Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions

Naha, Ranesh Kumar; Garg, Saurabh; Georgakopoulos, Dimitrios; Jayaraman, Prem Prakash; Gao, Longxiang; Xiang, Yong; Ranjan, Rajiv

doi:10.1109/access.2018.2866491

Cited by 425 publications

(276 citation statements)

References 81 publications

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“…As the proposed architecture utilizes large scale parallelization, no result is obtained in terms of area requirement for a larger number of cities. However, it is well suited for real time applications like fog and cloud computing and scheduling [14], where the number of cities is typically smaller.…”

Section: Discussionmentioning

confidence: 99%

FPGA Implementation of Crossover Module of Genetic Algorithm

Attarmoghaddam

Kanan

2019

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This paper proposes a hardware realization of the crossover module in the genetic algorithm for the travelling salesman problem (TSP). In order to enhance performance, we employ a combination of pipelining and parallelization with a genetic algorithm (GA) processor to improve processing speed, as compared to software implementation. Simulation results showed that the proposed architecture is six times faster than the similar existing architecture. The presented field-programmable gate array (FPGA) implementation of PMX crossover operator is more than 400 times faster than in software.

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Section: Discussionmentioning

confidence: 99%

FPGA Implementation of Crossover Module of Genetic Algorithm

Attarmoghaddam

Kanan

2019

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“…For this reason, the latency of communication for the cloud is higher when compared to fog. Cloud is more centralized while fog is regionally coordinated and has dispersed solution [90].…”

Section: Technical Comparison Of Cloud Fog and Edge Computingmentioning

confidence: 99%

A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing

et al. 2020

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Cloud computing is an innovative technology that deploys networks of servers, located in wide remote areas, for performing operations on a large amount of data. In cloud computing, a workflow model is used to represent different scientific and web applications. One of the main issues in this context is scheduling large workflows of tasks with scientific standards on the heterogeneous cloud environment. Other issues are particular to public cloud computing. These include the need for the user to be satisfied with the quality of service (QoS) parameters, such as scalability and reliability, as well as maximize the end-users resource utilization rate. This paper surveys scheduling algorithms based on particle swarm optimization (PSO). This is aimed at assisting users to decide on the most suitable QoS consideration for large workflows in infrastructure as a service (IaaS) cloud applications and mapping tasks to resources. Besides, the scheduling schemes are categorized according to the variant of the PSO algorithm implemented. Their objectives, characteristics, limitations and testing tools have also been highlighted. Finally, further directions for future research are identified.

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“…Fog computing is a computing paradigm that integrates with the cloud infrastructure. It creates a computing facility for IoT services or other latency-sensitive applications through a distributed architecture [22].…”

Section: Overview Of Fog Computing With Iotmentioning

confidence: 99%

“…The end devices within a fog framework are decentralized [25]. If it is improperly optimized and programmed, it could lead to inaccurate data [22]. Data precision is essential in classifying the types of sound that are being recorded by the devices.…”

Section: Open Issues For Iot-fog Frameworkmentioning

confidence: 99%

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A scalable IoT-fog framework for urban sound sensing

Baucas

Spachos

2020

Computer Communications

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Internet of Things (IoT) is a system of interrelated devices that can be used to allow large-scale collection and analysis of data. However, as it grew, IoT networks were not capable of managing the data from these services. As a result, cloud computing was introduced to address the need for datacentres for IoT networks. As the technology evolved, the demand for a proper means of supporting and managing crowdsensing and real-time data increased, and cloud servers could no longer keep up with the large volumes of incoming data. This demand brought rise to fog computing. It became an extension to the cloud and allowed resources to be allocated around the network effectively. Its integration to IoT reduced the strain towards the cloud servers. However, issues in high power consumption at the end device and data management constraints surfaced. This paper proposes two approaches to alleviate these issues to keep fog computing remain as a reliable option for IoT-related applications. We created an IoT-based sensing framework that used an urban sound classification model. Through active low and high power states and resource reallocation, we created a network configuration. We tested this configuration against IoT frameworks that use the default fog and cloud setups. The results improved the framework's end device power consumption and server latency. Overall, with the proposed framework, fog computing was proven to be capable of supporting a scalable IoT framework for urban sound sensing.

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Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions

Cited by 425 publications

References 81 publications

FPGA Implementation of Crossover Module of Genetic Algorithm

FPGA Implementation of Crossover Module of Genetic Algorithm

A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing

A scalable IoT-fog framework for urban sound sensing

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