scholarly journals End-to-end diagnosis of cloud systems against intermittent faults

2020 ◽  
pp. 40-40
Author(s):  
Chao Wang ◽  
Zhongchuan Fu ◽  
Yanyan Huo
Keyword(s):  
Cloud Computing ◽  
Fault Diagnosis ◽  
Computing Environment ◽  
Bp Network ◽  
Cloud Computing Environment ◽  
Intermittent Faults ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
End To End ◽  
Detection Mechanisms

The diagnosis of intermittent faults is challenging because of their random manifestation due to intricate mechanisms. Conventional diagnosis methods are no longer effective for these faults, especially for hierachical environment, such as cloud computing. This paper proposes a fault diagnosis method that can effectively identify and locate intermittent faults originating from (but not limited to) processors in the cloud computing environment. The method is end-to-end in that it does not rely on artificial feature extraction for applied scenarios, making it more generalizable than conventional neural network-based methods. It can be implemented with no additional fault detection mechanisms, and is realized by software with almost zero hardware cost. The proposed method shows a higher fault diagnosis accuracy than BP network, reaching 97.98% with low latency.

scholarly journals Intrusion Detection Method of Electric Power Information Network in Cloud Computing Environment

2021 ◽  
Vol 2113 (1) ◽  
pp. 012050
Author(s):  
Jiaqi Zhang ◽  
Guoping Feng ◽  
Dexi Zhou ◽  
Mingjiu Li
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Electric Power ◽  
Detection Method ◽  
Power Grids ◽  
Normal Operation ◽  
Computing Environment ◽  
Bp Network ◽  
Widespread Application ◽  
Cloud Computing Environment

Abstract With the widespread application of power grid systems, the information security problems faced by power grids have become more obvious. Various internal and external intrusion attacks that occur frequently have become an important issue affecting the normal operation of power generation and operations. The purpose of this paper is to study the intrusion detection method of electric power information(PI) network in the cloud computing environment. With the help of the cloud platform’s ability to process big data, and based on the analysis of the PI network structure, a DBN optimized BP network algorithm is proposed, and the optimized BP neural network is used as a runtime classification program. Experimental results show that MR-DBN-BP has a detection rate of 96.7% for intrusion detection of PI networks, which can effectively detect intrusions and effectively protect the power dispatch system network.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

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