Energy-efficient fault-tolerant scheduling in a fog-based smart monitoring application

Sharif, Ahmad; Nickray, Mohsen; Shahidinejad, Ali

doi:10.1504/ijahuc.2021.112978

Cited by 3 publications

(2 citation statements)

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“…For fog and certain other distributed systems, fault prediction and tolerance strategies are investigated. These strategies are divided into two categories: proactive and reactive models [ 15 ]. Included in the proactive model are: Self-healing : is defined as a system’s capacity to have a self-recovery mechanism for errors by employing particular fault recovery techniques on occasion procedures that involve tasks for monitoring.…”

Section: Literature Surveymentioning

confidence: 99%

Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications

Haneef

Neelanarayanan

2023

Sensors

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Technology plays a significant role in our daily lives as real-time applications and services such as video surveillance systems and the Internet of Things (IoT) are rapidly developing. With the introduction of fog computing, a large amount of processing has been done by fog devices for IoT applications. However, a fog device’s reliability may be affected by insufficient resources at fog nodes, which may fail to process the IoT applications. There are obvious maintenance challenges associated with many read-write operations and hazardous edge environments. To increase reliability, scalable fault-predictive proactive methods are needed that predict the failure of inadequate resources of fog devices. In this paper, a Recurrent Neural Network (RNN)-based method to predict proactive faults in the event of insufficient resources in fog devices based on a conceptual Long Short-Term Memory (LSTM) and novel Computation Memory and Power (CRP) rule-based network policy is proposed. To identify the precise cause of failure due to inadequate resources, the proposed CRP is built upon the LSTM network. As part of the conceptual framework proposed, fault detectors and fault monitors prevent the outage of fog nodes while providing services to IoT applications. The results show that the LSTM along with the CRP network policy method achieves a prediction accuracy of 95.16% on the training data and a 98.69% accuracy on the testing data, which significantly outperforms the performance of existing machine learning and deep learning techniques. Furthermore, the presented method predicts proactive faults with a normalized root mean square error of 0.017, providing an accurate prediction of fog node failure. The proposed framework experiments show a significant improvement in the prediction of inaccurate resources of fog nodes by having a minimum delay, low processing time, improved accuracy, and the failure rate of prediction was faster in comparison to traditional LSTM, Support Vector Machines (SVM), and Logistic Regression.

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Section: Literature Surveymentioning

confidence: 99%

Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications

Haneef

Neelanarayanan

2023

Sensors

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“…Gao et al [23] propose a dynamic reconfiguration method of mobile e-commerce service workflow based on cloud edge. Using task replication, Sharif et al [24] meet the reliability requirements by improving the service quality. Yao et al [25] propose a nonreplicating model to take over the failed fog node to normalize the fog node by using an operable fog node.…”

Section: Related Workmentioning

confidence: 99%

An Approach to Modeling and Analyzing Reliability for Microservice‐Oriented Cloud Applications

Liu

Fan

et al. 2021

Wireless Communications and Mobile Computing

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Microservice architecture is a cloud-native architectural style, which has attracted extensive attention from the scientific research and industry communities to benefit independent development and deployment. However, due to the complexity of cloud-based platforms, the design of fault-tolerant strategies for microservice-oriented cloud applications becomes challenging. In order to improve the quality of service, it is essential to focus on the microservice with more criticality and maximize the reliability of the entire cloud application. This paper studies the modeling and analysis of service reliability in the cloud environment. Firstly, a formal description language is defined to model microservice, user request, and container accurately. Secondly, the reliability analysis is conducted to measure a critical microservice’s fluctuation and vibration attributes within a period, and the related properties of the constructed model are analyzed. Thirdly, a fault-tolerant strategy with redundancy operation has been proposed to optimize cloud application reliability. Finally, the effectiveness of the method is verified by experiments. The simulation results show that the algorithm obtains the maximum benefits and has high performance through several experiments.

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Fault Tolerant Edge Computing: Challenges and Opportunities

Pourreza,

Narasimhan

2023

2023 IEEE 7th International Conference on Fog and Edge Computing (ICFEC)

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Energy-efficient fault-tolerant scheduling in a fog-based smart monitoring application

Cited by 3 publications

References 0 publications

Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications

Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications

An Approach to Modeling and Analyzing Reliability for Microservice‐Oriented Cloud Applications

Fault Tolerant Edge Computing: Challenges and Opportunities

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