TY - GEN
T1 - Towards combining reactive and proactive cloud elasticity on running HPC applications
AU - Rodrigues, Vinicius Facco
AU - Da Rosa Righi, Rodrigo
AU - Da Costa, Cristiano André
AU - Singh, Dhananjay
AU - Munoz, Victor Mendez
AU - Chang, Victor
N1 - Publisher Copyright:
Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
PY - 2018
Y1 - 2018
N2 - The elasticity feature of cloud computing has been proved as pertinent for parallel applications, since users do not need to take care about the best choice for the number of processes/resources beforehand. To accomplish this, the most common approaches use threshold-based reactive elasticity or time-consuming proactive elasticity. However, both present at least one problem related to: the need of a previous user experience, lack on handling load peaks, completion of parameters or design for a specific infrastructure and workload setting. In this regard, we developed a hybrid elasticity service for parallel applications named SelfElastic. As parameterless model, SelfElastic presents a closed control loop elasticity architecture that adapts at runtime the values of lower and upper thresholds. Besides presenting SelfElastic, our purpose is to provide a comparison with our previous work on reactive elasticity called AutoElastic. The results present the SelfElastic’s lightweight feature, besides highlighting its performance competitiveness in terms of application time and cost metrics.
AB - The elasticity feature of cloud computing has been proved as pertinent for parallel applications, since users do not need to take care about the best choice for the number of processes/resources beforehand. To accomplish this, the most common approaches use threshold-based reactive elasticity or time-consuming proactive elasticity. However, both present at least one problem related to: the need of a previous user experience, lack on handling load peaks, completion of parameters or design for a specific infrastructure and workload setting. In this regard, we developed a hybrid elasticity service for parallel applications named SelfElastic. As parameterless model, SelfElastic presents a closed control loop elasticity architecture that adapts at runtime the values of lower and upper thresholds. Besides presenting SelfElastic, our purpose is to provide a comparison with our previous work on reactive elasticity called AutoElastic. The results present the SelfElastic’s lightweight feature, besides highlighting its performance competitiveness in terms of application time and cost metrics.
KW - Cloud Utility
KW - High-performance Computing
KW - Live Thresholding
KW - Resource Management
KW - Self-organizing
UR - http://www.scopus.com/inward/record.url?scp=85051955227&partnerID=8YFLogxK
U2 - 10.5220/0006761302610268
DO - 10.5220/0006761302610268
M3 - Conference Proceeding
AN - SCOPUS:85051955227
T3 - IoTBDS 2018 - Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security
SP - 261
EP - 268
BT - IoTBDS 2018 - Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security
A2 - Munoz, Victor Mendez
A2 - Wills, Gary
A2 - Walters, Robert
A2 - Firouzi, Farshad
A2 - Chang, Victor
PB - SciTePress
T2 - 3rd International Conference on Internet of Things, Big Data and Security, IoTBDS 2018
Y2 - 19 March 2018 through 21 March 2018
ER -