Abstract
Video streaming applications (VSAs) are increasingly being deployed on large-scale edge platforms, which have the potential to significantly improve the quality of service (QoS) and end-user experience (QoE), ultimately maximizing business outcomes. However, there is currently very little understanding of how QoS, QoE, and the impact of QoS on QoE for VSAs on edge platforms in the wild and at scale. To close the knowledge gap, we collect SNESet, an active measurement dataset comprising QoS and QoE telemetry metrics of 8 VSAs over four months, covering end-users from 798 edge sites,30 cities, and 3 ISPs in one country.We characterize and compare the QoS and QoE metrics in SNESet with existing publicly available datasets, highlighting that SNESet includes a significantly greater number of metrics (horizontal diversity and vertical hierarchy) and provides more comprehensive coverage of specific metrics.Moreover, we qualitatively and quantitatively analyze the impact of QoS on QoE in both domain-general and domain-specific scenarios. Our findings can inform the system design decisions that different entities in the video ecosystem (content providers, video player designers, third-party optimizers, edge vendors) make to maximize end-users experience and ultimately maximize the business outcomes. We hope SNESet can attract more research efforts in the data management community, computer network community, and beyond.
- Ziawasch Abedjan, Xu Chu, Dong Deng, Raul Castro Fernandez, Ihab F Ilyas, Mourad Ouzzani, Paolo Papotti, Michael Stonebraker, and Nan Tang. 2016. Detecting data errors: Where are we and what needs to be done? Proceedings of the VLDB Endowment, Vol. 9, 12 (2016), 993--1004.Google ScholarDigital Library
- Vijay K Adhikari, Yang Guo, Fang Hao, Volker Hilt, Zhi-Li Zhang, and Matteo Varvello. 2014. Measurement study of Netflix, Hulu, and a tale of three CDNs. IEEE/ACM Transactions On Networking, Vol. 23, 6 (2014), 1984--1997.Google ScholarDigital Library
- Akamail. 2020. Measuring Video Quality and Performance: Best Practices. https://www.akamai.com/site/it/documents/white-paper/measuring-video-quality-and-performance-best-practices.pdfGoogle Scholar
- Zahaib Akhtar, Yun Seong Nam, Jessica Chen, Ramesh Govindan, Ethan Katz-Bassett, and etc. Rao, Sanjay. 2018a. Understanding video management planes. In Proceedings of the ACM IMC Conference. 238--251.Google ScholarDigital Library
- Zahaib Akhtar, Yun Seong Nam, Ramesh Govindan, Sanjay Rao, Jessica Chen, Ethan Katz-Bassett, Bruno Ribeiro, Jibin Zhan, and Hui Zhang. 2018b. Oboe: Auto-tuning video ABR algorithms to network conditions. In Proceedings of the ACM SIGCOMM Conference. 44--58.Google ScholarDigital Library
- Alibaba. 2023. What is ENS? https://www.alibabacloud.com/help/en/ens/latest/e49d6bGoogle Scholar
- Lamine Amour, Souihi Sami, Said Hoceini, and Abdelhamid Mellouk. 2015. Building a large dataset for model-based QoE prediction in the mobile environment. In Proceedings of the ACM MSWiM Conference. 313--317.Google ScholarDigital Library
- Arvind Arasu et al. 2009. A grammar-based entity representation framework for data cleaning. In Proceedings of the ACM SIGMOD Conference. 233--244.Google Scholar
- Consumer Technology Association. 2020. Streaming Quality of Experience Events, Properties and Metrics. Technical Report. Arlington, VA, USA.Google Scholar
- Santiago Andrés Azcoitia et al. 2022. A survey of data marketplaces and their business models. ACM SIGMOD Record, Vol. 51, 3 (2022), 18--29.Google ScholarDigital Library
- Athula Balachandran, Vyas Sekar, Aditya Akella, Srinivasan Seshan, et al. 2013. Developing a predictive model of quality of experience for internet video. ACM SIGCOMM Computer Communication Review, Vol. 43, 4 (2013), 339--350.Google ScholarDigital Library
