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Energy-Saving QoS Resource Management of Virtualized Networked Data Centers for Big Data Stream Computing

Energy-Saving QoS Resource Management of Virtualized Networked Data Centers for Big Data Stream Computing

Nicola Cordeschi, Mohammad Shojafar, Danilo Amendola, Enzo Baccarelli
Copyright: © 2015 |Pages: 34
ISBN13: 9781466682139|ISBN10: 1466682132|EISBN13: 9781466682146
DOI: 10.4018/978-1-4666-8213-9.ch004
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MLA

Cordeschi, Nicola, et al. "Energy-Saving QoS Resource Management of Virtualized Networked Data Centers for Big Data Stream Computing." Emerging Research in Cloud Distributed Computing Systems, edited by Susmit Bagchi, IGI Global, 2015, pp. 122-155. https://doi.org/10.4018/978-1-4666-8213-9.ch004

APA

Cordeschi, N., Shojafar, M., Amendola, D., & Baccarelli, E. (2015). Energy-Saving QoS Resource Management of Virtualized Networked Data Centers for Big Data Stream Computing. In S. Bagchi (Ed.), Emerging Research in Cloud Distributed Computing Systems (pp. 122-155). IGI Global. https://doi.org/10.4018/978-1-4666-8213-9.ch004

Chicago

Cordeschi, Nicola, et al. "Energy-Saving QoS Resource Management of Virtualized Networked Data Centers for Big Data Stream Computing." In Emerging Research in Cloud Distributed Computing Systems, edited by Susmit Bagchi, 122-155. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-8213-9.ch004

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Abstract

In this chapter, the authors develop the scheduler which optimizes the energy-vs.-performance trade-off in Software-as-a-Service (SaaS) Virtualized Networked Data Centers (VNetDCs) that support real-time Big Data Stream Computing (BDSC) services. The objective is to minimize the communication-plus-computing energy which is wasted by processing streams of Big Data under hard real-time constrains on the per-job computing-plus-communication delays. In order to deal with the inherently nonconvex nature of the resulting resource management optimization problem, the authors develop a solving approach that leads to the lossless decomposition of the afforded problem into the cascade of two simpler sub-problems. The resulting optimal scheduler is amenable of scalable and distributed adaptive implementation. The performance of a Xen-based prototype of the scheduler is tested under several Big Data workload traces and compared with the corresponding ones of some state-of-the-art static and sequential schedulers.

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