NDLI: Intelligent management of virtualized resources for database systems in cloud environment

Content Provider	IEEE Xplore Digital Library
Author	Pengcheng Xiong Yun Chi Shenghuo Zhu Hyun Jin Moon Calton Pu Hacigumus, H.
Copyright Year	2011
Description	Author affiliation: School of Computer Science, Georgia Institute of Technology, 266 Ferst Drive, Atlanta, 30332, USA (Pengcheng Xiong; Calton Pu) \|\| NEC Laboratories America, 10080 North Wolfe Road, SW3-350, Cupertino, CA 95014, USA (Yun Chi; Shenghuo Zhu; Hyun Jin Moon; Hacigumus, H.)
Abstract	In a cloud computing environment, resources are shared among different clients. Intelligently managing and allocating resources among various clients is important for system providers, whose business model relies on managing the infrastructure resources in a cost-effective manner while satisfying the client service level agreements (SLAs). In this paper, we address the issue of how to intelligently manage the resources in a shared cloud database system and present SmartSLA, a cost-aware resource management system. SmartSLA consists of two main components: the system modeling module and the resource allocation decision module. The system modeling module uses machine learning techniques to learn a model that describes the potential profit margins for each client under different resource allocations. Based on the learned model, the resource allocation decision module dynamically adjusts the resource allocations in order to achieve the optimum profits. We evaluate SmartSLA by using the TPC-W benchmark with workload characteristics derived from real-life systems. The performance results indicate that SmartSLA can successfully compute predictive models under different hardware resource allocations, such as CPU and memory, as well as database specific resources, such as the number of replicas in the database systems. The experimental results also show that SmartSLA can provide intelligent service differentiation according to factors such as variable workloads, SLA levels, resource costs, and deliver improved profit margins.
Starting Page	87
Ending Page	98
File Size	1104307
Page Count	12
File Format	PDF
ISBN	9781424489596
ISSN	10636382
e-ISBN	9781424489602
e-ISBN	9781424489589
DOI	10.1109/ICDE.2011.5767928
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-11
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Databases Resource management Machine learning Linear regression System performance Regression tree analysis Time factors multitenant databases cloud computing virtualization database systems
Content Type	Text
Resource Type	Article

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