- Kaustubh Beedkar et al. 2021. Compliant geo-distributed query processing. In Proceedings of the ACM SIGMOD Conference. 181--193.Google Scholar
- James Bergstra and Yoshua Bengio. 2012. Random search for hyper-parameter optimization. Journal of machine learning research, Vol. 13, 2 (2012).Google Scholar
- Anant Bhardwaj, Souvik Bhattacherjee, Amit Chavan, Amol Deshpande, Aaron J Elmore, et al. 2015. Datahub: Collaborative data science & dataset version management at scale. In Proceedings of the CIDR Conference.Google Scholar
- BITAG. 2021. 2020 PandemicNetwork Performance. https://bitag.org/documents/bitag_report.pdfGoogle Scholar
- Niklas Blum, Serge Lachapelle, et al. 2021. WebRTC: Real-time communication for the open web platform. Commun. ACM, Vol. 64, 8 (2021), 50--54.Google ScholarDigital Library
- Alex Bogatu, Alvaro AA Fernandes, Norman W Paton, and Nikolaos Konstantinou. 2020. Dataset discovery in data lakes. In Proceedings of the IEEE ICDE Conference. IEEE, 709--720.Google ScholarCross Ref
- Francesco Bronzino, Paul Schmitt, Sara Ayoubi, Guilherme Martins, Renata Teixeira, and Nick Feamster. 2019. Inferring streaming video quality from encrypted traffic: Practical models and deployment experience. Proceedings of the ACM SIGMETRICS Conference, Vol. 3, 3 (2019), 1--25.Google ScholarDigital Library
- Wei Cao, Yusong Gao, Bingchen Lin, Xiaojie Feng, Yu Xie, Xiao Lou, and Peng Wang. 2018. Tcprt: Instrument and diagnostic analysis system for service quality of cloud databases at massive scale in real-time. In Proceedings of the ACM SIGMOD Conference. 615--627.Google ScholarDigital Library
- Riccardo Cappuzzo, Paolo Papotti, and Saravanan Thirumuruganathan. 2020. Creating embeddings of heterogeneous relational datasets for data integration tasks. In Proceedings of the ACM SIGMOD Conference. 1335--1349.Google ScholarDigital Library
- Paris Carbone, Asterios Katsifodimos, Stephan Ewen, Volker Markl, et al. 2015. Apache flink: Stream and batch processing in a single engine. The Bulletin of the Technical Committee on Data Engineering, Vol. 38, 4 (2015).Google Scholar
- Neal Cardwell, Yuchung Cheng, and etc. Gunn, C Stephen. 2017. BBR: congestion-based congestion control. Commun. ACM, Vol. 60, 2 (2017), 58--66.Google ScholarDigital Library
- Giovanna Carofiglio, Giulio Grassi, Enrico Loparco, Luca Muscariello, Michele Papalini, and Jacques Samain. 2021. Characterizing the relationship between application QoE and network QoS for real-time services. In Proceedings of the ACM SIGCOMM Workshop on Network-application Integration. 20--25.Google ScholarDigital Library
- Hyunseok Chang, Matteo Varvello, Fang Hao, and Sarit Mukherjee. 2021. Can you see me now? A measurement study of Zoom, Webex, and Meet. In Proceedings of the ACM IMC Conference. 216--228.Google Scholar
- Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the ACM SIGKDD Conference. 785--794.Google ScholarDigital Library
- Xu Chu, Ihab F Ilyas, Sanjay Krishnan, and Jiannan Wang. 2016. Data cleaning: Overview and emerging challenges. In Proceedings of the ACM SIGMOD Conference. 2201--2206.Google ScholarDigital Library
- Eli Cortez, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Fontoura, and Ricardo Bianchini. 2017. Resource central: Understanding and predicting workloads for improved resource management in large cloud platforms. In Proceedings of the ACM SOSP Conference. 153--167.Google ScholarDigital Library
- Umur Cubukcu, Ozgun Erdogan, Sumedh Pathak, Sudhakar Sannakkayala, and Marco Slot. 2021. Citus: Distributed postgresql for data-intensive applications. In Proceedings of the ACM SIGMOD Conference. 2490--2502.Google ScholarDigital Library
