Research Area:  Cloud Computing
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.
Author(s) Name:  Alexandros Evangelidis; David Parker and Rami Bahsoon
Conferrence name:   17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)
Publisher name:  IEEE
Paper Link:   https://ieeexplore.ieee.org/document/7973721