A Performance-Aware Selection Strategy for Cloud-based Video Services with Micro-Service Architecture

Abstract

The cloud micro-service architecture provides loosely coupling services and efficient virtual resources, which becomes a promising solution for large-scale video services. It is difficult to efficiently select the optimal services under micro-service architecture, because the large number of micro-services leads to an exponential increase in the number of service selection candidate solutions. In addition, the time sensitivity of video services increases the complexity of service selection, and the video data can affects the service selection results. However, the current video service selection strategies are insufficient under micro-service architecture, because they do not take into account the resource fluctuation of the service instances and the features of the video service comprehensively. In this paper, we focus on the video service selection strategy under micro-service architecture. Firstly, we propose a QoS Prediction (QP) method using explicit factor analysis and linear regression. The QP can accurately predict the QoS values based on the features of video data and service instances. Secondly, we propose a Performance-Aware Video Service Selection (PVSS) method. We prune the candidate services to reduce computational complexity and then efficiently select the optimal solution based on Fruit Fly Optimization (FFO) algorithm. Finally, we conduct extensive experiments to evaluate our strategy, and the results demonstrate the effectiveness of our strategy.