Towards Faster Response Time Models for Vertical Elasticity

Abstract

Resource provisioning in cloud computing is typically coarse-grained. For example, entire CPU cores may be allocated for periods of up to an hour. The Resource-as-a-Service cloud concept has been introduced to improve the efficiency of resource utilization in clouds. In this concept, resources are allocated in terms of CPU core fractions, with granularities of seconds. Such infrastructures could be created using existing technologies such as lightweight virtualization using LXC or by exploiting the Xen hyper visor's capacity for vertical elasticity. However, performance models for determining how much capacity to allocate to each application are currently lacking. To address this deficit, we evaluate two performance models for predicting mean response times: the previously proposed queue length model and the novel inverse model. The models are evaluated using 3 applications under both open and closed system models. The inverse model reacted rapidly and remained stable even with targets as low as 0.5 seconds.