scholarly journals Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Scheduling

2017 ◽  
Vol 7 (6) ◽  
pp. 3570 ◽  
Author(s):  
Gibet Tani Hicham ◽  
El Amrani Chaker ◽  
Elaachak Lotfi
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Artificial Neural Networks ◽  
Comparative Study ◽  
Operating Systems ◽  
Processing Unit ◽  
Cpu Scheduling ◽  
Computing Model ◽  
Artificial Neural ◽  
Computing Platforms

Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling.

scholarly journals Reconstruction of Storm‐Time Total Electron Content Using Ionospheric Tomography and Artificial Neural Networks: A Comparative Study Over the African Region

Radio Science ◽  
2018 ◽  
Vol 53 (11) ◽  
pp. 1328-1345 ◽  
Author(s):  
Jean Claude Uwamahoro ◽  
Nigussie M. Giday ◽  
John Bosco Habarulema ◽  
Zama T. Katamzi‐Joseph ◽  
Gopi Krishna Seemala
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comparative Study ◽  
Total Electron Content ◽  
Electron Content ◽  
African Region ◽  
Total Electron ◽  
Ionospheric Tomography ◽  
Artificial Neural

Domain Knowledge Embedding Regularization Neural Networks for Workload Prediction and Analysis in Cloud Computing

2018 ◽  
Vol 11 (4) ◽  
pp. 137-154 ◽  
Author(s):  
Lei Li ◽  
Min Feng ◽  
Lianwen Jin ◽  
Shenjin Chen ◽  
Lihong Ma ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Artificial Neural Networks ◽  
Cost Effectiveness ◽  
Domain Knowledge ◽  
Large Scale ◽  
Workload Prediction ◽  
Platform As A Service ◽  
Resource Cost ◽  
Artificial Neural

Online services are now commonly deployed via cloud computing based on Infrastructure as a Service (IaaS) to Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). However, workload is not constant over time, so guaranteeing the quality of service (QoS) and resource cost-effectiveness, which is determined by on-demand workload resource requirements, is a challenging issue. In this article, the authors propose a neural network-based-method termed domain knowledge embedding regularization neural networks (DKRNN) for large-scale workload prediction. Based on analyzing the statistical properties of a real large-scale workload, domain knowledge, which provides extended information about workload changes, is embedded into artificial neural networks (ANN) for linear regression to improve prediction accuracy. Furthermore, the regularization with noisy is combined to improve the generalization ability of artificial neural networks. The experiments demonstrate that the model can achieve more accuracy of workload prediction, provide more adaptive resource for higher resource cost effectiveness and have less impact on the QoS.

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