TY - GEN
T1 - Environment-sensitive performance tuning for distributed service orchestration
AU - Lin, Yu
AU - Ivančić, Franjo
AU - Joshi, Pallavi
AU - Balakrishnan, Gogul
AU - Ganai, Malay
AU - Gupta, Aarti
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Modern distributed systems are designed to tolerate unreliable environments, i.e., they aim to provide services even when some failures happen in the underlying hardware or network. However, the impact of unreliable environments can be significant on the performance of the distributed systems, which should be considered when deploying the services. In this paper, we present an approach to optimize performance of the distributed systems under unreliable deployed environments, through searching for optimal configuration parameters. To simulate an unreliable environment, we inject several failures in the environment of a service application, such as a node crash in the cluster, network failures between nodes, resource contention in nodes, etc. Then, we use a search algorithm to find the optimal parameters automatically in the user-selected parameter space, under the unreliable environment we created.We have implemented our approach in a testing-based framework and applied it to several well-known distributed service systems.
AB - Modern distributed systems are designed to tolerate unreliable environments, i.e., they aim to provide services even when some failures happen in the underlying hardware or network. However, the impact of unreliable environments can be significant on the performance of the distributed systems, which should be considered when deploying the services. In this paper, we present an approach to optimize performance of the distributed systems under unreliable deployed environments, through searching for optimal configuration parameters. To simulate an unreliable environment, we inject several failures in the environment of a service application, such as a node crash in the cluster, network failures between nodes, resource contention in nodes, etc. Then, we use a search algorithm to find the optimal parameters automatically in the user-selected parameter space, under the unreliable environment we created.We have implemented our approach in a testing-based framework and applied it to several well-known distributed service systems.
KW - Distributed application
KW - Disturbance action
KW - Performance optimization
UR - http://www.scopus.com/inward/record.url?scp=84942574472&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84942574472&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-17353-5_18
DO - 10.1007/978-3-319-17353-5_18
M3 - Conference contribution
AN - SCOPUS:84942574472
SN - 9783319173528
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 209
EP - 223
BT - High Performance Computing for Computational Science - VECPAR 2014 - 11th International Conference, Revised Selected Papers
A2 - Marques, Osni
A2 - Dayde, Michel
A2 - Nakajima, Kengo
PB - Springer Verlag
T2 - 11th International Conference on High Performance Computing for Computational Science, VECPAR 2014
Y2 - 30 June 2014 through 3 July 2014
ER -