- Michele Dallachiesa, Amr Ebaid, Ahmed Eldawy, Ahmed Elmagarmid, Ihab F Ilyas, Mourad Ouzzani, and Nan Tang. 2013. NADEEF: a commodity data cleaning system. In Proceedings of the ACM SIGMOD Conference. 541--552.Google ScholarDigital Library
- Florin Dobrian, Vyas Sekar, Asad Awan, Ion Stoica, Dilip Joseph, and Aditya Ganjam. 2011. Understanding the impact of video quality on user engagement. ACM SIGCOMM Computer Communication Review, Vol. 41, 4 (2011), 362--373.Google ScholarDigital Library
- Zhengfang Duanmu, Wentao Liu, Diqi Chen, Zhuoran Li, Zhou Wang, Yizhou Wang, and Wen Gao. 2019. A knowledge-driven quality-of-experience model for adaptive streaming videos. arXiv preprint arXiv:1911.07944 (2019).Google Scholar
- Nagabhushan Eswara, S Ashique, Anand Panchbhai, Soumen Chakraborty, Hemanth P Sethuram, Kiran Kuchi, Abhinav Kumar, and Sumohana S Channappayya. 2019. Streaming video QoE modeling and prediction: A long short-term memory approach. IEEE Transactions on Circuits and Systems for Video Technology, Vol. 30, 3 (2019), 661--673.Google ScholarDigital Library
- Kevin R Fall and W Richard Stevens. 2011. TCP/IP illustrated, volume 1: The protocols. addison-Wesley.Google Scholar
- Raul Castro Fernandez, Ziawasch Abedjan, Famien Koko, Gina Yuan, Samuel Madden, and Michael Stonebraker. 2018. Aurum: A data discovery system. In Proceedings of the IEEE ICDE Conference. IEEE, 1001--1012.Google Scholar
- Guanyu Gao, Linsen Dong, Huaizheng Zhang, and Yonggang Wen. 2019. Content-aware personalised rate adaptation for adaptive streaming via deep video analysis. In Proceedings of the IEEE ICC Conference. IEEE, 1--8.Google ScholarCross Ref
- Nishant Garg. 2013. Apache kafka. Packt Publishing Birmingham, UK.Google Scholar
- Apache Grafana. 2023. Apache Grafana. https://grafana.com/Google Scholar
- Alibaba Group. 2018. Alibaba Cluster Trace Program. https://github.com/alibaba/clusterdata/blob/master/cluster-trace-v2018/trace_2018.mdGoogle Scholar
- Jing Guo, Zihao Chang, Sa Wang, Haiyang Ding, Yihui Feng, Liang Mao, and Yungang Bao. 2019. Who limits the resource efficiency of my datacenter: An analysis of alibaba datacenter traces. In Proceedings of the IEEE IWQoS Conference. 1--10.Google ScholarDigital Library
- Ori Hadary, Luke Marshall, Ishai Menache, Abhisek Pan, Esaias E Greeff, David Dion, Star Dorminey, Shailesh Joshi, Yang Chen, Mark Russinovich, et al. 2020. Protean: VM allocation service at scale. In Proceedings of the USENIX OSDI Conference. 845--861.Google Scholar
- Bo Han, Yu Liu, and Feng Qian. 2020. ViVo: Visibility-aware mobile volumetric video streaming. In Proceedings of the ACM MobiCom Conference. 1--13.Google ScholarDigital Library
- Arvid Heise, Gjergji Kasneci, et al. 2014. Estimating the number and sizes of fuzzy-duplicate clusters. In Proceedings of the ACM CIKM Conference. 959--968.Google ScholarDigital Library
- Tin Kam Ho. 1995. Random decision forests. In Proceedings of International Conference on Document Analysis and Recognition, Vol. 1. IEEE, 278--282.Google Scholar
- Yun Chao Hu, Milan Patel, Dario Sabella, Nurit Sprecher, and Valerie Young. 2015. Mobile edge computing-A key technology towards 5G. ETSI white paper, Vol. 11, 11 (2015), 1--16.Google Scholar
- Haoyu Huang and Shahram Ghandeharizadeh. 2021. Nova-LSM: a distributed, component-based LSM-tree key-value store. In Proceedings of the ACM SIGMOD Conference. 749--763.Google ScholarDigital Library
- Tianchi Huang, Chao Zhou, Rui-Xiao Zhang, Chenglei Wu, et al. 2020. Stick: A harmonious fusion of buffer-based and learning-based approach for adaptive streaming. In Proceedings of the IEEE INFOCOM Conference. IEEE, 1967--1976.Google ScholarDigital Library
