Frequency-Reconfigurable Cloud Versus Fog Computing: An Energy-Efficiency Aspect

Hou, Shoulu; Ni, Wei; Zhao, Shuai; Cheng, Bo; Chen, Shiping; Chen, Junliang

doi:10.1109/tgcn.2019.2953891

Cited by 15 publications

(5 citation statements)

References 44 publications

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“…The authors in [8] have proposed optimization methods for fog-assisted and fogcoordinated cloud platforms in order to improve energy conservation performance. In case of a fog-coordinated cloud platform, the raw data among the data centers can be dispatched by the fog nodes.…”

Section: Energy-efficient Fog Computingmentioning

confidence: 99%

“…Numerous studies in the literature have highlighted the significance of ensuring energy efficiency in fog computing, as seen in [7,8]. In [7], the authors have proposed a tree-based fog computing model with the objective of ensuring the energy-efficient distribution of data to servers and fog nodes.…”

Section: Introductionmentioning

confidence: 99%

“…In cases when the logical cluster is unable to accept any new task due to its capacity saturation, the task is assigned to another logical cluster with a lower trust score (these other logical clusters perform as a backup cluster, catering for the task requests). Meanwhile, the authors in [8] have introduced optimization techniques for fog-assisted and fog-coordinated cloud platforms to improve energy saving performance.…”

Section: Introductionmentioning

confidence: 99%

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Green Demand Aware Fog Computing: A Prediction-Based Dynamic Resource Provisioning Approach

et al. 2022

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Fog computing could potentially cause the next paradigm shift by extending cloud services to the edge of the network, bringing resources closer to the end-user. With its close proximity to end-users and its distributed nature, fog computing can significantly reduce latency. With the appearance of more and more latency-stringent applications, in the near future, we will witness an unprecedented amount of demand for fog computing. Undoubtedly, this will lead to an increase in the energy footprint of the network edge and access segments. To reduce energy consumption in fog computing without compromising performance, in this paper we propose the Green-Demand-Aware Fog Computing (GDAFC) solution. Our solution uses a prediction technique to identify the working fog nodes (nodes serve when request arrives), standby fog nodes (nodes take over when the computational capacity of the working fog nodes is no longer sufficient), and idle fog nodes in a fog computing infrastructure. Additionally, it assigns an appropriate sleep interval for the fog nodes, taking into account the delay requirement of the applications. Results obtained based on the mathematical formulation show that our solution can save energy up to 65% without deteriorating the delay requirement performance.

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Section: Energy-efficient Fog Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Green Demand Aware Fog Computing: A Prediction-Based Dynamic Resource Provisioning Approach

et al. 2022

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“…Based on paper (Hou et al, 2020), the author proposes new, optimally distributed online approaches to cloud services that are fog-assisted and fog-coordinated. New distributed techniques are designed to refine the distributed programming models' data dispatching and resource distribution, such as MapReduce, in a completely distributed way, to improve the model energy efficiency while preserving the consistency of the architecture.…”

Section: Work Related To Energy Optimizationmentioning

confidence: 99%

(Offloading) QoE-Aware Application Mapping and Energy-Aware Module Placement in Fog Computing + Offloading

Keat

Ang

Chong

et al. 2022

International Journal of Web Services Research

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Fog computing is a potential solution for the Internet of Things in close connection with things and end-users. Fog computing will easily transfer sensitive data without delaying distributed devices. Moreover, fog computing is more in real-time streaming applications, sensor networks, IoT which need high speed and reliable internet connectivity. Due to the heterogeneous and distributed characteristics, finley distributing the task with computation offloading is a challenging task. Developing an efficient QoE-aware application mapping policy is challenging due to the different user interests. The energy consumption would usually increase after such an algorithm and policy are implemented. In this paper, we enhanced the future from the previous QoE paper by proposing a computation offloading algorithm. The proposed algorithm is to prevent overloading on fog devices. Our proposed solution has been evaluated and compared with other existing solutions, the results show that our proposed solution performs better in terms of execution time, energy consumption, and network usage.

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“…While comparing cloud with fog platform and without fog platform, the former one proved that cloud with fogging helps increasing energy efficiency and reducing queue length of time averaged applications [64]. Drones are significantly used in different fields to capture data.…”

Section: Related Work Based On Internet Of Things and Fogmentioning

confidence: 99%

Application of IoT‐Fog based real‐time monitoring system for open‐cast mines—A survey

Yadav

Mishra²,

Jayanthu

et al. 2021

IET Wireless Sensor Systems

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Fog computing (FC) archetypes is in focus recently for its potential to utilise resources optimally by the Internet of Things (IoT) network bearing ample end-devices. This enhances the quality of service (QoS) to the proximity of end-users as latency-free processing is obtained in cloud-IoT-based environs. Mining industry will encounter a level-up due to these technological advancements like fog-IoT-based systems. Monitoring mining activities such as blasting, slope monitoring, and miner tracking are time-sensitive. Hence, instant response is demand of extreme mine environment so that life and property are not compromised. New opportunities emerge in mining since the FC approach addresses these demands depending on the demand of the situation/user. The parameters of integrating the state-of-art of fog, IoT, and cloud architectures are provided, and a Fog-IoT mines monitoring (FIoTMM) system for real-time monitoring of open-cast mines is proposed. This work also envisions the applications and future trends of FC concerning IoT and cloud. It emphasises how key features of fog help the system in achieving lower network influx, reduced latency, resource utilisation, and immediate data computation and storage because these services take place closer to point of data generation. This data can be immediately visualised/accessed and used to generate early warning in the mines. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Frequency-Reconfigurable Cloud Versus Fog Computing: An Energy-Efficiency Aspect

Cited by 15 publications

References 44 publications

Green Demand Aware Fog Computing: A Prediction-Based Dynamic Resource Provisioning Approach

Green Demand Aware Fog Computing: A Prediction-Based Dynamic Resource Provisioning Approach

(Offloading) QoE-Aware Application Mapping and Energy-Aware Module Placement in Fog Computing + Offloading

Application of IoT‐Fog based real‐time monitoring system for open‐cast mines—A survey

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