QoS4IVSaaS: a QoS management framework for intelligent video surveillance as a service

Abstract

Quality of service (QoS) is critical for real-time intelligent video surveillance as a service (IVSaaS) platform, which is both computation intensive and data intensive by nature. However, there is scarce work on a QoS framework for IVSaaS platform. In this paper, we propose QoS for intelligent video surveillance as a service, a QoS framework to make computing resources highly available. In the framework, multiple metrics such as throughput, loads of CPU/GPU, memory and IO are taken into account with different time series models to enhance the adaptivity of different video services. A model selection algorithm is proposed to choose the model that achieves the best performance under various error indicators. At the same time, a resource abnormality detection algorithm is designed to detect anomalies when a service is underperformed. Evaluation results show that the proposed QoS framework can successfully ensure QoS by dynamically scheduling computing resources.