TY - JOUR
T1 - SaaS enabled admission control for MCMC simulation in cloud computing infrastructures
AU - Vázquez-Poletti, J. L.
AU - Moreno-Vozmediano, R.
AU - Han, R.
AU - Wang, W.
AU - Llorente, I. M.
N1 - Publisher Copyright:
© 2016 Elsevier B.V.
PY - 2017/2/1
Y1 - 2017/2/1
N2 - Markov Chain Monte Carlo (MCMC) methods are widely used in the field of simulation and modelling of materials, producing applications that require a great amount of computational resources. Cloud computing represents a seamless source for these resources in the form of HPC. However, resource over-consumption can be an important drawback, specially if the cloud provision process is not appropriately optimized. In the present contribution we propose a two-level solution that, on one hand, takes advantage of approximate computing for reducing the resource demand and on the other, uses admission control policies for guaranteeing an optimal provision to running applications.
AB - Markov Chain Monte Carlo (MCMC) methods are widely used in the field of simulation and modelling of materials, producing applications that require a great amount of computational resources. Cloud computing represents a seamless source for these resources in the form of HPC. However, resource over-consumption can be an important drawback, specially if the cloud provision process is not appropriately optimized. In the present contribution we propose a two-level solution that, on one hand, takes advantage of approximate computing for reducing the resource demand and on the other, uses admission control policies for guaranteeing an optimal provision to running applications.
KW - Admission control
KW - Cloud computing
KW - PaaS
KW - SaaS
UR - http://www.scopus.com/inward/record.url?scp=84979231188&partnerID=8YFLogxK
U2 - 10.1016/j.cpc.2016.07.004
DO - 10.1016/j.cpc.2016.07.004
M3 - Article
AN - SCOPUS:84979231188
SN - 0010-4655
VL - 211
SP - 88
EP - 97
JO - Computer Physics Communications
JF - Computer Physics Communications
ER -