- Tianchi Huang, Chao Zhou, Rui-Xiao Zhang, Chenglei Wu, and Lifeng Sun. 2022. Learning tailored adaptive bitrate algorithms to heterogeneous network conditions: A domain-specific priors and meta-reinforcement learning approach. IEEE Journal on Selected Areas in Communications, Vol. 40, 8 (2022), 2485--2503.Google ScholarCross Ref
- Te-Yuan Huang, Ramesh Johari, Nick McKeown, et al. 2014. A buffer-based approach to rate adaptation: Evidence from a large video streaming service. In Proceedings of the ACM SIGCOMM Conference. 187--198.Google ScholarDigital Library
- Yuzhen Huang, Xiao Yan, Guanxian Jiang, Tatiana Jin, James Cheng, An Xu, et al. 2019. Tangram: bridging immutable and mutable abstractions for distributed data analytics. In Proceedings of the USENIX ATC Conference. 191--206.Google Scholar
- ITU. 2017. Vocabulary for performance, quality of service and quality of experience. https://www.itu.int/rec/T-REC-P.10--201711-I/enGoogle Scholar
- Subramania Jayaraman, S Esakkirajan, and T Veerakumar. 2009. Digital image processing. Vol. 7014. Tata McGraw Hill Education New Delhi.Google Scholar
- Martin Jergler, Mohammad Sadoghi, and Hans-Arno Jacobsen. 2015. D2WORM: A management infrastructure for distributed data-centric workflows. In Proceedings of the ACM SIGMOD Conference. 1427--1432.Google ScholarDigital Library
- Junchen Jiang, Vyas Sekar, Ion Stoica, et al. 2013. Shedding light on the structure of internet video quality problems in the wild. In Proceedings of the ACM CoNEXT Conference. 357--368.Google ScholarDigital Library
- Junchen Jiang, Vyas Sekar, and Hui Zhang. 2012. Improving fairness, efficiency, and stability in http-based adaptive video streaming with festive. In Proceedings of the ACM CoNEXT Conference. 97--108.Google ScholarDigital Library
- Zhimeng Jiang, Kaixiong Zhou, Zirui Liu, Li Li, Rui Chen, Soo-Hyun Choi, and Xia Hu. 2021. An information fusion approach to learning with instance-dependent label noise. In Proceedings of the ICLR Conference.Google Scholar
- Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. Proceedings of the NIPS Conference, Vol. 30 (2017).Google Scholar
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google Scholar
- Kongjian. 2022. Taobao System Activity Reporter. https://github.com/alibaba/tsarGoogle Scholar
- Alok Gautam Kumbhare, Reza Azimi, Ioannis Manousakis, Anand Bonde, et al. 2021. Prediction-Based power oversubscription in cloud platforms.. In Proceedings of the USENIX ATC Conference. 473--487.Google Scholar
- Adam Langley, Alistair Riddoch, Alyssa Wilk, Antonio Vicente, Charles Krasic, Dan Zhang, Fan Yang, Fedor Kouranov, Ian Swett, Janardhan Iyengar, et al. 2017. The quic transport protocol: Design and internet-scale deployment. In Proceedings of the ACM SIGCOMM Conference. 183--196.Google ScholarDigital Library
- Jinyang Li, Zhenyu Li, Ri Lu, Kai Xiao, Songlin Li, Jufeng Chen, Jingyu Yang, Chunli Zong, Aiyun Chen, Qinghua Wu, et al. 2022. LiveNet: a low-latency video transport network for large-scale live streaming. In Proceedings of the ACM SIGCOMM Conference. 812--825.Google ScholarDigital Library
- Xiang Li, Fabing Li, and Mingyu Gao. 2023. Flare: A Fast, Secure, and Memory-Efficient Distributed Analytics Framework. Proceedings of the VLDB Endowment, Vol. 16, 6 (2023), 1439--1452.Google ScholarDigital Library
- Yuliang Li, Xiaolan Wang, Zhengjie Miao, and Wang-Chiew Tan. 2021. Data augmentation for ml-driven data preparation and integration. Proceedings of the VLDB Endowment, Vol. 14, 12 (2021), 3182--3185.Google ScholarDigital Library
- Zhi Li, Xiaoqing Zhu, Joshua Gahm, Rong Pan, Hao Hu, Ali C Begen, and David Oran. 2014. Probe and adapt: Rate adaptation for HTTP video streaming at scale. IEEE Journal on Selected Areas in Communications, Vol. 32, 4 (2014), 719--733.Google ScholarCross Ref
