TY - GEN
T1 - Exploring instance heterogeneity in public cloud providers for HPC applications
AU - Roloff, Eduardo
AU - Diener, Matthias
AU - Gaspary, Luciano P.
AU - Navaux, Philippe O.A.
N1 - Funding Information:
This work has been partially supported by the project “GREEN-CLOUD: Computacao em Cloud com Computacao Sustentavel” (#16/2551-0000 488-9), from FAPERGS and CNPq Brazil. This research received partial funding from CYTED for the RICAP Project.
Publisher Copyright:
Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Public cloud providers offer a wide range of instance types with different speeds, configurations, and prices, which allows users to choose the most appropriate configurations for their applications. When executing parallel applications that require multiple instances to execute, such as large scientific applications, most users pick an instance type that fits their overall needs best, and then create a cluster of interconnected instances of the same type. However, the tasks of a parallel application often have different demands in terms of performance and memory usage. This difference in demands can be exploited by selecting multiple instance types that are adapted to the demands of the application. This way, the combination of public cloud heterogeneity and application heterogeneity can be exploited in order to reduce the execution cost without significant performance loss. In this paper we conduct an evaluation of three major public cloud providers: Microsoft, Amazon, and Google, comparing their suitability for heterogeneous execution. Results show that Azure is the most suitable of the three providers, with cost efficiency gains of up to 50% compared to homogeneous execution, while maintaining the same performance.
AB - Public cloud providers offer a wide range of instance types with different speeds, configurations, and prices, which allows users to choose the most appropriate configurations for their applications. When executing parallel applications that require multiple instances to execute, such as large scientific applications, most users pick an instance type that fits their overall needs best, and then create a cluster of interconnected instances of the same type. However, the tasks of a parallel application often have different demands in terms of performance and memory usage. This difference in demands can be exploited by selecting multiple instance types that are adapted to the demands of the application. This way, the combination of public cloud heterogeneity and application heterogeneity can be exploited in order to reduce the execution cost without significant performance loss. In this paper we conduct an evaluation of three major public cloud providers: Microsoft, Amazon, and Google, comparing their suitability for heterogeneous execution. Results show that Azure is the most suitable of the three providers, with cost efficiency gains of up to 50% compared to homogeneous execution, while maintaining the same performance.
KW - Amazon
KW - Cloud computing
KW - Cost efficiency
KW - Google
KW - Heterogeneity
KW - Microsoft
UR - http://www.scopus.com/inward/record.url?scp=85067486895&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067486895&partnerID=8YFLogxK
U2 - 10.5220/0007799302100222
DO - 10.5220/0007799302100222
M3 - Conference contribution
AN - SCOPUS:85067486895
T3 - CLOSER 2019 - Proceedings of the 9th International Conference on Cloud Computing and Services Science
SP - 210
EP - 222
BT - CLOSER 2019 - Proceedings of the 9th International Conference on Cloud Computing and Services Science
A2 - Munoz, Victor Mendez
A2 - Ferguson, Donald
A2 - Helfert, Markus
A2 - Pahl, Claus
PB - SciTePress
T2 - 9th International Conference on Cloud Computing and Services Science, CLOSER 2019
Y2 - 2 May 2019 through 4 May 2019
ER -