scholarly journals Adaptive fault-tolerant model for improving cloud computing performance using artificial neural network

2020 ◽  
Vol 170 ◽  
pp. 929-934
Author(s):  
Awatif Ragmani ◽  
Amina Elomri ◽  
Noreddine Abghour ◽  
Khalid Moussaid ◽  
Mohammed Rida ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cloud Computing ◽  
Fault Tolerant ◽  
Computing Performance ◽  
Artificial Neural

scholarly journals Performance evaluation of an adopted model based on big-bang big-crunch and artificial neural network for cloud applications

2021 ◽  
Vol 48 (4) ◽  
Author(s):  
Pradeep Singh Rawat ◽  
◽  
Robin Singh Bhadoria ◽  
Punit Gupta ◽  
G. P. Saroha ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cloud Computing ◽  
Big Bang ◽  
Ann Model ◽  
Computing Paradigm ◽  
Big Crunch ◽  
Artificial Neural ◽  
Cloud Applications ◽  
The Way

High-performance computing is changing the way we compute. In the past decade, the cloud computing paradigm has changed the way we compute, communicate, and technology. Cover real-world problems. There are still many complex challenges in the cloud computing paradigm. Improving effective planning strategies is a complex problem in the service-oriented computing paradigm.In this article, our research focuses on improving task scheduler strategies to improve the performance of cloud applications. The proposed model is inspired by an artificial neural network-based system and astrology base scheduler Big-Bang Big-Crunch. The results show that the proposed strategy based on BBBC and neural network is superior to the method based on astrology (BigBang BigCrunch costaware), genetic cost and many other existing methods.The proposed BB-BC-ANN model is validated using standard workload file (San Diego Supercomputer Center (SDSC) Blue Horizon logs). The results show that the proposed BB-BC-ANN model performs better than some of the existing approaches using performance indicators like total completion time (ms), average start time (ms), average finish time(ms), scheduling time(ms), and total execution time(ms).

Developing Cloud Computing Time Slot-availability Predictions Using an Artificial Neural Network

2020 ◽  
Vol 9 (1) ◽  
pp. 49-57
Author(s):  
Alanazi Rayan ◽  
Muhammad Ashfaq khan ◽  
Fawaz Alhazemi ◽  
Hamoud Alshammari ◽  
Yunmook Nah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cloud Computing ◽  
Time Slot ◽  
Computing Time ◽  
Artificial Neural

Minimizing Energy Consumption and SLA Violation in Fog Computing Using Artificial Neural Network

2020 ◽  
Vol 10 ◽  
Author(s):  
Shivi Sharma ◽  
Hemraj Saini
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Fog Computing ◽  
Multi Objective ◽  
Sla Violation ◽  
Artificial Neural ◽  
Reduction Of Energy Consumption ◽  
Data Centres

: With the fast development of cloud computing methods, exponential growth is faced by number of users. It is complex for traditional data centres for performing number of jobs in real time because of inadequate resources bandwidth. Therefore, the method of fog computing is recommended for supporting and for providing fast cloud services. It is not a substitute but is a powerful complement of cloud computing. Reduction of energy consumption through the notion of fog computing has certainly been a challenge for the current researcher ,industry and community. Various industries including finance and health care do require a rich resource based platform for the purpose of processing large amount of data with cloud computing across fog architecture. The consumption of energy across fog servers relies on allocating techniques for services (user requests).It facilitates processing at the edge with the probability to interact with cloud. This article has proposed energy aware scheduling by using Artificial neural network (ANN) and Modified multi objective job scheduling (MMJS) techniques. The emphasis of the work is on reduction of energy consumption rate with less Service level agreement (SLA) violation in fog computing for data centres. The result shows that there is 3.9% reduction in SLA Violation when multi-objective function with ANN is applied.

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