Context Sensitive Efficient Automatic Resource Scheduling for Cloud Applications

Nowadays applications in cloud computing often use containers as their service infrastructures to provide online services. Applications deployed in lightweight containers can be quickly started and stopped, which adapt to changing resource requirements brought from workload fluctuation. Existing works to schedule physical resources often model applications’ performance according to a training dataset, but they cannot self-adapt dynamic deployment environment. To address this above issue, this paper proposes a feedback based physical resource scheduling approach for containers to deal with the significant changes of physical resources brought from dynamic workloads. First, we model applications’ performance with a queue-based model, which represents the correlations between workloads and performance metrics. Second, we predict applications’ response time by adjusting the parameters of the application performance model with a fuzzy Kalman filter. Finally, we schedule physical resources according to the predicted response time. Experimental results show that our approach can adapt to dynamic workloads and achieve high performance.