TY - GEN
T1 - Leveraging cloud heterogeneity for cost-efficient execution of parallel applications
AU - Roloff, Eduardo
AU - Diener, Matthias
AU - Diaz Carreño, Emmanuell
AU - Gaspary, Luciano Paschoal
AU - Navaux, Philippe O.A.
N1 - Funding Information:
Acknowledgments. This research received funding from the EU H2020 Programme and from MCTI/RNP-Brazil under the HPC4E project, grant agreement no. 689772. Additional funding was provided by FAPERGS in the context of the GreenCloud Project.
Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - Public cloud providers offer a wide range of instance types, with different processing and interconnection speeds, as well as varying prices. Furthermore, the tasks of many parallel applications show different computational demands due to load imbalance. These differences can be exploited for improving the cost efficiency of parallel applications in many cloud environments by matching application requirements to instance types. In this paper, we introduce the concept of heterogeneous cloud systems consisting of different instance types to leverage the different computational demands of large parallel applications for improved cost efficiency. We present a mechanism that automatically suggests a suitable combination of instances based on a characterization of the application and the instance types. With such a heterogeneous cloud, we are able to improve cost efficiency significantly for a variety of MPI-based applications, while maintaining a similar performance.
AB - Public cloud providers offer a wide range of instance types, with different processing and interconnection speeds, as well as varying prices. Furthermore, the tasks of many parallel applications show different computational demands due to load imbalance. These differences can be exploited for improving the cost efficiency of parallel applications in many cloud environments by matching application requirements to instance types. In this paper, we introduce the concept of heterogeneous cloud systems consisting of different instance types to leverage the different computational demands of large parallel applications for improved cost efficiency. We present a mechanism that automatically suggests a suitable combination of instances based on a characterization of the application and the instance types. With such a heterogeneous cloud, we are able to improve cost efficiency significantly for a variety of MPI-based applications, while maintaining a similar performance.
KW - Cloud computing
KW - Cost efficiency
KW - Heterogeneity
KW - Performance
UR - http://www.scopus.com/inward/record.url?scp=85028704091&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028704091&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-64203-1_29
DO - 10.1007/978-3-319-64203-1_29
M3 - Conference contribution
AN - SCOPUS:85028704091
SN - 9783319642024
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 399
EP - 411
BT - Euro-Par 2017
A2 - Rivera, Francisco F.
A2 - Pena, Tomas F.
A2 - Cabaleiro, Jose C.
PB - Springer
T2 - 23rd International Conference on Parallel and Distributed Computing, Euro-Par 2017
Y2 - 28 August 2017 through 1 September 2017
ER -