basic characteristics, research challenges, and opportunities of different edge computing paradigms [19][20][21][22]. Caprolu et al [2] discussed security issues about supporting technologies for the edge computing, and Dustdar et al [23] presented detailed individual characteristics and use cases along with certain future challenges of edge and fog computing paradigms. Alrowaily et al [24] reviewed the concepts, characteristics, security, and edge computing IoT-driven applications besides the security features of a data-driven world.…”

Section: B Related Workmentioning

confidence: 99%

“…To ensure the security and privacy on edge and fog devices in a network, researchers have to assure the most important characteristics such as confidentiality, integrity, and accessibility [2]. Among these characteristics, confidentiality and integrity provide data privacy guarantee while accessibility assures the sharing of the resource between edge nodes whenever required [159].…”

Section: ) Security and Privacymentioning

confidence: 99%

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Edge Computing and Its Convergence With Blockchain in 5G and Beyond: Security, Challenges, and Opportunities

2020

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“…Edge computing is a shared application mode with communication, computing, and storage functions. Compared with cloud computing, edge computing mode is closer to connected devices in geography, which can effectively solve the problems of time extension and network congestion in cloud computing mode [4], [5]. However, compared to cloud servers, the resources and service scope of a single edge server are limited.…”

Section: Introductionmentioning

confidence: 99%

Distributed Edge Computing Offloading Algorithm Based on Deep Reinforcement Learning

Wang

et al. 2020

IEEE Access

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As a mode of processing task request, edge computing paradigm can reduce task delay and effectively alleviate network congestion caused by the proliferation of Internet of things(IoT) devices compared with cloud computing. However, in the actual construction of the network, there are various edge autonomous subnets in the adjacent areas, which leads to the possibility of unbalance of server load among autonomous subnets during the peak period of task request. In this paper, a deep reinforcement learning algorithm is proposed to solve the complex computation offloading problem for the heterogeneous Edge Computing Server(ECS) collaborative computing. The problem is solved based on the real-time state of the network and the attributes of the task, which adopts Actor Critic and Policy Gradient's Deep Deterministic Policy Gradient(DDPG) to make optimized decisions of computation offloading. Considering multi-task, the heterogeneity of edge subnet and mobility of edge devices, the proposed algorithm can learn the network environment and generate the computation offloading decision to minimize the task delay.The simulation results show that the proposed DDPG-based algorithm is competitive compared with the Deep Q Network(DQN) algorithm and Asynchronous Advantage Actor-Critic(A3C) algorithm. Moreover, the optimal solutions are leveraged to analyze the influence of edge network parameters on task delay. INDEX TERMS Edge computing, computation offload, collaborative computing, reinforcement learning, DDPG.

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Edge Computing Perspectives: Architectures, Technologies, and Open Security Issues

Cited by 159 publications

References 15 publications

Dynamic resource provisioning for cyber-physical systems in cloud-fog-edge computing

Dynamic resource provisioning for cyber-physical systems in cloud-fog-edge computing

Edge Computing and Its Convergence With Blockchain in 5G and Beyond: Security, Challenges, and Opportunities

Distributed Edge Computing Offloading Algorithm Based on Deep Reinforcement Learning

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