NDLI: Revenue Maximization Using Adaptive Resource Provisioning in Cloud Computing Environments

Content Provider	IEEE Xplore Digital Library
Author	Guofu Feng Garg, S. Buyya, R. Wenzhong Li
Copyright Year	2012
Abstract	Compared with the traditional computing models such as grid computing and cluster computing, a key advantage of Cloud computing is that it provides a practical business model for customers to use remote resources. However, it is challenging for Cloud providers to allocate the pooled computing resources dynamically among the differentiated customers so as to maximize their revenue. It is not an easy task to transform the customer-oriented service metrics into operating level metrics, and control the Cloud resources adaptively based on Service Level Agreement (SLA). This paper addresses the problem of maximizing the provider's revenue through SLA-based dynamic resource allocation as SLA plays a vital role in Cloud computing to bridge service providers and customers. We formalize the resource allocation problem using Queuing Theory and propose optimal solutions for the problem considering various Quality of Service (QoS) parameters such as pricing mechanisms, arrival rates, service rates and available resources. The experimental results, both with the synthetic dataset and with traced dadataset, show that our algorithms outperform related work.
Starting Page	192
Ending Page	200
File Size	345090
Page Count	9
File Format	PDF
ISBN	9781467329019
ISSN	15505510
DOI	10.1109/Grid.2012.16
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time factors Servers Pricing Mathematical model Resource management Virtual machining Cloud computing Resource Allocation Cloud Computing Service Level Agreement
Content Type	Text
Resource Type	Article

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