Predicting Traffic in the Cloud: A Statistical Approach

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

Dalmazo, BL (Author) Other; João P. Vilela (Author) Other; Curado, M (Author) Other

Conference proceedings International

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

Pages: 121-126

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

Karlsruhe, GERMANY, SEP 30-OCT 02, 2013

Indexing

ISI Web of Knowledge - 13 Citations

Scopus - 17 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6