- Xianshang Lin, Yunfei Ma, Junshao Zhang, Yao Cui, Jing Li, Shi Bai, Ziyue Zhang, Dennis Cai, Hongqiang Harry Liu, and Ming Zhang. 2022. GSO-simulcast: global stream orchestration in simulcast video conferencing systems. In Proceedings of the ACM SIGCOMM Conference. 826--839.Google ScholarDigital Library
- Xingchi Liu, Mahsa Derakhshani, and Lyudmila Mihaylova. 2022. Risk-Aware Contextual Learning for Edge-Assisted Crowdsourced Live Streaming. IEEE Journal on Selected Areas in Communications (2022).Google Scholar
- YouTube Live. 2023. https://www.youtube.com/liveGoogle Scholar
- Shutian Luo, Huanle Xu, Chengzhi Lu, Kejiang Ye, Guoyao Xu, Liping Zhang, Yu Ding, Jian He, and Chengzhong Xu. 2021. Characterizing microservice dependency and performance: Alibaba trace analysis. In Proceedings of the ACM SoCC Conference. 412--426.Google ScholarDigital Library
- Kyle MacMillan, Tarun Mangla, and James Saxon. 2021. Measuring the performance and network utilization of popular video conferencing applications. In Proceedings of the ACM IMC Conference. 229--244.Google ScholarDigital Library
- Hongzi Mao, Ravi Netravali, et al. 2017. Neural adaptive video streaming with pensieve. In Proceedings of the ACM SIGCOMM Conference. 197--210.Google ScholarDigital Library
- Matthew Mathis, Jeffrey Semke, Jamshid Mahdavi, and Teunis Ott. 1997. The macroscopic behavior of the TCP congestion avoidance algorithm. ACM SIGCOMM Computer Communication Review, Vol. 27, 3 (1997), 67--82.Google ScholarDigital Library
- Justin Meza, Tianyin Xu, Kaushik Veeraraghavan, and Onur Mutlu. 2018. A large scale study of data center network reliability. In Proceedings of the ACM IMC Conference. 393--407.Google ScholarDigital Library
- Zhengjie Miao, Yuliang Li, and Xiaolan Wang. 2021. Rotom: A meta-learned data augmentation framework for entity matching, data cleaning, text classification, and beyond. In Proceedings of the ACM SIGMOD Conference. 1303--1316.Google ScholarDigital Library
- Oliver Michel, Satadal Sengupta, Hyojoon Kim, Ravi Netravali, and Jennifer Rexford. 2022. Enabling passive measurement of zoom performance in production networks. In Proceedings of the ACM IMC Conference. 244--260.Google ScholarDigital Library
- Tom Michael Mitchell et al. 2007. Machine learning. Vol. 1. McGraw-hill New York.Google Scholar
- Ricky KP Mok, Hongyu Zou, Rui Yang, Tom Koch, Ethan Katz-Bassett, and Kimberly C Claffy. 2021. Measuring the network performance of Google Cloud platform. In Proceedings of the ACM IMC Conference. 54--61.Google ScholarDigital Library
- Hyunwoo Nam, Kyung-Hwa Kim, and Henning Schulzrinne. 2016. QoE matters more than QoS: Why people stop watching cat videos. In Proceedings of the IEEE INFOCOM Conference. IEEE, 1--9.Google ScholarDigital Library
- Jyoti Nandimath, Ekata Banerjee, Ankur Patil, Pratima Kakade, Saumitra Vaidya, et al. 2013. Big data analysis using Apache Hadoop. In Proceedings of the IEEE IRI Conference. IEEE, 700--703.Google ScholarCross Ref
- Arvind Narayanan, Xumiao Zhang, Ruiyang Zhu, Ahmad Hassan, Shuowei Jin, Xiao Zhu, Xiaoxuan Zhang, Denis Rybkin, and Zhengxuan Yang. 2021. A variegated look at 5G in the wild: performance, power, and QoE implications. In Proceedings of the ACM SIGCOMM Conference. 610--625.Google ScholarDigital Library
- Netflix. 2022. VMAF - Video Multi-Method Assessment Fusion. https://github.com/Netflix/vmafGoogle Scholar
- Cagri Ozcinar and Julián Cabrera. 2019. Visual attention-aware omnidirectional video streaming using optimal tiles for virtual reality. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, Vol. 9, 1 (2019), 217--230.Google ScholarCross Ref
