DeskPred: Two-Stage Video Stream Bandwidth Prediction for Cold-Start and Training Forgetting in Cloud Desktops

Le résumé fourni par la source

As a cloud-hosted virtual desktop service, cloud desktop supports various fields such as telecommuting, collaborative development, while enabling real-time user interaction through video stream. The stability of this process is determined by bandwidth, which significantly influences the user experience. Therefore, precise bandwidth prediction of video streams is essential in cloud desktops. This work proposes DeskPred for video stream transmission in cloud desktops, focusing on dynamic bandwidth prediction. In the startup stage, the limited data amount poses a challenge for achieving precise bandwidth predictions. We propose an Affinity-based Federated Learning algorithm, which leverages the historical records of high-affinity users for assisted training, all while protecting user privacy. During the long-term adjustment stage, we propose a Fluctuation-based Adaptive Incremental Prediction algorithm for independent training to address the issue of pattern forgetting. The algorithm considers both periodic features and instantaneous features, incorporating new patterns while revisiting previous knowledge through the memory module and Adversarial Elastic Weight Consolidation. We have verified DeskPred through an actual cloud desktop project supported by Lenovo Research. Through experiments conducted on a total of over 18 million data items (approximately 10 GB), DeskPred achieves the highest total score of 71.11%, making it highly suitable for cloud desktop environments.

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