{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T14:58:57Z","timestamp":1784300337012,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100006374","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CNS-2211381,CNS-2211384,CAREER-2338034"],"award-info":[{"award-number":["CNS-2211381,CNS-2211384,CAREER-2338034"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,11,4]]},"DOI":"10.1145\/3704742.3704964","type":"proceedings-article","created":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T18:18:57Z","timestamp":1735323537000},"page":"17-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Caravan: Practical Online Learning of In-Network ML Models with Labeling Agents"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3208-4601","authenticated-orcid":false,"given":"Qizheng","family":"Zhang","sequence":"first","affiliation":[{"name":"Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7194-2357","authenticated-orcid":false,"given":"Ali","family":"Imran","sequence":"additional","affiliation":[{"name":"Purdue University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3099-5374","authenticated-orcid":false,"given":"Enkeleda","family":"Bardhi","sequence":"additional","affiliation":[{"name":"Sapienza University of Rome"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2975-6042","authenticated-orcid":false,"given":"Tushar","family":"Swamy","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9668-902X","authenticated-orcid":false,"given":"Nathan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5168-9045","authenticated-orcid":false,"given":"Muhammad","family":"Shahbaz","sequence":"additional","affiliation":[{"name":"Purdue University and University of Michigan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8779-0636","authenticated-orcid":false,"given":"Kunle","family":"Olukotun","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,12,27]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"AI and ML: The New Frontier for Data Center Innovation and Optimization. https:\/\/www.techradar.com\/news\/data-centres-in-an-ai-and-ml-driven-future."},{"key":"e_1_3_2_1_2_1","volume-title":"https:\/\/github.com\/Per-Packet-AI\/Caravan-Artifact-OSDI24","author":"Artifact","unstructured":"Artifact for Caravan (OSDI 24). https:\/\/github.com\/Per-Packet-AI\/Caravan-Artifact-OSDI24."},{"key":"e_1_3_2_1_3_1","unstructured":"CISCO NetFlow. https:\/\/www.cisco.com\/c\/en\/us\/tech\/quality-of-service-qos\/netflow\/index.html."},{"key":"e_1_3_2_1_4_1","unstructured":"Creating a Predictive Network for the Human Mind. https:\/\/newsroom.cisco.com\/c\/r\/newsroom\/en\/us\/a\/y2022\/m05\/creating-a-predictive-network-for-the-human-mind.html."},{"key":"e_1_3_2_1_5_1","unstructured":"Data Centers in an AI and ML Driven Future. https:\/\/www.techradar.com\/news\/data-centres-in-an-ai-and-ml-driven-future."},{"key":"e_1_3_2_1_6_1","unstructured":"Every Request Every Microsecond: Scalable Machine Learning at Cloudflare. https:\/\/blog.cloudflare.com\/scalable-machine-learning-at-cloudflare\/."},{"key":"e_1_3_2_1_7_1","unstructured":"Intel Tofino 2. https:\/\/www.intel.com\/content\/www\/us\/en\/products\/details\/network-io\/intelligent-fabric-processors\/tofino-2.html."},{"key":"e_1_3_2_1_8_1","unstructured":"Introducing Meta Llama 3: The Most Capable Openly Available LLM to Date. https:\/\/ai.meta.com\/blog\/meta-llama-3\/."},{"key":"e_1_3_2_1_9_1","unstructured":"Marvell OCTEON 10 DPU Platform. https:\/\/www.marvell.com\/content\/dam\/marvell\/en\/public-collateral\/embedded-processors\/marvell-octeon-10-dpu-platform-product-brief.pdfe\/."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/967900.967989"},{"key":"e_1_3_2_1_11_1","volume-title":"ICML","author":"Arora Simran","year":"2022","unstructured":"Simran Arora, Avanika Narayan, Mayee F Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, and Christopher Re. Ask Me Anything: A Simple Strategy for Prompting Language Models. In ICML, 2022."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24067"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1879141.1879175"},{"key":"e_1_3_2_1_14_1","first-page":"119","volume-title":"USENIX NSDI","author":"Bhardwaj Romil","year":"2022","unstructured":"Romil Bhardwaj, Zhengxu Xia, Ganesh Ananthanarayanan, Junchen Jiang, Yuanchao Shu, Nikolaos Karianakis, Kevin Hsieh, Paramvir Bahl, and Ion Stoica. Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers. In USENIX NSDI, pages 119--135, 2022."