- Jitendra Padhye, Victor Firoiu, Don Towsley, and Jim Kurose. 1998. Modeling TCP throughput: A simple model and its empirical validation. In Proceedings of the ACM SIGCOMM Conference. 303--314.Google ScholarDigital Library
- H Parmar and M Thornburgh. 2012. Adobe's real time messaging protocol. Copyright Adobe Systems Incorporated (2012), 1--52.Google Scholar
- Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin. 2018. CatBoost: unbiased boosting with categorical features. Proceedings of the NIPS Conference, Vol. 31 (2018).Google Scholar
- Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, et al. 2022. Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML. NET Pipelines. ACM SIGMOD Record, Vol. 51, 2 (2022), 30--37.Google ScholarDigital Library
- Qifan Pu, Ganesh Ananthanarayanan, Peter Bodik, Srikanth Kandula, Aditya Akella, et al. 2015. Low latency geo-distributed data analytics. ACM SIGCOMM Computer Communication Review, Vol. 45, 4 (2015), 421--434.Google ScholarDigital Library
- Eric Rescorla. 2000. Http over tls. Technical Report.Google Scholar
- Anthony Robins. 1995. Catastrophic forgetting, rehearsal and pseudorehearsal. Connection Science, Vol. 7, 2 (1995), 123--146.Google ScholarCross Ref
- Werner Robitza, Marie-Neige Garcia, and Alexander Raake. 2017. A modular http adaptive streaming qoe model-candidate for itu-t p. 1203 (?p. nats"). In Proceedings of the IEEE QoMEX Conference. IEEE, 1--6.Google Scholar
- Stéphane Ross, Geoffrey Gordon, and Drew Bagnell. 2011. A reduction of imitation learning and structured prediction to no-regret online learning. In Proceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 627--635.Google Scholar
- Arjun Roy, Hongyi Zeng, Jasmeet Bagga, George Porter, and Alex C Snoeren. 2015. Inside the social network's (datacenter) network. In Proceedings of the ACM SIGCOMM Conference. 123--137.Google ScholarDigital Library
- Mohammad Shahrad, Rodrigo Fonseca, Í nigo Goiri, Gohar Chaudhry, Paul Batum, Jason Cooke, Eduardo Laureano, Colby Tresness, Mark Russinovich, and Ricardo Bianchini. 2020. Serverless in the wild: Characterizing and optimizing the serverless workload at a large cloud provider. In Proceedings of the USENIX ATC Conference. 205--218.Google Scholar
- Zeyuan Shang, Emanuel Zgraggen, Benedetto Buratti, Ferdinand Kossmann, Philipp Eichmann, Yeounoh Chung, Carsten Binnig, Eli Upfal, and Tim Kraska. 2019. Democratizing data science through interactive curation of ml pipelines. In Proceedings of the ACM SIGMOD Conference. 1171--1188.Google ScholarDigital Library
- Jie Song, George Alter, and HV Jagadish. 2019. C2Metadata: Automating the capture of data transformations from statistical scripts in data documentation. In Proceedings of the ACM SIGMOD Conference. 2005--2008.Google Scholar
- Kevin Spiteri, Rahul Urgaonkar, and Ramesh K Sitaraman. 2020. BOLA: Near-optimal bitrate adaptation for online videos. IEEE/ACM Transactions On Networking, Vol. 28, 4 (2020), 1698--1711.Google ScholarDigital Library
- Yi Sun, Xiaoqi Yin, Junchen Jiang, Vyas Sekar, Fuyuan Lin, Nanshu Wang, Tao Liu, and Bruno Sinopoli. 2016. CS2P: Improving video bitrate selection and adaptation with data-driven throughput prediction. In Proceedings of the ACM SIGCOMM Conference. 272--285.Google ScholarDigital Library
- Rebecca Taft, Irfan Sharif, Andrei Matei, Nathan VanBenschoten, Jordan Lewis, Tobias Grieger, Kai Niemi, Andy Woods, Anne Birzin, Raphael Poss, et al. 2020. Cockroachdb: The resilient geo-distributed sql database. In Proceedings of the ACM SIGMOD Conference. 1493--1509.Google ScholarDigital Library
- Alibaba Taobao Tengine. 2023. https://github.com/alibaba/tengine Retrieved Jul 20, 2023 fromGoogle Scholar
