Cost-Availability Aware Scaling: Towards Optimal Scaling of Cloud Services

Abstract

Cloud services have become increasingly popular for developing large-scale applications due to the abundance of resources they offer. The scalability and accessibility of these resources have made it easier for organizations of all sizes to develop and implement sophisticated and demanding applications to meet demand instantly. As monetary fees are involved in the use of the cloud, one of the challenges for application developers and operators is to balance their budget constraints with crucial quality attributes, such as availability. Industry standards usually default to simplified solutions that cannot simultaneously consider competing objectives. Our research addresses this challenge by proposing a Cost-Availability Aware Scaling (CAAS) approach that uses multi-objective optimization of availability and cost. We evaluate CAAS using two open-source microservices applications, yielding improved results compared to the industry standard CPU-based Autoscaler (AS). CAAS can find optimal system configurations with higher availability, between 1 and 2 nines on average, and reduced costs, 6% on average, with the first application, and 1 nine of availability on average, and reduced costs up to 18% on average, with the second application. The gap in the results between our model and the default AS suggests that operators can significantly improve the operation of their applications.