Fog and edge computing: concepts, tools and focus areas

Hurbungs, V.; Bassoo, Vandana; Fowdur, Tulsi Pawan

doi:10.1007/s41870-020-00588-5

Cited by 28 publications

(5 citation statements)

References 36 publications

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“…In the first part, we implemented the A* star algorithm and observed the time taken using the algorithm. The first step in doing so was to choose a cloud service [26]. Here, we chose Google Cloud Platform (GCP) for this purpose, as they offered a free tier and an interactive console.…”

Section: Resultsmentioning

confidence: 99%

Development of Cloud Autonomous System for Enhancing the Performance of Robots’ Path

et al. 2023

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With the development of computer technology and artificial intelligence (AI), service robots are widely used in our daily life. At the same time, the manufacturing cost of the robots is too expensive for almost all small companies. The greatest technical limitations are the design of the service robot and the resource sharing of the robot groups. Path planning for robots is one of the issues playing an important role in every application of service robots. Path optimization, fast computation, and minimum computation time are required in all applications. This paper aims to propose the Google Cloud Computing Platform and Amazon Web Service (AWS) platforms for robot path planning. The aim is to identify the effect and impact of using a cloud computing platform for service robots. The cloud approach shifts the computation load from robots to the cloud server. Three different path-planning algorithms were considered to find the path for robots using the Google Cloud Computing Platform, while with AWS, three different types of environments, namely dense, moderate, and sparse, were selected to run the path-planning algorithms for robots. The paper presents the comparison and analysis of the results carried out for robot path planning using cloud services with that of the traditional approach. The proposed approach of using a cloud platform performs better in this case. The time factor is crucially diagnosed and presented in the paper. The major advantage derived from this experiment is that as the size of the environment increases, the respective relative delay decreases. This proves that increasing the scale of work can be beneficial by using cloud platforms. The result obtained using the proposed methodology proves that using cloud platforms improves the efficiency of path planning. The result reveals that using the cloud computing platform for service robots can change the entire perspective of using service robots in the future. The main advantage is that with the increase in the scale of services, the system remains stable, while the traditional system starts deteriorating in terms of performance.

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Section: Resultsmentioning

confidence: 99%

Development of Cloud Autonomous System for Enhancing the Performance of Robots’ Path

et al. 2023

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“…It was found that compared to baselines during the rush hours, the proposed approach could reduce the service delay by 8%-20% and the migration cost by more than 75%. Finally in Hurbungs et al (2021), the authors performed a detailed literature review on the edge, fog and cloud computing paradigms and in Hurbungs and Bassoo (2022) an enhanced binary classifier for edge devices was proposed.…”

Section: Related Workmentioning

confidence: 99%

Performance analysis of edge, fog and cloud computing paradigms for real-time video quality assessment and phishing detection

Fowdur

Abdoolla

Doobur

2023

IJPCC

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Purpose The purpose of this paper is to perform a comparative analysis of the delay associated in running two real-time machine learning-based applications, namely, a video quality assessment (VQA) and a phishing detection application by using the edge, fog and cloud computing paradigms. Design/methodology/approach The VQA algorithm was developed using Android Studio and run on a mobile phone for the edge paradigm. For the fog paradigm, it was hosted on a Java server and for the cloud paradigm on the IBM and Firebase clouds. The phishing detection algorithm was embedded into a browser extension for the edge paradigm. For the fog paradigm, it was hosted on a Node.js server and for the cloud paradigm on Firebase. Findings For the VQA algorithm, the edge paradigm had the highest response time while the cloud paradigm had the lowest, as the algorithm was computationally intensive. For the phishing detection algorithm, the edge paradigm had the lowest response time, and the cloud paradigm had the highest, as the algorithm had a low computational complexity. Since the determining factor for the response time was the latency, the edge paradigm provided the smallest delay as all processing were local. Research limitations/implications The main limitation of this work is that the experiments were performed on a small scale due to time and budget constraints. Originality/value A detailed analysis with real applications has been provided to show how the complexity of an application can determine the best computing paradigm on which it can be deployed.

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“…Transmitting the overwhelming IoT data to the cloud would cause network overhead, consuming bandwidth, and latency issues [ 167 ]. Hence, to cut back on the data transfer cost as well as network delays, service providers are steering towards the fog and edge computing [ 168 ], with an additional opportunity for enforcing security and privacy [ 169 ]. The IoT systems comprise edge equipment, sensors, and actuators with latency, bandwidth, and security necessities [ 166 ].…”

Section: Confluence Of ML and Fog/edgementioning

confidence: 99%

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Angel

Ravindran

Vincent

et al. 2021

Sensors

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Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical constraints of novel IoT applications generating vast data, which entails a swift response time with improved privacy. The newest drift is moving computational and storage resources to the edge of the network, involving a decentralized distributed architecture. The data processing and analytics perform at proximity to end-users, and overcome the bottleneck of cloud computing. The trend of deploying machine learning (ML) at the network edge to enhance computing applications and services has gained momentum lately, specifically to reduce latency and energy consumed while optimizing the security and management of resources. There is a need for rigorous research efforts oriented towards developing and implementing machine learning algorithms that deliver the best results in terms of speed, accuracy, storage, and security, with low power consumption. This extensive survey presented on the prominent computing paradigms in practice highlights the latest innovations resulting from the fusion between ML and the evolving computing paradigms and discusses the underlying open research challenges and future prospects.

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Fog and edge computing: concepts, tools and focus areas

Cited by 28 publications

References 36 publications

Development of Cloud Autonomous System for Enhancing the Performance of Robots’ Path

Development of Cloud Autonomous System for Enhancing the Performance of Robots’ Path

Performance analysis of edge, fog and cloud computing paradigms for real-time video quality assessment and phishing detection

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

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