Performance Analysis of Network Traffic Predictors in the Cloud

• Tipo: Artigo em Revista Científica Internacional
• Data: 2017

Autores

Dalmazo, BL (Autor) Outra; João P. Vilela (Autor) Outra; Curado, M (Autor) Outra

Revista

Título: Journal of Network and Systems Management

Vol. 25

Páginas: 290-320

ISSN: 1064-7570

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 17 Citações

Scopus - 23 Citações

Outras Informações

ID Authenticus: P-00K-VD7

DOI: 10.1007/s10922-016-9392-x

Abstract (EN): Predicting the inherent traffic behaviour of a network is an essential task, which can be used for various purposes, such as monitoring and managing the network's infrastructure. However, the recent surge of dynamic environments, such as Internet of Things and Cloud Computing have hampered this task. This means that the traffic on these networks is even more complex, displaying a nonlinear behaviour with specific aperiodic characteristics during daily operation. Traditional network traffic predictors are usually based on large historical data bases which are used to train algorithms. This may not be suitable for these highly volatile environments, where the strength of the force exerted in the interaction between past and current values may change quickly with time. In light of this, a taxonomy for network traffic prediction models, including the review of state of the art, is presented here. In addition, an analysis mechanism, focused on providing a standardized approach for evaluating the best candidate predictor models for these environments, is proposed. These contributions favour the analysis of the efficacy and efficiency of network traffic prediction among several prediction models in terms of accuracy, historical dependency, running time and computational overhead. An evaluation of several prediction mechanisms is performed by assessing the Normalized Mean Square Error and Mean Absolute Percent Error of the values predicted by using traces taken from two real case studies in cloud computing.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 31