ABSTRACT
Conventional wisdom claims that in order to improve viewer engagement, the cloud-edge providers should serve the viewers with the nearest edge nodes, however, we show that doing this for crowdsourced live streaming (CLS) services can introduce significant costs inefficiency. We observe that the massive number of channels has greatly burdened the operating expenditure of the cloud-edge providers, and most importantly, unbalanced viewer distribution makes the edge nodes suffer significant costs inefficiency. To tackle the above concerns, we propose AggCast, a novel CLS scheduling framework to optimize the edge node utilization for the cloud-edge provider. The core idea of AggCast is to aggregate some viewers who are initially scattered on different regions, and assign them to fewer pre-selected nodes, thereby reducing bandwidth costs. In particular, by leveraging the insights obtained from our large-scale measurement, AggCast can not only ensure quality of experience (QoS), but also satisfy the systematic requirements of CLS services. AggCast has been A/B tested and fully deployed in a top cloud-edge provider in China for over eight months. The online and trace-driven experiments show that, compared to the common practice, AggCast can save over 15% back-to-source (BTS) bandwidth costs while having no negative impacts on QoS.
Supplemental Material
- Vijay Kumar Adhikari, Yang Guo, Fang Hao, Matteo Varvello, Volker Hilt, Moritz Steiner, and Zhi-Li Zhang. 2012. Unreeling Netflix: Understanding and Improving Multi-cdn Movie Delivery. In 2012 Proceedings IEEE INFOCOM. IEEE, 1620--1628.Google Scholar
- A. Asheralieva and D. Niyato. 2020. Combining Contract Theory and Lyapunov Optimization for Content Sharing With Edge Caching and Device-to-Device Communications. IEEE/ACM Transactions on Networking 28, 3 (2020), 1213--1226.Google ScholarDigital Library
- Yonghwan Bang, June-Koo Kevin Rhee, KyungSoo Park, Kyongchun Lim, Giyoung Nam, John D Shinn, Jongmin Lee, Sungmin Jo, Ja-Ryeong Koo, Jonggyu Sung, et al. 2016. CDN Interconnection Service Trial: Implementation and Analysis. IEEE Communications Magazine 54, 6, 94--100.Google ScholarDigital Library
- Christopher M Bishop. 2006. Pattern Recognition and Machine Learning. springer.Google Scholar
- Timm Böttger, Felix Cuadrado, Gareth Tyson, Ignacio Castro, and Steve Uhlig. 2018. Open Connect Everywhere: A Glimpse at the Internet Ecosystem through the Lens of the Netflix CDN. Proceedings of the 2018 ACM SIGCOMM Conference 48, 1, 28--34.Google ScholarDigital Library
- Xingyan Chen, Changqiao Xu, and et. al. 2021. A Universal Transcoding and Transmission Method for Livecast with Networked Multi-Agent Reinforcement Learning. In 2021 Proceedings IEEE INFOCOM. IEEE, 945--946.Google ScholarDigital Library
- https://golang.org/. 2021. GO programming language.Google Scholar
- https://grpc.io/. 2021. gRPC communication protocol.Google Scholar
- https://www.businessofapps.com/data/twitch statistics/. 2020. Twitch Revenue and Usage Statistics.Google Scholar
- https://www.chinainternetwatch.com/30115/kwai-dec 2019/. 2020. Kuaishou Live Broadcast DAU Exceeded 100 Million in 2019.Google Scholar
- https://www.java.com/. 2021. JAVA programming language.Google Scholar
- Junchen Jiang, Rajdeep Das, Ganesh Ananthanarayanan, Philip A Chou, Venkata Padmanabhan, Vyas Sekar, Esbjorn Dominique, Marcin Goliszewski, Dalibor Kukoleca, Renat Vafin, et al. 2016. Via: Improving Internet Telephony Call Quality Using Predictive Relay Selection. In Proceedings of the 2016 ACM SIGCOMM Conference. 286--299.Google ScholarDigital Library
- Junchen Jiang, Vyas Sekar, Henry Milner, Davis Shepherd, Ion Stoica, and Hui Zhang. 2016. CFA: A Practical Prediction System for Video QoE Optimization. In 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 16). 137--150.Google ScholarDigital Library
- Junchen Jiang, Shijie Sun, Vyas Sekar, and Hui Zhang. 2017. Pytheas: Enabling Data-driven Quality of Experience Optimization Using Group-based Explorationexploitation. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17). 393--406.Google Scholar
