Autoflex: Service Agnostic Auto-scaling Framework for IaaS Deployment Models

Elasticity is a key property to reduce the costs associated with running services in cloud systems that employ an infrastructure-as-a-service (IaaS) deployment model. However, to be able to exploit this property, users of IaaS systems need to be able to anticipate the short-term future demand of their own services, so that only the required infrastructure is requested at any instant in time. This guarantees that service level objectives (SLOs) are always honored by the users of IaaS systems, while over provisioning is avoided. The process of automatically change the amount of resources used to run a service in an IaaS system is named auto-scaling, and the state-of-the-practice uses simple reactive approaches. Although these approaches can successfully reduce the costs due to over provisioning, they are frequently insufficient to minimize the costs due to SLO violations. For that, proactive approaches are required. In this paper we propose a framework for the implementation of auto-scaling services that follows both reactive and proactive approaches. The latter is based on the use of a set of predictors of the future demand of services deployed over IaaS resources, and a selection mechanism that chooses, over time, what is the best predictor to be used. We have also proposed a correction method that uses historical data on the predictors' errors to reduce the probability of under provisioning the services, thus further diminishing the number of SLO violations. We have evaluated the performance of the proposed approach using production traces of HP customers. Our results show that costs savings of as much as 37% can be achieved, while the probability of an SLO violation can be kept, on average, as small as 0.008%, and never larger than 0.036%.