Predicting Traffic in the Cloud: A Statistical Approach

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2013

Autores

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

Ata de Conferência Internacional

Título: 2013 IEEE THIRD INTERNATIONAL CONFERENCE ON CLOUD AND GREEN COMPUTING (CGC 2013)

Páginas: 121-126

3rd IEEE International Conference on Cloud and Green Computing (CGC)

Karlsruhe, GERMANY, SEP 30-OCT 02, 2013

Indexação

ISI Web of Knowledge - 13 Citações

Scopus - 17 Citações

Outras Informações

ID Authenticus: P-009-3Z3

DOI: 10.1109/cgc.2013.26

Abstract (EN): Monitoring and managing traffic are vital elements to the operation of a network. Traffic prediction is an essential tool that captures the underlying behavior of a network and can be used, for example, to detect anomalies by defining acceptable data traffic thresholds. In this context, most current solutions are heavily based on historical time data, which makes it difficult to employ them in a dynamic environment such as cloud computing. We propose a traffic prediction approach based on a statistical model where observations are weighted with a Poisson distribution inside a sliding window. The evaluation of the proposed method is performed by assessing the Normalized Mean Square Error of predicted values over observed values from a real cloud computing dataset, collected by monitoring the utilization of Dropbox. Compared with other predictors, our solution exhibits the strongest correlation level and shows a close match with real observations.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6