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|Title:||Dynamic priority-based resource provisioning for video transcoding with heterogeneous QoS||Authors:||Gao, Guanyu
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2018||Source:||Gao, G., Wen, Y., & Westphal, C. (2019). Dynamic priority-based resource provisioning for video transcoding with heterogeneous QoS. IEEE Transactions on Circuits and Systems for Video Technology, 29(5), 1515-1529. doi:10.1109/TCSVT.2018.2840351||Journal:||IEEE Transactions on Circuits and Systems for Video Technology||Abstract:||Video transcoding is widely adopted in online video services to transcode videos into multiple representations for dynamic adaptive bitrate streaming. This solution may consume significant resources and incur intolerable processing delays. Meanwhile, different videos have different quality-of-service (QoS) requirements for transcoding. Delay-sensitive videos must be transcoded within a strict deadline, whereas delay-tolerant videos are not required to be transcoded immediately. Some intelligent policies are required for provisioning the right amount of resources in the transcoding system to meet the heterogeneous QoS requirements, especially under dynamic workloads. To this end, we develop a robust dynamic priority-based resource provisioning scheme for video transcoding. We adopt the preemptive resume priority discipline to design a multiple-priority transcoding mechanism. The system performs the transcoding for delay-tolerant videos by utilizing idle resources for improving resource utilization while not affecting the transcoding for delay-sensitive videos. We adopt the model predictive control framework to design an online algorithm for dynamic resource provisioning to accommodate time-varying workloads by predicting future workloads. To seek performance robustness against prediction noise, we improve the performance of our online algorithm via robust design. The experimental results demonstrate that our proposed method can satisfy the heterogeneous QoS requirements while significantly reducing computing resource consumption.||URI:||https://hdl.handle.net/10356/142288||ISSN:||1051-8215||DOI:||10.1109/TCSVT.2018.2840351||Rights:||© 2018 IEEE. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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