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Rafael Pereira Esteves

Researcher at Universidade Federal do Rio Grande do Sul

Publications -  16
Citations -  965

Rafael Pereira Esteves is an academic researcher from Universidade Federal do Rio Grande do Sul. The author has contributed to research in topics: Virtualization & Cloud computing. The author has an hindex of 11, co-authored 16 publications receiving 905 citations. Previous affiliations of Rafael Pereira Esteves include University of Rio Grande & University of Waterloo.

Papers
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Journal ArticleDOI

Data Center Network Virtualization: A Survey

TL;DR: A survey of the current state-of-the-art in data center networks virtualization, and a detailed comparison of the surveyed proposals are presented.
Proceedings Article

On tackling virtual data center embedding problem

TL;DR: A new embedding solution for data centers that, in addition to virtual machine placement, explicitly considers the relation between switches and links, allows multiple resources of the same request to be mapped to a single physical resource, and reduces resource fragmentation in terms of CPU is proposed.
Journal ArticleDOI

On the management of virtual networks

TL;DR: This article surveys some of the prominent solutions for network virtualization management and identifies research opportunities in the area and investigates the investigation of novel management solutions in recent years.
Proceedings ArticleDOI

A cloud monitoring framework for self-configured monitoring slices based on multiple tools

TL;DR: This paper proposes a framework to address the problem of creating monitoring slices automatically independent of the monitoring solutions employed, and develops FlexACMS, flexible automate cloud monitoring slices, which relies on a modules that flexibly handles cloud platforms and monitoring solutions.
Journal ArticleDOI

Using Empirical Estimates of Effective Bandwidth in Network-Aware Placement of Virtual Machines in Datacenters

TL;DR: Experimental results show that, in contrast to the Oktopus network-aware VM placement system, MAPLE can allocate computing and network resources in a manner that balances efficiency of resource utilization with performance predictability.