A Survey on Edge Computing Systems and Tools

Liu, Fang; Tang, Guoming; Li, Youhuizi; Cai, Zixing; Zhang, Xingzhou; Zhou, Tongqing

doi:10.1109/jproc.2019.2920341

Cited by 224 publications

(93 citation statements)

References 69 publications

(62 reference statements)

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“…A survey on EC for Internet of things (IoT) is investigated in [22]. Liu et al [23] surveyed systems and tools in EC. Peng et al [24] studied service adaptation and provision in mobile EC (MEC).…”

Section: Dementioning

confidence: 99%

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

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Section: Dementioning

confidence: 99%

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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“…CloudPath [38] is an edge computing system that is consists of a set of shortcycle and stateless functions to support a path computing architecture. EdgeX framework is proposed for IoT edge computing to support connection with heterogeneous sensors and devices based on different protocols, and management of devices and data [39]. EdgeX framework is consists of multiple server modules that provide microservices to allows services to scale up and down based on the capability of the edge gateway.…”

Section: Related Workmentioning

confidence: 99%

Secure Edge Computing Management Based on Independent Microservices Providers for Gateway-Centric IoT Networks

Jin

You

et al. 2020

IEEE Access

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Edge computing is an emerging computing paradigm that distributes the computational capability to the edge of networks for enabling the computation near to the environment where the sensors and actuators are deployed. Therefore, from the network edge, heterogeneous solutions can be provided to the Internet based on sufficient computing ability. Nevertheless, computing and networking resources are constrained for devices in the network edge. Providing secure services from edge computing is a challenge based on constrained resources. In this paper, we propose a secure edge computing to provide management of device, data, user and additional services based on deploying independent microservices providers with a security gateway on an edge gateway. The edge gateway is the hub of a local network where multiple IoT devices are deployed to interact with the physical environment for sensing and actuating. The gateway provides the management functionalities through microservices based on multiple independent server modules. Each gateway-centric local network has a standalone management service based on the gateway. For providing secure edge computing services through the edge gateway, a security gateway is deployed on the proposed edge gateway to provide Representational State Transfer Application Programming Interfaces to expose the security services to the Internet instead of microservices from management modules. Moreover, a client support gateway is deployed in the edge gateway to provide services of User Interface and access forwarding based on web sessions to support user authentication and authorization with the security gateway. Based on the proposed edge gateway including client support and security gateway, IoT clients and IoT devices are enabled to communicate for providing secure edge services of access and visualization to users.

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“…The edge computing brings the location of data storage and processing, near to the end device, instead of sending the produced data of the end devices to the remote data centers. 6,7 In edge computing, the data are sent through the local gateways to the MEC servers to be processed or preprocessed. 8,9 If the data processing is finished, then the processing results are sent to the user.…”

Section: Mobile Edge Computingmentioning

confidence: 99%

A proposed computation, which benefits from the cooperation of dew, edge, and cloud computations

Abdoos

Taheri

Taeezadeh

2019

Trans Emerging Tel Tech

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We propose a computation that compensates the weakness of the processing power and storage capacity of the dew and edge servers. In the proposed computation, the nodes near to each other, which can be the dew, edge, or cloud servers, form a computational grid. The dew servers can be mobile devices, laptops, or computers. Their processing power and storage capacity can be used when they are idle. Our goal is to achieve load balancing of different nodes (servers). A server of the grid with proper processing power, storage capacity, remained energy, and low task load is selected as the grid head (each server of grid shares its specification with the other servers of grid periodically). The grid head (server) is responsible for grid communications. The grid head shares the specification of the grid, with the neighbor nodes (we suppose that the end device is aware of the specification of the nearest dew, edge, and cloud grid heads to itself). The end device decides about sending its created tasks to the grid head of a dew or edge or cloud grid, with attention to the distance between itself and the grid head, and the free processing power and storage capacity of the grid. The scheduling algorithm of a grid is the combination of first‐come–first‐served and priority scheduling algorithms. The simulation results show that the proposed computation has less delay and energy consumption and more efficiency than the cloud and edge computations.

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A Survey on Edge Computing Systems and Tools

Cited by 224 publications

References 69 publications

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Secure Edge Computing Management Based on Independent Microservices Providers for Gateway-Centric IoT Networks

A proposed computation, which benefits from the cooperation of dew, edge, and cloud computations

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