Container Based Video Surveillance Cloud Service with Fine-Grained Resource Provisioning

Abstract

Many current video surveillance cloud platforms are built based on Virtual Machine (VM) technology that usually induces the loss of service performance and brings some challenges in the service management agility. In addition, the efficient resource provisioning for the heterogeneous video services is also a challenging issue in such a dynamic and shared cloud environment. In this paper, we firstly design a novel video surveillance cloud platform that employs the lightweight container technology and is defined according to the ITU standards. Our platform can provide a flexible and reconfigurable video microservice management environment with the high service capacity. Secondly, we propose a predictive fine-grained resource provisioning approach that can periodically predict the future workload and perform the proactive resource supply for the video microservices in the cloud. Our approach utilizes the service similarity matching and the time-series nearest neighbor regression to efficiently predict the future resource requirements, and dynamically optimizes the usage of resources based on predictive results while ensuring quality of service. Finally, we implement the proposed platform, and conduct the extensive experiments. The experimental results indicate that the proposed solution provides the higher service deployment density, accurately predicts the resource demands and significantly improves the resource utilization.