RAFALE: Rethinking the provisioning of virtuAl network services using a Fast and scAlable machine LEarning approach

Suwi, Hanan; Lahlou, Laaziz; Kara, Nadjia; Edstrom, Claes

doi:10.1007/s11227-022-04492-6

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2022

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“…They have proposed solutions to these problems from different perspectives. Many domestic universities and research institutes have conducted systematic research on scalable machine learning, and achieved good results [5][6]. Some scholars introduced how to use the model to determine the similarity between sample points.…”

Section: Introductionmentioning

confidence: 99%

Parallel and Distributed Optimization Algorithms for Scalable Machine Learning

2022

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In recent years, the emergence of a large number of parallel data makes us pay more and more attention to how to improve the efficiency of decision-making. In this paper, scalable machine learning is the research object. First, the existing problems and development directions of existing related technologies are analyzed. Then, an extensible machine learning algorithm is introduced to solve these problems. Finally, MATLAB is used to realize the optimization simulation of n subsystems, and the regression equation between the final result and the optimal value as well as the mean square error is obtained. The corresponding conclusions are drawn. The parallel and distributed optimization algorithms of extensible machine learning run for a short time, The parallel capacity is about 3500k, and the global performance can be effectively improved by updating existing methods in the feasible region.

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Section: Introductionmentioning

confidence: 99%

Parallel and Distributed Optimization Algorithms for Scalable Machine Learning

2022

View full text Add to dashboard Cite

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RAFALE: Rethinking the provisioning of virtuAl network services using a Fast and scAlable machine LEarning approach

Cited by 1 publication

References 49 publications

Parallel and Distributed Optimization Algorithms for Scalable Machine Learning

Parallel and Distributed Optimization Algorithms for Scalable Machine Learning

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