Scheduling Internet of Things requests to minimize latency in hybrid Fog–Cloud​ computing

Aburukba, Raafat; AliKarrar, Mazin; Landolsi, Taha; El-Fakih, Khaled

doi:10.1016/j.future.2019.09.039

Cited by 176 publications

(96 citation statements)

References 30 publications

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“…Many efforts were addressed addressing the communication and interoperability problems caused by the information silos arising from the diversity of the IoT system in the field of Electronic Health Records. Aburukba et al [159] emphasized that fog computing having a small communication delay, and when compared to the latency deployment feature of cloud and fog computing technologies, cloud computing has high communication latency deployment. Jaleel et al [160] proposed a novel framework that integrates cloud, edge, and fog computing technologies in order to provide interoperability and optimal bounded requirements.…”

Section: Remote Services Layermentioning

confidence: 99%

Analysis of factors affecting IoT-based smart hospital design

2020

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Currently, rapidly developing digital technological innovations affect and change the integrated information management processes of all sectors. The high efficiency of these innovations has inevitably pushed the health sector into a digital transformation process to optimize the technologies and methodologies used to optimize healthcare management systems. In this transformation, the Internet of Things (IoT) technology plays an important role, which enables many devices to connect and work together. IoT allows systems to work together using sensors, connection methods, internet protocols, databases, cloud computing, and analytic as infrastructure. In this respect, it is necessary to establish the necessary technical infrastructure and a suitable environment for the development of smart hospitals. This study points out the optimization factors, challenges, available technologies, and opportunities, as well as the system architecture that come about by employing IoT technology in smart hospital environments. In order to do that, the required technical infrastructure is divided into five layers and the system infrastructure, constraints, and methods needed in each layer are specified, which also includes the smart hospital’s dimensions and extent of intelligent computing and real-time big data analytic. As a result of the study, the deficiencies that may arise in each layer for the smart hospital design model and the factors that should be taken into account to eliminate them are explained. It is expected to provide a road map to managers, system developers, and researchers interested in optimization of the design of the smart hospital system.

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Section: Remote Services Layermentioning

confidence: 99%

Analysis of factors affecting IoT-based smart hospital design

2020

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“…Aburukba et al [43] study on the optimization of weighted sum of all request delays with deadline constraints in IoTedge-cloud environments to allocate an edge-cloud resource (processor) for each task's execution. They formulate the optimization problem as a Mixed Integer Linear Programming (MILP) problem which is NP-Hard, and propose a genetic algorithm to solve the MILP problem, where each gene represents a map between a task and an edge-cloud resource.…”

Section: ) All Offloading A: Response Time Optimizationmentioning

confidence: 99%

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Wang

Huang

et al. 2020

IEEE Access

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Edge-cloud computing, combining the benefits of both edge computing and cloud computing, is one of the most promising ways to address the resource insufficiency of smart devices. Task offloading is an important challenge must be addressed for edge-cloud computing in practice, which decides the place and the time for performing each task. Even though there is existing research focusing on the task offloading in edge-cloud computing, a lot of problems should be solved before the application of these offloading technologies. Thus, in this article, we first propose a taxonomy of task offloading in edge-cloud environments to investigate and classify related research articles, and then summarize several challenges which have not been addressed for future research directions on this area to promote the development of edge-cloud market.

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“…Thus, a new architecture needs to be considered, due to the following reasons: Latency: many IoT applications interacted with the real environment through sensing and actuation, which makes these applications latency-critical applications, such as e-health applications. Integrating IoT applications with cloud computing alone is not enough due to the distance of cloud computing resources of IoT devices, therefore, integrating cloud with edge computing solves this issue as discussed in the next subsection [ 44 , 45 ]. Data size: the increasing number of IoT devices means more bandwidth needed to transmit IoT data to the cloud especially when smartphones are capable to send streaming videos and photos to the cloud, which leads to bringing cloud-like services near to the end-user to reduce the bandwidth required to transmit IoT data [ 46 ].…”

Section: Key Survey Topicsmentioning

confidence: 99%

“…Latency: many IoT applications interacted with the real environment through sensing and actuation, which makes these applications latency-critical applications, such as e-health applications. Integrating IoT applications with cloud computing alone is not enough due to the distance of cloud computing resources of IoT devices, therefore, integrating cloud with edge computing solves this issue as discussed in the next subsection [ 44 , 45 ].…”

Section: Key Survey Topicsmentioning

confidence: 99%

Edge-Computing Architectures for Internet of Things Applications: A Survey

Hamdan

Ayyash

Almajali

2020

Sensors

118

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The rapid growth of the Internet of Things (IoT) applications and their interference with our daily life tasks have led to a large number of IoT devices and enormous sizes of IoT-generated data. The resources of IoT devices are limited; therefore, the processing and storing IoT data in these devices are inefficient. Traditional cloud-computing resources are used to partially handle some of the IoT resource-limitation issues; however, using the resources in cloud centers leads to other issues, such as latency in time-critical IoT applications. Therefore, edge-cloud-computing technology has recently evolved. This technology allows for data processing and storage at the edge of the network. This paper studies, in-depth, edge-computing architectures for IoT (ECAs-IoT), and then classifies them according to different factors such as data placement, orchestration services, security, and big data. Besides, the paper studies each architecture in depth and compares them according to various features. Additionally, ECAs-IoT is mapped according to two existing IoT layered models, which helps in identifying the capabilities, features, and gaps of every architecture. Moreover, the paper presents the most important limitations of existing ECAs-IoT and recommends solutions to them. Furthermore, this survey details the IoT applications in the edge-computing domain. Lastly, the paper recommends four different scenarios for using ECAs-IoT by IoT applications.

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Scheduling Internet of Things requests to minimize latency in hybrid Fog–Cloud computing

Cited by 176 publications

References 30 publications

Analysis of factors affecting IoT-based smart hospital design

Analysis of factors affecting IoT-based smart hospital design

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Edge-Computing Architectures for Internet of Things Applications: A Survey

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Scheduling Internet of Things requests to minimize latency in hybrid Fog–Cloud​ computing

Cited by 176 publications

References 30 publications

Analysis of factors affecting IoT-based smart hospital design

Analysis of factors affecting IoT-based smart hospital design

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Edge-Computing Architectures for Internet of Things Applications: A Survey

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Product

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Scheduling Internet of Things requests to minimize latency in hybrid Fog–Cloud computing