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-pluscomputing 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.

Energy-saving QoS resource management of virtualized networked data centers for Big Data stream computing / Cordeschi, Nicola; Shojafar, Mohammad; Amendola, Danilo; Baccarelli, Enzo. - STAMPA. - (2015), pp. 122-155. [10.4018/978-1-4666-8213-9.ch004].

Energy-saving QoS resource management of virtualized networked data centers for Big Data stream computing

CORDESCHI, Nicola;SHOJAFAR, MOHAMMAD;AMENDOLA, DANILO;BACCARELLI, Enzo
2015

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-pluscomputing 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.
2015
Emerging Research in Cloud distributed computing systems
9781466682139
9781466682146
Big data; stream computing; cloud
02 Pubblicazione su volume::02a Capitolo o Articolo
Energy-saving QoS resource management of virtualized networked data centers for Big Data stream computing / Cordeschi, Nicola; Shojafar, Mohammad; Amendola, Danilo; Baccarelli, Enzo. - STAMPA. - (2015), pp. 122-155. [10.4018/978-1-4666-8213-9.ch004].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/778018
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