- TikTok. 2023. https://www.tiktok.com/Google Scholar
- Muhammad Tirmazi, Adam Barker, Nan Deng, Md E Haque, Zhijing Gene Qin, Steven Hand, et al. 2020. Borg: the next generation. In Proceedings of the ACM EuroSys Conference. 1--14.Google ScholarDigital Library
- Ruben Torres, Alessandro Finamore, Jin Ryong Kim, Marco Mellia, et al. 2011. Dissecting video server selection strategies in the youtube cdn. In Proceedings of the IEEE ICDCS Conference. IEEE, 248--257.Google ScholarDigital Library
- Vladislav Vasilev, Jérémie Leguay, Stefano Paris, Lorenzo Maggi, and Mérouane Debbah. 2018. Predicting QoE factors with machine learning. In Proceedings of the IEEE ICC Conference. IEEE, 1--6.Google ScholarCross Ref
- Abhishek Verma, Luis Pedrosa, Madhukar Korupolu, David Oppenheimer, Eric Tune, and John Wilkes. 2015. Large-scale cluster management at Google with Borg. In Proceedings of the ACM EuroSys Conference. 1--17.Google ScholarDigital Library
- Raajay Viswanathan, Ganesh Ananthanarayanan, and Aditya Akella. 2016. CLARINET:WAN-Aware Optimization for Analytics Queries. In Proceedings of the USENIX OSDI Conference. 435--450.Google Scholar
- Ashish Vulimiri, Carlo Curino, P Brighten Godfrey, Thomas Jungblut, Jitu Padhye, and George Varghese. 2015. Global analytics in the face of bandwidth and regulatory constraints. In Proceedings of the USENIX NSDI Conference. 323--336.Google Scholar
- Ashish Vulimiriu, Carlo Curinom, Brighten Godfreyu, Konstantinos Karanasosm, and George Varghesem. 2015. WANalytics: Analytics for a geo-distributed data-intensive world. Proceedings of the CIDR Conference.Google ScholarDigital Library
- Zhaohua Wang, Zhenyu Li, Guangming Liu, Yunfei Chen, and Qinghua Wu. 2021. Examination of WAN traffic characteristics in a large-scale data center network. In Proceedings of the ACM IMC Conference. 1--14.Google ScholarDigital Library
- Zhou Wang, Eero P Simoncelli, and Alan C Bovik. 2003. Multiscale structural similarity for image quality assessment. In The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, Vol. 2. Ieee, 1398--1402.Google ScholarCross Ref
- Qizhen Weng, Wencong Xiao, Yinghao Yu, Wei Wang, Cheng Wang, Jian He, Yong Li, Liping Zhang, Wei Lin, and Yu Ding. 2022. MLaaS in the Wild: Workload Analysis and Scheduling in Large-Scale Heterogeneous GPU Clusters. In Proceedings of the USENIX NSDI Conference. 945--960.Google Scholar
- Doris Xin, Hui Miao, Aditya Parameswaran, and Neoklis Polyzotis. 2021. Production machine learning pipelines: Empirical analysis and optimization opportunities. In Proceedings of the ACM SIGMOD Conference. 2639--2652.Google ScholarDigital Library
- Pengcheng Xiong, Hakan Hacigumus, et al. 2014. A software-defined networking based approach for performance management of analytical queries on distributed data stores. In Proceedings of the ACM SIGMOD Conference. 955--966.Google ScholarDigital Library
- Mengwei Xu, Zhe Fu, Xiao Ma, Li Zhang, Yanan Li, Feng Qian, Shangguang Wang, Ke Li, et al. 2021. From cloud to edge: a first look at public edge platforms. In Proceedings of the ACM IMC Conference. 37--53.Google ScholarDigital Library
- An Yan and Bill Howe. 2021. Equitensors: Learning fair integrations of heterogeneous urban data. In Proceedings of the ACM SIGMOD Conference. 2338--2347.Google ScholarDigital Library
- Francis Y Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong, Keyi Zhang, Philip Alexander Levis, and Keith Winstein. 2020. Learning in situ: a randomized experiment in video streaming. In Proceedings of the USENIX NSDI Conference, Vol. 20. 495--511.Google Scholar
