Publication
Title
Optimizing IaaS reserved contract procurement using load prediction
Author
Abstract
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.
Language
English
Source (book)
Proceedings IEEE CLOUD2014, June 27 - July 2, 2014, Anchorage, Alaska, USA
Publication
S.l. : 2014
ISBN
978-1-4799-5062-1
DOI
10.1109/CLOUD.2014.22
Volume/pages
p. 88-95
ISI
000392940500012
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Project info
Cost-efficient scheduling of power consuming tasks in households with renewable energy sources and local storage capacity.
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 27.03.2015
Last edited 09.10.2023
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