Fault‐tolerant with load balancing scheduling in a fog‐based IoT application

Sharif, Ahmad; Nickray, Mohsen; Shahidinejad, Ali

doi:10.1049/iet-com.2020.0080

Cited by 31 publications

(22 citation statements)

References 37 publications

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“…Fault tolerance is very important concept to maintain the reliability and performance of the system [39]. Fog and edge computing paradigms provide an effective fault tolerant service to run the system continually without any interruption by fixing the faults dynamically [40]. Further, there is a need to find out the software, network or hardware failure and their reasons and fixes to improve the reliability of service [41].…”

Section: Fault Tolerance (Reliability)mentioning

confidence: 99%

A Manifesto for Modern Fog and Edge Computing: Vision, New Paradigms, Opportunities, and Future Directions

Gill

2021

EAI/Springer Innovations in Communication and Computing

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The advancements in the use of Internet of Things (IoT) devices is increasing continuously and generating huge amounts of data in a fast manner. Cloud computing is an important paradigm which processes and manages user data effectively. Further, fog and edge computing paradigms are introduced to improve user service by reducing latency and response time. This chapter presents a manifesto for modern fog and edge computing systems based on the current research trends. Further, architectures and applications of fog and edge computing are explained.Moreover, research opportunities and promising future directions are presented with respect to the new paradigms, which will be helpful for practitioners, researchers and academicians to continue their research.

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Section: Fault Tolerance (Reliability)mentioning

confidence: 99%

A Manifesto for Modern Fog and Edge Computing: Vision, New Paradigms, Opportunities, and Future Directions

Gill

2021

EAI/Springer Innovations in Communication and Computing

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“…In addition, some solutions for enabling network reliability and faulttolerance between IoT and fog nodes have been proposed [127,128]. Furthermore, some initial work in fault-tolerance for fog computing has been conducted [129][130][131][132]. However, there are more issues to be addressed with regard to achieving fault-tolerant fog computing for large IoT-based environments such as smart cities [133].…”

Section: Reliabilitymentioning

confidence: 99%

Applications of Integrated IoT-Fog-Cloud Systems to Smart Cities: A Survey

et al. 2021

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Several cities have recently moved towards becoming smart cities for better services and quality of life for residents and visitors, with: optimized resource utilization; increased environmental protection; enhanced infrastructure operations and maintenance; and strong safety and security measures. Smart cities depend on deploying current and new technologies and different optimization methods to enhance services and performance in their different sectors. Some of the technologies assisting smart city applications are the Internet of Things (IoT), fog computing, and cloud computing. Integrating these three to serve one system (we will refer to it as integrated IoT-fog-cloud system (iIFC)) creates an advanced platform to develop and operate various types of smart city applications. This platform will allow applications to use the best features from the IoT devices, fog nodes, and cloud services to deliver best capabilities and performance. Utilizing this powerful platform will provide many opportunities for enhancing and optimizing applications in energy, transportation, healthcare, and other areas. In this paper we survey various applications of iIFCs for smart cities. We identify different common issues associated with utilizing iIFCs for smart city applications. These issues arise due to the characteristics of iIFCs on the one side and the requirements of different smart city applications on the other. In addition, we outline the main requirements to effectively utilize iIFCs for smart city applications. These requirements are related to optimization, networking, and security.

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“…The offloading decision is to execute a request in the edge layer or the cloud layer. The main components of offloading manager in the smart gateway are monitoring and offloading decision 34 . The offloading decision component has two inputs; monitoring and requests.…”

Section: Proposed Approachmentioning

confidence: 99%

Joint computation offloading and resource provisioning for edge‐cloud computing environment: A machine learning‐based approach

Shahidinejad

Ghobaei‐Arani

2020

Softw Pract Exp

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Summary In recent years, the usage of smart mobile applications to facilitate day‐to‐day activities in various domains for enhancing the quality of human life has increased widely. With rapid developments of smart mobile applications, the edge computing paradigm has emerged as a distributed computing solution to support serving these applications closer to mobile devices. Since the submitted workloads to the smart mobile applications changes over the time, decision making about offloading and edge server provisioning to handle the dynamic workloads of mobile applications is one of the challenging issues into the resource management scope. In this work, we utilized learning automata as a decision‐maker to offload the incoming dynamic workloads into the edge or cloud servers. In addition, we propose an edge server provisioning approach using long short‐term memory model to estimate the future workload and reinforcement learning technique to make an appropriate scaling decision. The simulation results obtained under real and synthetic workloads demonstrate that the proposed solution increases the CPU utilization and reduces the execution time and energy consumption, compared with the other algorithms.

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Fault‐tolerant with load balancing scheduling in a fog‐based IoT application

Cited by 31 publications

References 37 publications

A Manifesto for Modern Fog and Edge Computing: Vision, New Paradigms, Opportunities, and Future Directions

A Manifesto for Modern Fog and Edge Computing: Vision, New Paradigms, Opportunities, and Future Directions

Applications of Integrated IoT-Fog-Cloud Systems to Smart Cities: A Survey

Joint computation offloading and resource provisioning for edge‐cloud computing environment: A machine learning‐based approach

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