Performance modelling and verification of cloud-based auto-scaling policies

• Published in: Future generation computer systems Vol. 87; pp. 629 - 638
• Main Authors: Evangelidis, Alexandros, Parker, David, Bahsoon, Rami
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2018
• Subjects: Auto-scaling; Markov models; Probabilistic verification; Probabilistic verification; Markov models; Auto-scaling
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2017.12.047

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 the 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 techniques through a detailed validation process on two public cloud providers, Amazon EC2 and Microsoft Azure, targeting two cloud computing models, Infrastructure as a Service (IaaS) and Platform as a Service (PaaS), respectively. Our experimental results show that the modelling process along with the model itself can be very effective in providing the necessary formal reasoning to cloud application owners with respect to the configuration of their auto-scaling policies, and consequently helping them to specify an auto-scaling policy which could minimise QoS violations. •Proposes a framework for formal reasoning about cloud-based auto-scaling policies.•Presents a novel combination of performance modelling and formal verification.•Validation is done using real experiments on Amazon EC2 and Microsoft Azure cloud.