A review of edge computing: Features and resource virtualization

Mansouri, Yaser; Babar, Mohammad

doi:10.1016/j.jpdc.2020.12.015

Cited by 123 publications

(48 citation statements)

References 187 publications

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“…Integer programming seems to be an inappropriate technique for resource management in cloud, since there is high complexity when the problem size increases [27]- [30]. Therefore, most studies implement heuristics-based or metaheuristics-based solutions for Cloud Computing scenarios [12]. In this paper, we argue that integer programming is an efficient and effective method to find the optimal task allocation for HEµCs where the problem size is tractable.…”

Section: Related Workmentioning

confidence: 97%

“…For practical applicability, we advocate representing and studying resource management in physical, realistic edge environments, rather than using simulation-based tools. Simulations provide an abstraction of the computing environment, which may not be representative of the full scope of uncertainties [11], [12]. In this paper, we describe a testbed which represents a concrete instantiation of the HEµC concept.…”

mentioning

confidence: 99%

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Optimizing Task Allocation for Edge Micro-Clusters in Smart Cities

Alhaizaey

Singer

Michala

2021

2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

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Section: Related Workmentioning

confidence: 97%

mentioning

confidence: 99%

Optimizing Task Allocation for Edge Micro-Clusters in Smart Cities

Alhaizaey

Singer

Michala

2021

2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

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“…The classifier constructed by this method is used as the edge analysis and edge optimization module to predict the attack samples that are in the oil depot dataset. Then the system saves the results to the upper computer to realize the "real-time" feature of edge calculation (Mansouri & Babar, 2021). It is very effective and necessary for time-sensitive and precision-sensitive industrial control systems.…”

Section: Safety Prediction Of An Oil Depotmentioning

confidence: 99%

“…Edge is defined as any computing and network resource node between data source and cloud data center (Zhao, 2020). The process of extending computing services from centralized cloudbased paradigms to network edges is called edge computing (Mansouri & Babar, 2021). In theory, edge computing should be analyzed and processed near the data source (Shi et al, 2016).…”

Section: Safety Prediction Of An Oil Depotmentioning

confidence: 99%

A Method Based on Multiple Population Genetic Algorithm to Select Hyper-Parameters of Industrial Intrusion Detection Classifier

Liu¹,

Wang²,

Zhang³

et al. 2021

STUD INFORM CONTROL

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The security of industrial control systems is increasingly prominent, and the performance of intrusion detection classifiers depends more on hyper-parameters. This paper proposes an improved multiple population genetic algorithm (IMPGA) used to intelligently search hyper-parameters of classifiers, and the simulated annealing algorithm (SAA) is used to control the evolution rate among various populations. In addition, the hash fitness value is used to reduce resource consumption and the directional evolution operator is introduced to optimize the population. This method can effectively avoid the algorithm falling into local optimal solution and save the optimal solution in the process of evolution. Thus, the optimal or approximate optimal combinations of hyper-parameters of classifiers are obtained and the accuracy of the classifiers is finally improved. In this paper, experiments are conducted on the following datasets: the natural gas pipeline experimental dataset of Mississippi State University from 2014 (a gas dataset), the intrusion detection systems dataset of Canadian Institute for Cybersecurity from 2017 (CICIDS2017 dataset) and an oil depot dataset. The experimental results of those three datasets show that the area under curve (AUC) of the back propagation neural network (BPNN) is more than 98%, of the extreme gradient boosting (XGBoost) is more than 99%, and of the support vector machines (SVM) is more than 98%. This selection method can effectively detect the intrusion attacks.

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“…erefore, it has become critical to bring computing power to the edge of the network. Both edge and fog computing efficiently help to obtain a certain satisfaction in terms of quality of service (in IoT applications that demand low latency), high bandwidth, and mobility service that a cloud environment itself cannot offer due to its centralized nature [7][8][9]. is paper reviews evolutionary trends in cloud computing (beginning from federated cloud through edge computing and finally to fog computing) by developing and describing the primary concept around each technology and the revolution that each technology has brought to the IT domain.…”

Section: Introductionmentioning

confidence: 99%

A Review of Evolutionary Trends in Cloud Computing and Applications to the Healthcare Ecosystem

Molo

Badejo

Adetiba

et al. 2021

Applied Computational Intelligence and Soft Computing

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Cloud computing is a technology that allows dynamic and flexible computing capability and storage through on-demand delivery and pay-as-you-go services over the Internet. This technology has brought significant advances in the Information Technology (IT) domain. In the last few years, the evolution of cloud computing has led to the development of new technologies such as cloud federation, edge computing, and fog computing. However, with the development of Internet of Things (IoT), several challenges have emerged with these new technologies. Therefore, this paper discusses each of the emerging cloud-based technologies, as well as their architectures, opportunities, and challenges. We present how cloud computing evolved from one paradigm to another through the interplay of benefits such as improvement in computational resources through the combination of the strengths of various Cloud Service Providers (CSPs), decrease in latency, improvement in bandwidth, and so on. Furthermore, the paper highlights the application of different cloud paradigms in the healthcare ecosystem.

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A review of edge computing: Features and resource virtualization

Cited by 123 publications

References 187 publications

Optimizing Task Allocation for Edge Micro-Clusters in Smart Cities

Optimizing Task Allocation for Edge Micro-Clusters in Smart Cities

A Method Based on Multiple Population Genetic Algorithm to Select Hyper-Parameters of Industrial Intrusion Detection Classifier

A Review of Evolutionary Trends in Cloud Computing and Applications to the Healthcare Ecosystem

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