Cloud, Fog, and Edge Computing: A Software Engineering Perspective

Al-Qamash, Amal; Soliman, Iten; Abulibdeh, Rawan; Saleh, Moutaz

doi:10.1109/comapp.2018.8460443

Cited by 12 publications

(17 citation statements)

References 24 publications

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“…The systems based on such paradigms offer many opportunities, such as context-driven m-learning [13], and improve mlearning efficiency. Edge-based learning opened new windows for processing data locally at multiple endpoints instead of centralized clouds [25,26]. While AI-based ITSs teach course content using personalized methods [14], augmented reality mobile applications make learning conscious [27].…”

Section: M-learning Applications and Lmsmentioning

confidence: 99%

“…academic institutions, edge computing has emerged to make the data computation faster but closer to the m-learning actors. Edge computing is a novel trend of decentralizing data processing at multiple endpoints instead of centralized datacentres or clouds [25,26]. Edge is the immediate first hop from locally distributed IoT devices [19].…”

Section: M-learning Applications Andmentioning

confidence: 99%

“…Edge is the immediate first hop from locally distributed IoT devices [19]. It ensures privacy, low latency, resource optimization, location-aware services, and reduced network traffic [25]. Hence, integrating m-learning, IoT devices, and edge computing enables highly efficient m-learning performance and promotes intelligent educational environments.…”

Section: M-learning Applications Andmentioning

confidence: 99%

“…m-learning applications based on IoT-edge computing platforms are growing extensively, and they can be deployed and coupled with institutions' micro datacentres [20,22]. Edge-cloud m-learning applications are developed to facilitate resource-poor devices and support educational resources widely available [25]. Educational institutions utilize edge-cloud architectures to improve m-learning efficiency and QoS for m-learning actors [38].…”

Section: M-learning Applications Andmentioning

confidence: 99%

See 3 more Smart Citations

Mobile Learning New Trends in Emerging Computing Paradigms: An Analytical Approach Seeking Performance Efficiency

Mohiuddin

Miladi

Khan

et al. 2022

Wireless Communications and Mobile Computing

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Mobile learning (m-learning) adoption has increased and shall be demonstrated superior performance by implementing related computing paradigms, such as IoT, edge, mobile edge, fog, AI, and 5G. Mobile cloud architectures (MCAs) enable m-learning with several benefits and face limitations while executing real-time applications. This study investigates the state-of-the-art m-learning architectures, determines a layered m-learning-MCA obtaining numerous benefits of related computing paradigms, and expands m-learning functional structure. It evaluates m-learning performance across the four physical layer’s MCAs—distance cloud, cloudlet, operator-centric cloud, ad hoc cloud, and emerging computing architectures. Surprisingly, only distance-cloud MCA is adopted for developing m-learning systems by ignoring the other three. Performance evaluation shows m-learning gets terrific benefits and users QoE in related computing paradigms. Mobile edge computing offers ultralow latency, whereas the current architecture improves task execution time (1.87, 2.01, 2.63, and 3.97) for the resource-intensive application (i.e., 4.2 MB). Fog using AI algorithms is exceptional for more complex learning objects, IoT is superior for intelligent learning tools, and 5G ultrawideband services are more significant for intelligent video analytics. These findings help learners, educators, and institutions adopt an appropriate model for achieving their academic objectives across educational disciplines. The presented approach enables future research to design innovative architectures considering resource-intensive m-learning application execution requirements, such as video content analytics and virtual reality learning models.

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Section: M-learning Applications and Lmsmentioning

confidence: 99%

Section: M-learning Applications Andmentioning

confidence: 99%

Section: M-learning Applications Andmentioning

confidence: 99%

Section: M-learning Applications Andmentioning

confidence: 99%

See 2 more Smart Citations

Mobile Learning New Trends in Emerging Computing Paradigms: An Analytical Approach Seeking Performance Efficiency

Mohiuddin

Miladi

Khan

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…Some research studies have therefore suggested combining this architectural pattern (microservices) with various computational paradigms (cloud, fog or edge) [22], [23] in various health scenarios, with the aim of significantly improving the accuracy of medical diagnosis by establishing predictions based on expert systems or other types of artificial intelligence.…”

Section: Related Workmentioning

confidence: 99%

Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

et al. 2020

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This article describes the design, development and implementation of a set of microservices based on an architecture that enables detection and assisted clinical diagnosis within the field of infectious diseases of elderly patients, via a telemonitoring system. The proposed system is designed to continuously update a medical database fed with vital signs from biosensor kits applied by nurses to elderly people on a daily basis. The database is hosted in the cloud and is managed by a flexible microservices software architecture. The computational paradigms of the edge and the cloud were used in the implementation of a hybrid cloud architecture in order to support versatile high-performance applications under the microservices pattern for the pre-diagnosis of infectious diseases in elderly patients. The results of an analysis of the usability of the equipment, the performance of the architecture and the service concept show that the proposed e-health system is feasible and innovative. The system components are also selected to give a cost-effective implementation for people living in disadvantaged areas. The proposed e-health system is also suitable for distributed computing, big data and NoSQL structures, thus allowing the immediate application of machine learning and AI algorithms to discover knowledge patterns from the overall population.

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Journey from cloud of things to fog of things: Survey, new trends, and research directions

et al. 2022

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With the advent of the Internet of Things (IoT) paradigm, the cloud model is unable to offer satisfactory services for latency-sensitive and real-time applications due to high latency and scalability issues. Hence, an emerging computing paradigm named as fog/edge computing was evolved, to offer services close to the data source and optimize the quality of services (QoS) parameters such as latency, scalability, reliability, energy, privacy, and security of data. This article presents the evolution in the computing paradigm from the client-server model to edge computing along with their objectives and limitations. A state-of-the-art review of Cloud Computing and Cloud of Things (CoT) is presented that addressed the techniques, constraints, limitations, and research challenges. Further, we have discussed the role and mechanism of fog/edge computing and Fog of Things (FoT), along with necessitating amalgamation with CoT. We reviewed the several architecture, features, applications, and existing research challenges of fog/edge computing. The comprehensive survey of these computing paradigms offers the depth knowledge about the various aspects, trends, motivation, vision, and integrated architectures. In the end, experimental tools and future research directions are discussed with the hope that this study will work as a stepping-stone in the field of emerging computing paradigms. K E Y W O R D Scloud computing, internet of things, cloud of things, fog computing, latency INTRODUCTIONDifferent computing paradigms have evolved over the decades as per the needs in various situations. The concept of the Cloud computing paradigm has evolved from distributed systems and it has gained attention by offering a plethora of services to end-users anywhere and anytime on pay per use basis. It has also become the business model in the last decade due to well-known characteristics like scalability, multi-tenant environment, virtualization, and resource pooling. It is observed that a large number of industries, government, as well as private organization, have shifted their applications and data to the cloud platform. Further, the Cloud is also playing a significant role in the advancement of Internet of Things (IoT) applications. As per the transformation and movement in ubiquitous computing, we need the integration

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Cloud, Fog, and Edge Computing: A Software Engineering Perspective

Cited by 12 publications

References 24 publications

Mobile Learning New Trends in Emerging Computing Paradigms: An Analytical Approach Seeking Performance Efficiency

Mobile Learning New Trends in Emerging Computing Paradigms: An Analytical Approach Seeking Performance Efficiency

Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

Journey from cloud of things to fog of things: Survey, new trends, and research directions

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