{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T23:24:21Z","timestamp":1740180261018,"version":"3.37.3"},"reference-count":31,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Meas. Anal. Comput. Syst."],"published-print":{"date-parts":[[2023,12,7]]},"abstract":"When verifying that a communications network fulfills its specified performance, it is critical to note sudden shifts in network behavior as quickly as possible. Change point detection methods can be useful in this endeavor, but classical methods rely on measuring with a fixed measurement period, which can often be suboptimal in terms of measurement costs. In this paper, we extend the existing framework of change point detection with a notion of physical time. Instead of merely deciding when to stop, agents must now also decide at which future time to take the next measurement. Agents must now minimize the necessary number of measurements pre- and post-change, while maintaining a trade-off between post-change delay and false alarm rate. We establish, through this framework, the suboptimality of typical periodic measurements and propose a simple alternative, called crisis mode agents. We show analytically that crisis mode agents significantly outperform periodic measurements schemes. We further verify this in numerical evaluation, both on an array of synthetic change point detection problems as well as on the problem of detecting traffic load changes in a 5G test bed through end-to-end RTT measurements.<\/jats:p>","DOI":"10.1145\/3626784","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T20:20:29Z","timestamp":1702412429000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Change Point Detection with Adaptive Measurement Schedules for Network Performance Verification"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6183-8996","authenticated-orcid":false,"given":"Simon","family":"Lindst\u00e5hl","sequence":"first","affiliation":[{"name":"Ericsson Research & KTH Royal Institute of Technology, Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4679-4673","authenticated-orcid":false,"given":"Alexandre","family":"Proutiere","sequence":"additional","affiliation":[{"name":"KTH Royal Institute of Technology, Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3743-9431","authenticated-orcid":false,"given":"Andreas","family":"Johnsson","sequence":"additional","affiliation":[{"name":"Ericsson Research & Uppsala University, Stockholm, Sweden"}]}],"member":"320","published-online":{"date-parts":[[2023,12,12]]},"reference":[{"volume-title":"3rd Generation Partnership Project (3GPP). Version 17.4.0.","author":"GPP.","key":"e_1_2_1_1_1","unstructured":"3GPP. 2020. Service requirements for cyber-physical control applications in vertical domains. Technical Specification (TS). 3rd Generation Partnership Project (3GPP). Version 17.4.0."},{"key":"e_1_2_1_2_1","first-page":"213","article-title":"NR; Physical layer procedures for control","volume":"38","author":"GPP.","year":"2021","unstructured":"3GPP. 2021. NR; Physical layer procedures for control. Technical Specification (TS) 38.213. 3rd Generation Partnership Project (3GPP). Version 16.5.0.","journal-title":"Technical Specification (TS)"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11030412"},{"key":"e_1_2_1_4_1","first-page":"3425","article-title":"Locally private online change point detection","volume":"34","author":"Berrett Tom","year":"2021","unstructured":"Tom Berrett and Yi Yu. 2021. Locally private online change point detection. Advances in Neural Information Processing Systems , Vol. 34 (2021), 3425--3437.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3466167"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729489"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/VR46266.2020.00103"},{"key":"e_1_2_1_8_1","volume-title":"Tactile-internet-based telesurgery system for healthcare 4.0: An architecture, research challenges, and future directions","author":"Gupta Rajesh","year":"2019","unstructured":"Rajesh Gupta, Sudeep Tanwar, Sudhanshu Tyagi, and Neeraj Kumar. 2019. Tactile-internet-based telesurgery system for healthcare 4.0: An architecture, research challenges, and future directions. IEEE network, Vol. 33, 6 (2019), 22--29."},{"key":"e_1_2_1_9_1","volume-title":"Workshop on Profile and Feedback-Directed Compilation (PFDC). Citeseer","author":"Hsu Chung-Hsing","year":"1998","unstructured":"Chung-Hsing Hsu and Ulrich Kremer. 1998. IPERF: A framework for automatic construction of performance prediction models. In Workshop on Profile and Feedback-Directed Compilation (PFDC). Citeseer, Paris, France, 1--10."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/18.737522"},{"key":"e_1_2_1_11_1","volume-title":"Sequential change-point detection when the pre-and post-change parameters are unknown. Sequential analysis","author":"Lai Tze Leung","year":"2010","unstructured":"Tze Leung Lai and Haipeng Xing. 2010. Sequential change-point detection when the pre-and post-change parameters are unknown. Sequential analysis, Vol. 29, 2 (2010), 162--175."},{"volume-title":"Spatial and syndromic surveillance for public health","author":"Lawson Andrew B","key":"e_1_2_1_12_1","unstructured":"Andrew B Lawson and Ken Kleinman. 2005. Spatial and syndromic surveillance for public health. John Wiley & Sons, Hoboken, NJ, USA."},{"key":"e_1_2_1_13_1","volume-title":"Procedures for reacting to a change in distribution. The annals of mathematical statistics","author":"Lorden Gary","year":"1971","unstructured":"Gary Lorden. 1971. Procedures for reacting to a change in distribution. The annals of mathematical statistics , Vol. 42, 6 (1971), 1897--1908."},{"volume-title":"Algorithmic Learning Theory","author":"Maillard Odalric-Ambrym","key":"e_1_2_1_14_1","unstructured":"Odalric-Ambrym Maillard. 2019. Sequential change-point detection: Laplace concentration of scan statistics and non-asymptotic delay bounds. In Algorithmic Learning Theory. PMLR, Chicago, IL, USA, 610--632."},{"key":"e_1_2_1_16_1","volume-title":"Optimal stopping times for detecting changes in distributions. the Annals of Statistics","author":"Moustakides George V","year":"1986","unstructured":"George V Moustakides. 1986. Optimal stopping times for detecting changes in distributions. the Annals of Statistics , Vol. 14, 4 (1986), 1379--1387."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/41.1-2.100"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176346587"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2534169.2486017"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3538394.3546039"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1966.10490374"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1137\/1108002"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2010.5684137"},{"key":"e_1_2_1_25_1","volume-title":"Rudolf B Blavz ek, and Hongjoong Kim","author":"Tartakovsky Alexander G","year":"2006","unstructured":"Alexander G Tartakovsky, Boris L Rozovskii, Rudolf B Blavz ek, and Hongjoong Kim. 2006. Detection of intrusions in information systems by sequential change-point methods. Statistical methodology , Vol. 3, 3 (2006), 252--293."},{"key":"e_1_2_1_26_1","first-page":"339","article-title":"Change-point detection in multichannel and distributed systems","volume":"173","author":"Tartakovsky Alexander G","year":"2004","unstructured":"Alexander G Tartakovsky and Venugopal V Veeravalli. 2004. Change-point detection in multichannel and distributed systems. Applied Sequential Methodologies: Real-World Examples with Data Analysis , Vol. 173 (2004), 339--370.","journal-title":"Applied Sequential Methodologies: Real-World Examples with Data Analysis"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1080\/07474940802446236"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/NOMS.2014.6838228"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177731118"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.3390\/sym10120713"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2021.3072962"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/PIMRC.2019.8904232"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097895.3097900"}],"container-title":["Proceedings of the ACM on Measurement and Analysis of Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3626784","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T14:37:30Z","timestamp":1708353450000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626784"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,7]]},"references-count":31,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,12,7]]}},"alternative-id":["10.1145\/3626784"],"URL":"https:\/\/doi.org\/10.1145\/3626784","relation":{},"ISSN":["2476-1249"],"issn-type":[{"type":"electronic","value":"2476-1249"}],"subject":[],"published":{"date-parts":[[2023,12,7]]},"assertion":[{"value":"2023-12-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}