Optimizing IaaS reserved contract procurement using load predictionOptimizing IaaS reserved contract procurement using load prediction
Faculty of Sciences. Mathematics and Computer Science
Research group
Modeling Of Systems and Internet Communication (MOSAIC)
Publication type
S.l. , [*]
Source (book)
Proceedings IEEE CLOUD2014, June 27 - July 2, 2014, Anchorage, Alaska, USA
ISBN - Hoofdstuk
Target language
English (eng)
Full text (Publishers DOI)
University of Antwerp
With the increased adoption of cloud computing, new challenges have emerged related to the cost-effective management of cloud resources. The proliferation of resource properties and pricing plans has made the selection, procurement and management of cloud resources a time-consuming and complex task, which stands to benefit from automation. This contribution focuses on the procurement decision of reserved contracts in the context of Infrastructure-as-a-Service (IaaS) providers such as Amazon EC2. Such reserved contracts complement pay-by-the-hour pricing models, and offer a significant reduction in price (up to 70%) for a particular period in return for an upfront payment. Thus, customers can reduce costs by predicting and analyzing their future needs in terms of the number and type of server instances. We present an algorithm that uses load prediction with automated time series forecasting based on a Double-seasonal Holt-Winters model, in order to make cost-efficient purchasing decisions among a wide range of contract types while taking into account an organization's current contract portfolio. We analyze its cost effectiveness through simulation of real-world web traffic traces. Our analysis investigates the impact of different prediction techniques on cost compared to a clairvoyant predictor and compares the algorithm's performance with a stationary contract renewal approach. Our results show that the algorithm is able to significantly reduce IaaS resource costs through automated reserved contract procurement. Moreover, the algorithm's computational cost makes it applicable to large-scale real-world settings.