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AuthorIqbal W.
AuthorErradi A.
AuthorMahmood A.
Available date2019-09-24T08:16:00Z
Publication Date2018
Publication NameJournal of Network and Computer Applications
ISSN10848045
URIhttp://dx.doi.org/10.1016/j.jnca.2018.09.023
URIhttp://hdl.handle.net/10576/11925
AbstractProactive auto-scaling methods dynamically manage the resources for an application according to the current and future load predictions to preserve the desired performance at a reduced cost. However, auto-scaling web applications remain challenging mainly due to dynamic workload intensity and characteristics which are difficult to predict. Most existing methods mainly predict the request arrival rate which only partially captures the workload characteristics and the changing system dynamics that influence the resource needs. This may lead to inappropriate resource provisioning decisions. In this paper, we address these challenges by proposing a framework for prediction of dynamic workload patterns as follows. First, we use an unsupervised learning method to analyze the web application access logs to discover URI (Uniform Resource Identifier) space partitions based on the response time and the document size features. Then for each application URI, we compute its distribution across these partitions based on historical access logs to accurately capture the workload characteristics compared to just representing the workload using the request arrival rate. These URI distributions are then used to compute the Probabilistic Workload Pattern (PWP), which is a probability vector describing the overall distribution of incoming requests across URI partitions. Finally, the identified workload patterns for a specific number of last time intervals are used to predict the workload pattern of the next interval. The latter is used for future resource demand prediction and proactive auto-scaling to dynamically control the provisioning of resources. The framework is implemented and experimentally evaluated using historical access logs of three real web applications, each with increasing, decreasing, periodic, and randomly varying arrival rate behaviors. Results show that the proposed solution yields significantly more accurate predictions of workload patterns and resource demands of web applications compared to existing approaches. ? 2018 Elsevier Ltd
SponsorThis work was made possible by NPRP grant # 7-481-1-088 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Waheed Iqbal is a Postdoc researcher with the Department of Computer Science and Engineering, Qatar University. He also holds a position of Assistant Professor at Punjab University College of Information Technology, University of the Punjab, Lahore, Pakistan. His research interests lie in cloud computing, distribute systems, machine learning, and large scale system performance evaluation. Waheed received his Ph.D. degree from the Asian Institute of Technology, Thailand. He received dual Masters degrees in Computer Science and Information Technology from the Asian Institute of Technology and the Technical University of Catalonia (UPC), Barcelona, Spain, respectively. Abdelkarim Erradi is an Assistant Professor in the Computer Science and Engineering Department at Qatar University. His research and development activities and interests focus on autonomic computing, self-managing systems and cybersecurity. He leads several funded research projects in these areas. He has authored several scientific papers in international conferences and journals. He received his Ph.D. in computer science from the University of New South Wales, Sydney, Australia. Besides his academic experience, he possesses 12 years professional experience as a Designer and a Developer of large scale enterprise applications. Arif Mahmood is an Associate Professor in the Department of Computer Science, Information Technology University (ITU). He received his Masters and the PhD degrees in Computer Science from the Lahore University of Management Sciences in 2003 and 2011 respectively with Gold Medal and academic distinction. He also worked as Postdoc researcher with Qatar University and as Research Assistant Professor with the School of Mathematics and Statistics, and with the College of Computer Science and Software Engineering, the University of the Western Australia (UWA). His major research interests are in Computer Vision and Pattern Recognition. More specifically he has performed research in data clustering, classification, action and object recognition using image sets, scene background modeling, and person segmentation and action recognition in crowds.
Languageen
PublisherAcademic Press
SubjectProactive auto-scaling
SubjectWeb applications
SubjectWorkload characterization
SubjectWorkload patterns
SubjectWorkload patterns prediction
TitleDynamic workload patterns prediction for proactive auto-scaling of web applications
TypeArticle
Pagination94 - 107
Volume Number124


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