TY - GEN
T1 - Multilayered Cloud Applications Autoscaling Performance Estimation
AU - Jindal, Anshul
AU - Podolskiy, Vladimir
AU - Gerndt, Michael
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - A multilayered autoscaling gets an increasing attention both in research and business communities. Introduction of new virtualization layers such as containers, pods, and clusters has turned a deployment and a management of cloud applications into a simple routine. Each virtualization layer usually provides its own solution for scaling. However, synchronization and collaboration of these solutions on multiple layers of virtualization remains an open topic. In the scope of the paper, we consider a wide research problem of the autoscaling across several layers for cloud applications. A novel approach to multilayered autoscalers performance measurement is introduced in this paper. This approach is implemented in Autoscaling Performance Measurement Tool (APMT), which architecture and functionality are also discussed. Results of model experiments on different requests patterns are also provided in the paper.
AB - A multilayered autoscaling gets an increasing attention both in research and business communities. Introduction of new virtualization layers such as containers, pods, and clusters has turned a deployment and a management of cloud applications into a simple routine. Each virtualization layer usually provides its own solution for scaling. However, synchronization and collaboration of these solutions on multiple layers of virtualization remains an open topic. In the scope of the paper, we consider a wide research problem of the autoscaling across several layers for cloud applications. A novel approach to multilayered autoscalers performance measurement is introduced in this paper. This approach is implemented in Autoscaling Performance Measurement Tool (APMT), which architecture and functionality are also discussed. Results of model experiments on different requests patterns are also provided in the paper.
KW - cloud
KW - cloud applications autoscaling
KW - multilayered autoscaling
KW - performance of autoscaling
KW - tool to estimate autoscaling performance on multiple layers
UR - http://www.scopus.com/inward/record.url?scp=85050757595&partnerID=8YFLogxK
U2 - 10.1109/SC2.2017.12
DO - 10.1109/SC2.2017.12
M3 - Conference contribution
AN - SCOPUS:85050757595
T3 - Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017
SP - 24
EP - 31
BT - Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE International Symposium on Cloud and Service Computing, SC2 2017
Y2 - 22 November 2017 through 25 November 2017
ER -