},{"key":"e_1_3_2_1_15_1","volume-title":"Applications and Research Opportunities. Journal of Internet Services and Applications","author":"Boutaba Raouf","year":"2018","unstructured":"Raouf Boutaba, Mohammad A. Salahuddin, Noura Limam, Sara Ayoubi, Nashid Shahriar, Felipe Estrada-Solano, and Oscar M. Caicedo. A Comprehensive Survey on Machine Learning for Networking: Evolution, Applications and Research Opportunities. Journal of Internet Services and Applications, 2018."},{"key":"e_1_3_2_1_16_1","volume-title":"pForest: In-Network Inference with Random Forests. arXiv preprint arXiv:1909.05680","author":"Busse-Grawitz Coralie","year":"2019","unstructured":"Coralie Busse-Grawitz, Roland Meier, Alexander Dietm\u00fcller, Tobias B\u00fchler, and Laurent Vanbever. pForest: In-Network Inference with Random Forests. arXiv preprint arXiv:1909.05680, 2019."},{"key":"e_1_3_2_1_17_1","volume-title":"Computer Networks","author":"Este Alice","year":"2009","unstructured":"Alice Este, Francesco Gringoli, and Luca Salgarelli. Support Vector Machines for TCP Traffic Classification. Computer Networks, 2009."},{"key":"e_1_3_2_1_18_1","volume-title":"Inference and Measurement in Data Center Networks. In USENIX NSDI","author":"Geng Yilong","year":"2019","unstructured":"Yilong Geng, Shiyu Liu, Zi Yin, Ashish Naik, Balaji Prabhakar, Mendel Rosenblum, and Amin Vahdat. SIMON: A Simple and Scalable Method for Sensing, Inference and Measurement in Data Center Networks. In USENIX NSDI, 2019."},{"key":"e_1_3_2_1_19_1","volume-title":"netFound: Foundation Model for Network Security. arXiv preprint arXiv:2310.17025","author":"Guthula Satyandra","year":"2023","unstructured":"Satyandra Guthula, Navya Battula, Roman Beltiukov, Wenbo Guo, and Arpit Gupta. netFound: Foundation Model for Network Security. arXiv preprint arXiv:2310.17025, 2023."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.01.012"},{"key":"e_1_3_2_1_21_1","volume-title":"ACM SIGCOMM","author":"Kim Changhoon","year":"2015","unstructured":"Changhoon Kim, Anirudh Sivaraman, Naga Katta, Antonin Bas, Advait Dixit, and Lawrence J Wobker. In-Band Network Telemetry via Programmable Dataplanes. In ACM SIGCOMM, 2015."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3563766.3564109"},{"key":"e_1_3_2_1_23_1","volume-title":"NeurIPS","author":"Lewis Patrick","year":"2020","unstructured":"Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich K\u00fcttler, Mike Lewis, Wen-tau Yih, Tim Rockt\u00e4schel, Sebastian Riedel, and Douwe Kiela. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. In NeurIPS, 2020."},{"key":"e_1_3_2_1_24_1","volume-title":"ServeFlow: A Fast-Slow Model Architecture for Network Traffic Analysis. arXiv preprint arXiv:2402.03694","author":"Liu Shinan","year":"2024","unstructured":"Shinan Liu, Ted Shaowang, Gerry Wan, Jeewon Chae, Jonatas Marques, Sanjay Krishnan, and Nick Feamster. ServeFlow: A Fast-Slow Model Architecture for Network Traffic Analysis. arXiv preprint arXiv:2402.03694, 2024."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/IMSCCS.2007.52"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3005745.3005750"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3098822.3098843"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISMSC.2015.7594039"},{"key":"e_1_3_2_1_29_1","unstructured":"OpenAI. GPT-4 Technical Report. arXiv preprint arXiv:2303.08774 2023."},{"key":"e_1_3_2_1_30_1","volume-title":"Language Models as Knowledge Bases? arXiv preprint arXiv:1909.01066","author":"Petroni Fabio","year":"2019","unstructured":"Fabio Petroni, Tim Rockt\u00e4schel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel. Language Models as Knowledge Bases? arXiv preprint arXiv:1909.01066, 2019."},{"key":"e_1_3_2_1_31_1","first-page":"1","volume-title":"Christopher R\u00e9. Snorkel Metal: Weak Supervision for Multi-Task Learning. In Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning","author":"Ratner Alex","year":"2018","unstructured":"Alex Ratner, Braden Hancock, Jared Dunnmon, Roger Goldman, and Christopher R\u00e9. Snorkel Metal: Weak Supervision for Multi-Task Learning. In Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning, pages 1--4, 2018."},{"issue":"2","key":"e_1_3_2_1_32_1","first-page":"709","article-title":"Snorkel","volume":"29","author":"Ratner Alexander","year":"2020","unstructured":"Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher R\u00e9. Snorkel: Rapid Training Data Creation with Weak Supervision. The VLDB Journal, 29(2-3):709--730, 2020.","journal-title":"Rapid Training Data Creation with Weak Supervision. The VLDB Journal"},{"key":"e_1_3_2_1_33_1","volume-title":"Quickly. In NeurIPS","author":"Ratner Alexander J","year":"2016","unstructured":"Alexander J Ratner, Christopher M De Sa, Sen Wu, Daniel Selsam, and Christopher R\u00e9. Data Programming: Creating Large Training Sets, Quickly. In NeurIPS, 2016."