Multi-tier Service Differentiation: Coordinated Resource Provisioning and Admission Control

Multiple Internet applications are often hosted in one datacenter and share underlying virtualized server resources. It is important but challenging to provide differentiated treatment to co-hosted applications and improve overall performance with efficient use of limited resources. We propose a coordinated self-adaptive resource management and admission control for multi-tier Internet service differentiation and performance improvement in a shared virtualized platform. We develop reinforcement learning based approaches for virtual machine (VM) auto-configuration and session based admission control. VM auto-configuration simultaneously provisions proportional service differentiation between co-located applications and improves application response time. Admission control improves session throughput of the applications, minimizing resource wastage due to aborted sessions. A shared reward actualizes coordination between the two learning modules. For system agility and scalability, we integrate reinforcement learning with cascade neural networks. We implement the integrated approach in a virtualized blade server system hosting multi-tier RUBiS applications. Experimental results demonstrate that the approach accurately meets differentiation targets and achieves performance improvement of applications. Our approach reacts to dynamic bursty workloads in agile and scalable manner.