TY - GEN
T1 - Variation-aware resource allocation evaluation for cloud workflows using statistical model checking
AU - Huang, Saijie
AU - Chen, Mingsong
AU - Liu, Xiao
AU - Du, Dehui
AU - Chen, Xiaohong
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - Aiming at minimizing service operating costs and SLA (Service Level Agreement) violations, various resource allocation strategies have been investigated to support Cloud service providers' decision making. However, due to the service execution time variation, traditional optimal resource allocation strategies cannot achieve the best performance in practice. To address this problem, we propose an automated variation-aware evaluation framework for resource allocation strategies based on statistical model checker UPPAAL-SMC. Our framework can systematically evaluate the performance of resource allocation strategies under variations, and conduct complex queries on the quality of service. The experimental results show that our framework can not only filter inferior solutions efficiently, but also can enable the tuning of requirement constraints. Since our approach can be fully automated, the human efforts in resource allocation strategy evaluation can be significantly reduced.
AB - Aiming at minimizing service operating costs and SLA (Service Level Agreement) violations, various resource allocation strategies have been investigated to support Cloud service providers' decision making. However, due to the service execution time variation, traditional optimal resource allocation strategies cannot achieve the best performance in practice. To address this problem, we propose an automated variation-aware evaluation framework for resource allocation strategies based on statistical model checker UPPAAL-SMC. Our framework can systematically evaluate the performance of resource allocation strategies under variations, and conduct complex queries on the quality of service. The experimental results show that our framework can not only filter inferior solutions efficiently, but also can enable the tuning of requirement constraints. Since our approach can be fully automated, the human efforts in resource allocation strategy evaluation can be significantly reduced.
KW - Cloud Workflows
KW - Resource Allocation Evaluation
KW - Statistical Model Checking
UR - https://www.scopus.com/pages/publications/84924417664
U2 - 10.1109/BDCloud.2014.48
DO - 10.1109/BDCloud.2014.48
M3 - 会议稿件
AN - SCOPUS:84924417664
T3 - Proceedings - 4th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2014 with the 7th IEEE International Conference on Social Computing and Networking, SocialCom 2014 and the 4th International Conference on Sustainable Computing and Communications, SustainCom 2014
SP - 201
EP - 208
BT - Proceedings - 4th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2014 with the 7th IEEE International Conference on Social Computing and Networking, SocialCom 2014 and the 4th International Conference on Sustainable Computing and Communications, SustainCom 2014
A2 - Chen, Jinjun
A2 - Yang, Laurence T.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2014
Y2 - 3 December 2014 through 5 December 2014
ER -