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res and to reduce operational costs. Additionally, it
improves the MapReduce infrastructure throughput,
performance, and QoS, helping to meet job deadlines.
This paper describes the EME project blueprint,
as well as the ongoing efforts behind it. Our work is
being developed with Hadoop Yarn as the supporting
resource manager, but we believe that the ideas pre-
sented in this research can easily be adapted to other
resource management frameworks, for instance, Apa-
che Mesos and Docker Swarm. As it is usual in the
literature, we will start prototyping EME in a virtuali-
zed cluster and, when proving its usefulness, test it in
a bare-metal environment. Additionally, it is expected
to deploy an enterprise desktop grid, and to develop
an opportunistically, elastic resource allocation sche-
duler to be integrated within its architecture.
ACKNOWLEDGEMENTS
This work has been partially funded by the Spanish
Ministry of Science and Innovation with the Project
No. TIN2016-76373-P and for the Xunta de Gali-
cia with the Project No. GRC2014/008, the Consel-
ler
´
ıa de Cultura, Educaci
´
on e OU (accreditation 2016-
2019, ED431G/08), the European Regional Develop-
ment Fund (ERDF), the European Network HIPEAC,
and the Spanish Network CAPAP-H.
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