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"e_1_3_2_1_35_1","volume-title":"How Much Knowledge Can You Pack into the Parameters of a Language Model? arXiv preprint arXiv:2002.08910","author":"Roberts Adam","year":"2020","unstructured":"Adam Roberts, Colin Raffel, and Noam Shazeer. How Much Knowledge Can You Pack into the Parameters of a Language Model? arXiv preprint arXiv:2002.08910, 2020."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2785956.2787472"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3229591.3229594"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.368"},{"key":"e_1_3_2_1_39_1","volume-title":"USENIX NSDI","author":"Siracusano Giuseppe","year":"2022","unstructured":"Giuseppe Siracusano, Salvator Galea, Davide Sanvito, Mohammad Malekzadeh, Gianni Antichi, Paolo Costa, Hamed Haddadi, and Roberto Bifulco. Re-Architecting Traffic Analysis with Neural Network Interface Cards. In USENIX NSDI, 2022."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2014.2365795"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503222.3507726"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/WINCOM.2016.7777224"},{"key":"e_1_3_2_1_43_1","volume-title":"Gemini: A Family of Highly Capable Multimodal Models. arXiv preprint arXiv:2312.11805","author":"Team Gemini","year":"2024","unstructured":"Gemini Team. Gemini: A Family of Highly Capable Multimodal Models. arXiv preprint arXiv:2312.11805, 2024."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/2486001.2486020"},{"key":"e_1_3_2_1_45_1","volume-title":"NetLLM: Adapting Large Language Models for Networking. arXiv preprint arXiv:2402.02338","author":"Wu Duo","year":"2024","unstructured":"Duo Wu, Xianda Wang, Yaqi Qiao, Zhi Wang, Junchen Jiang, Shuguang Cui, and Fangxin Wang. NetLLM: Adapting Large Language Models for Networking. arXiv preprint arXiv:2402.02338, 2024."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-017-0466-8"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3365609.3365864"},{"key":"e_1_3_2_1_48_1","volume-title":"USENIX NSDI","author":"Yan Francis Y.","year":"2020","unstructured":"Francis Y. Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong, Keyi Zhang, Philip Alexander Levis, and Keith Winstein. Learning in situ: A Randomized Experiment in Video Streaming. In USENIX NSDI, 2020."},{"key":"e_1_3_2_1_49_1","volume-title":"Philip Alexander Levis, and Keith Winstein","author":"Yan Francis Y.","unstructured":"Francis Y. Yan, Jestin Ma, Greg D. Hill, Deepti Raghavan, Riad S. Wahby, Philip Alexander Levis, and Keith Winstein. Pantheon: The Training Ground for Internet Congestion-Control Research. In USENIX ATC, 2018."},{"key":"e_1_3_2_1_50_1","volume-title":"USENIX Security","author":"Yang Limin","year":"2021","unstructured":"Limin Yang, Wenbo Guo, Qingying Hao, Arridhana Ciptadi, Ali Ahmadzadeh, Xinyu Xing, and Gang Wang. CADE: Detecting and Explaining Concept Drift Samples for Security Applications. In USENIX Security, 2021."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3387514.3405870"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3131365.3131375"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3704742.3704964"},{"key":"e_1_3_2_1_54_1","volume-title":"IIsy: Hybrid In-Network Classification Using Programmable Switches","author":"Zheng Changgang","year":"2024","unstructured":"Changgang Zheng, Zhaoqi Xiong, Thanh T Bui, Siim Kaupmees, Riyad Bensoussane, Antoine Bernabeu, Shay Vargaftik, Yaniv Ben-Itzhak, and Noa Zilberman. IIsy: Hybrid In-Network Classification Using Programmable Switches. IEEE\/ACM Transactions on Networking, 2024."}],"event":{"name":"SOSP '24: ACM SIGOPS 30th Symposium on Operating Systems Principles","location":"Austin TX USA","acronym":"SOSP '24","sponsor":["SIGOPS ACM Special Interest Group on Operating Systems"]},"container-title":["Proceedings of the 3rd Workshop on Practical Adoption Challenges of ML for Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3704742.3704964","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3704742.3704964","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T20:34:42Z","timestamp":1755981282000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3704742.3704964"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,4]]},"references-count":54,"alternative-id":["10.1145\/3704742.3704964","10.1145\/3704742"],"URL":"https:\/\/doi.org\/10.1145\/3704742.3704964","relation":{},"subject":[],"published":{"date-parts":[[2024,11,4]]},"assertion":[{"value":"2024-12-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}