- Xinan Yan, Linguan Yang, Hongbo Zhang, Xiayue Charles Lin, Bernard Wong, et al. 2018. Carousel: Low-latency transaction processing for globally-distributed data. In Proceedings of the ACM SIGMOD Conference. 231--243.Google ScholarDigital Library
- Gunce Su Yilmaz, Tana Wattanawaroon, Liqi Xu, Abhishek Nigam, Aaron J Elmore, and Aditya Parameswaran. 2018. Datadiff: User-interpretable data transformation summaries for collaborative data analysis. In Proceedings of the ACM SIGMOD Conference. 1769--1772.Google ScholarDigital Library
- Xiaoqi Yin, Abhishek Jindal, Vyas Sekar, and Bruno Sinopoli. 2015. A control-theoretic approach for dynamic adaptive video streaming over HTTP. In Proceedings of the ACM SIGCOMM Conference. 325--338.Google ScholarDigital Library
- Junyong You. 2021. Long short-term convolutional transformer for no-reference video quality assessment. In Proceedings of the ACM MM Conference. 2112--2120.Google ScholarDigital Library
- Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, and Xia Hu. 2023. Data-centric artificial intelligence: A survey. arXiv preprint arXiv:2303.10158 (2023).Google Scholar
- Yue Zha and Jing Li. 2020. Virtualizing FPGAs in the cloud. In Proceedings of the ACM ASPLOS Conference. 845--858.Google ScholarDigital Library
- Li Zhang, Zhe Fu, Boqing Shi, Xiang Li, Rujin Lai, Chenyang Chen, Ao Zhou, Xiao Ma, Shangguang Wang, and Mengwei Xu. 2022. SoC-Cluster as an Edge Server: an Application-driven Measurement Study. arXiv preprint arXiv:2212.12842 (2022).Google Scholar
- Zicheng Zhang, Wei Wu, Wei Sun, Dangyang Tu, Wei Lu, Xiongkuo Min, Ying Chen, and Guangtao Zhai. 2023. MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos. In Proceedings of the IEEE CVPR Conference.Google ScholarCross Ref
- Azure Edge Zones. 2020. Microsoft partners with the industry to unlock new 5G scenarios with Azure Edge Zones. https://azure.microsoft.com/en-us/blog/microsoft-partners-with-the-industry-to-unlock-new-5g-scenarios-with-azure-edge-zones/ Retrieved Mar 31, 2020 fromGoogle Scholar
- AWS Local Zones. 2022. https://aws.amazon.com/cn/about-aws/global-infrastructure/localzones/locations/ Retrieved Mar 31, 2022 fromGoogle Scholar
- Zoom. 2023. https://zoom.us/Google Scholar
- Hui Zou and Trevor Hastie. 2005. Regularization and variable selection via the elastic net. Journal of the royal statistical society: series B (statistical methodology), Vol. 67, 2 (2005), 301--320.Google ScholarCross Ref
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- Demystifying the QoS and QoE of Edge-hosted Video Streaming Applications in the Wild with SNESet
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Characterizing the Relationship Between Application QoE and Network QoS for Real-Time Services
NAI'21: Proceedings of the ACM SIGCOMM 2021 Workshop on Network-Application IntegrationReal-time services require a good interplay between network and application dynamics in order to deliver a satisfactory user experience. The traditional monitoring and optimization of Quality of Service (QoS) parameters in the network lacks of knowledge ...
Measuring Video QoE from Encrypted Traffic
IMC '16: Proceedings of the 2016 Internet Measurement ConferenceTracking and maintaining satisfactory QoE for video streaming services is becoming a greater challenge for mobile network operators than ever before. Downloading and watching video content on mobile devices is currently a growing trend among users, that ...
Supporting multimedia applications through network redesign
Recent evolutions in high-performance computing and high speed broadband Internet access have paved a way to enterprise-wide multimedia applications, which require stern QoS from the underlying networks. In this paper, we have explored threefold studies ...
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