Performance Modelling and Verification of Cloud-based Auto-Scaling Policies

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

Colleges, School and Institutes

Abstract

Auto-scaling, a key property of cloud computing, allows application owners to acquire and release resources on demand. However, the shared environment, along with the exponentially large configuration space of available parameters, makes configuration of auto-scaling policies a challenging task. In particular, it is difficult to quantify, a priori, the impact of a policy on Quality of Service (QoS) provision. To address this problem, we propose a novel approach based on performance modelling and formal verification to produce performance guarantees on particular rule-based auto-scaling policies. We demonstrate the usefulness and efficiency of our model through a detailed validation process on the Amazon EC2 cloud, using two types of load patterns. Our experimental results show that it can be very effective in helping a cloud application owner configure an
auto-scaling policy in order to minimise the QoS violations.

Details

Original languageEnglish
Title of host publicationProceedings of 2017 IEEE/ACM 17th International Symposium on Cluster, Cloud and Grid Computing (CCGrid'17)
Publication statusPublished - 13 Jul 2017
EventIEEE/ACM 17th International Symposium on Cluster, Cloud and Grid Computing - Madrid, Spain
Duration: 14 May 201717 May 2017
https://www.arcos.inf.uc3m.es/wp/ccgrid2017/

Conference

ConferenceIEEE/ACM 17th International Symposium on Cluster, Cloud and Grid Computing
Abbreviated titleCCGrid 17
CountrySpain
CityMadrid
Period14/05/1717/05/17
Internet address

Keywords

  • cloud computing , probabilistic logic , computational modeling , quality of service , measurement , model checking , Markov processes