- Scott Kirkpatrick, C Daniel Gelatt, and Mario P Vecchi. 1983. Optimization by Simulated Annealing. science 220, 4598, 671--680.Google Scholar
- Hongqiang Harry Liu, Ye Wang, Yang Richard Yang, Hao Wang, and Chen Tian. 2012. Optimizing Cost and Performance for Content Multihoming. In Proceedings of the 2012 ACM SIGCOMM Conference. 371--382.Google ScholarDigital Library
- Shuhao Liu and Baochun Li. 2017. Stemflow: Software-defined Inter-datacenter Overlay as a Service. IEEE Journal on Selected Areas in Communications (JSAC) 35, 11 (2017), 2563--2573.Google ScholarCross Ref
- Filipe F Mazzini, Geraldo R Mateus, and James Macgregor Smith. 2003. Lagrangean-based Methods for Solving Large-scale Cellular Network Design Problems. Wireless Networks 9, 6, 659--672.Google ScholarDigital Library
- Sanjay Melkote and Mark S Daskin. 2001. Capacitated Facility Location/Network Design Problems. European journal of operational research 129, 3, 481--495.Google Scholar
- Syam Menon and Rakesh Gupta. 2004. Assigning cells to switches in cellular networks by incorporating a pricing mechanism into simulated annealing. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34, 1, 558--565.Google ScholarDigital Library
- Yipei Niu, Bin Luo, Fangming Liu, Jiangchuan Liu, and Bo Li. 2015. When Hybrid Cloud Meets Flash Crowd: Towards Cost-effective Service Provisioning. In 2015 Proceedings IEEE INFOCOM. IEEE, 1044--1052.Google Scholar
- Haitian Pang, Cong Zhang, Fangxin Wang, Han Hu, Zhi Wang, Jiangchuan Liu, and Lifeng Sun. 2018. Optimizing Personalized Interaction Experience in Crowd- Interactive Livecast: A Cloud-Edge Approach. In Proceedings of the 26th ACM international conference on Multimedia. 1217--1225.Google ScholarDigital Library
- Rachee Singh, Sharad Agarwal, Matt Calder, and Paramvir Bahl. 2021. Costeffective Cloud Edge Traffic Engineering with Cascara. In 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21). USENIX Association.Google Scholar
- Abhishek Sinha and Eytan Modiano. 2017. Optimal Control for Generalized Network-flow Problems. IEEE/ACM Transactions on Networking 26, 1 (2017), 506--519.Google ScholarDigital Library
- Feng Wang, Jiangchuan Liu, Minghua Chen, and Haiyang Wang. 2014. Migration towards Cloud-assisted Live Media Streaming. IEEE/ACM Transactions on Networking 24, 1, 272--282.Google ScholarDigital Library
- Fangxin Wang, Cong Zhang, Jiangchuan Liu, Yifei Zhu, Haitian Pang, Lifeng Sun, et al. 2019. Intelligent Edge-assisted Crowdcast with Deep Reinforcement Learning for Personalized QoE. In 2019 Proceedings IEEE INFOCOM. IEEE, 910--918.Google Scholar
- Huan Wang, Kui Wu, Jianping Wang, and Guoming Tang. 2020. Rldish: Edgeassisted qoe optimization of http live streaming with reinforcement learning. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications. IEEE, 706--715.Google ScholarDigital Library
- Wenhua Xiao, Weidong Bao, Xiaomin Zhu, Chen Wang, Lidong Chen, and Laurence T Yang. 2015. Dynamic Request Redirection and Resource Provisioning for Cloud-based Video Services under Heterogeneous Environment. IEEE Transactions on Parallel and Distributed Systems 27, 7 (2015), 1954--1967.Google ScholarDigital Library
- Rui-Xiao Zhang, Tianchi Huang, Ming Ma, Haitian Pang, Xin Yao, Chenglei Wu, and Lifeng Sun. 2019. Enhancing the Crowdsourced Live Streaming: a Deep Reinforcement Learning Approach. In Proceedings of the 29th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video. 55--60.Google ScholarDigital Library
- Rui-Xiao Zhang, Ming Ma, Tianchi Huang, Hanyu Li, Jiangchuan Liu, and Lifeng Sun. 2020. Leveraging QoE Heterogenity for Large-Scale Livecaset Scheduling. In Proceedings of the 28th ACM International Conference on Multimedia. 3678--3686.Google ScholarDigital Library
- Rui-Xiao Zhang, Ming Ma, Tianchi Huang, Haitian Pang, Xin Yao, Chenglei Wu, Jiangchuan Liu, and Lifeng Sun. 2019. Livesmart: A QoS-Guaranteed Cost- Minimum Framework of Viewer Scheduling for Crowdsourced Live Streaming. In Proceedings of the 27th ACM International Conference on Multimedia. 420--428.Google ScholarDigital Library
Index Terms
- AggCast: Practical Cost-effective Scheduling for Large-scale Cloud-edge Crowdsourced Live Streaming
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