{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:13:43Z","timestamp":1775326423966,"version":"3.50.1"},"reference-count":17,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T00:00:00Z","timestamp":1646006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Queue"],"published-print":{"date-parts":[[2022,2,28]]},"abstract":"<jats:p>Low latency is an important feature for many Google applications such as Search, and latency-analysis tools play a critical role in sustaining low latency at scale. For complex distributed systems that include services that constantly evolve in functionality and data, keeping overall latency to a minimum is a challenging task. In large, real-world distributed systems, existing tools such as RPC telemetry, CPU profiling, and distributed tracing are valuable to understand the subcomponents of the overall system, but are insufficient to perform end-to-end latency analyses in practice. Scalable and accurate fine-grain tracing has made Critical Path Tracing the standard approach for distributed latency analysis for many Google applications, including Google Search.<\/jats:p>","DOI":"10.1145\/3526967","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T22:07:59Z","timestamp":1648591679000},"page":"40-79","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Distributed Latency Profiling through Critical Path Tracing"],"prefix":"10.1145","volume":"20","author":[{"given":"Brian","family":"Eaton","sequence":"first","affiliation":[{"name":"Google"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeff","family":"Stewart","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jon","family":"Tedesco","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N. Cihan","family":"Tas","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,3,29]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Amazon Web Services. Amazon CloudWatch: Observability of your AWS resources and applications on AWS and on-premises; https:\/\/aws.amazon.com\/cloudwatch\/."},{"key":"e_1_2_1_2_1","unstructured":"Amazon Web Services. What is Amazon CodeGuru profiler?; https:\/\/docs.aws.amazon.com\/codeguru\/latest\/profiler-ug\/what-is-codeguru-profiler.html."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/98457.98518"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2600428.2609627"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the 11th Usenix Symposium on Operating Systems Design and Implementation, 217-231; https:\/\/dl.acm.org\/doi\/10","author":"Chow M.","year":"2014","unstructured":"Chow, M., Meisner, D., Flinn, J., Peek, D., Wenisch, T.F. 2014. The Mystery Machine: End-to-end performance analysis of large-scale Internet services. In Proceedings of the 11th Usenix Symposium on Operating Systems Design and Implementation, 217-231; https:\/\/dl.acm.org\/doi\/10.5555\/2685048.2685066."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2815400.2815409"},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Dean J. Barroso L.A. 2013. The tail at scale. Communications of the ACM 56(2) 74?80; https:\/\/dl.acm.org\/doi\/10.1145\/2408776.2408794.","DOI":"10.1145\/2408776.2408794"},{"key":"e_1_2_1_8_1","unstructured":"GitHub. pprof; https:\/\/github.com\/google\/pprof."},{"key":"e_1_2_1_9_1","unstructured":"Google. Cloud monitoring; https:\/\/cloud.google.com\/monitoring."},{"key":"e_1_2_1_10_1","unstructured":"Google. Cloud Profiler; https:\/\/cloud.google.com\/profiler."},{"key":"e_1_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Gregg B. 2016. The flame graph. Communications of the ACM 59(6) 48?57; https:\/\/dl.acm.org\/doi\/10.1145\/2909476.","DOI":"10.1145\/2909476"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Kelley. J.E. 1961. Critical path planning and scheduling: Mathematical basis. Operations Research 9(3) 296?435; https:\/\/www.jstor.org\/stable\/167563.","DOI":"10.1287\/opre.9.3.296"},{"key":"e_1_2_1_13_1","unstructured":"Microsoft. Profile production applications in Azure with application insights; https:\/\/docs.microsoft.com\/en-us\/azure\/azure-monitor\/app\/profiler-overview."},{"key":"e_1_2_1_14_1","unstructured":"Microsoft. Azure Monitor; https:\/\/azure.microsoft.com\/en-au\/services\/monitor\/."},{"key":"e_1_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Nokleberg C. Hawkes B. 2021. Best practice: Application frameworks. acmqueue 18(6) 52?77; https:\/\/queue.acm.org\/detail.cfm?id=3447806.","DOI":"10.1145\/3442632.3447806"},{"key":"e_1_2_1_16_1","volume-title":"Building Netflix's distributed tracing infrastructure. The Netflix Tech Blog","author":"Pandey M.","year":"1930","unstructured":"Pandey, M., Lew, K., Arunachalam, N., Carretto, E., Haffner, D., Ushakov, A., Katz, S., Burrell, G., Vaithilingam, R., Smith, M. 2020. Building Netflix's distributed tracing infrastructure. The Netflix Tech Blog; https:\/\/netflixtechblog.com\/building-netflixs-distributed-tracing-infrastructure-bb856c319304."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/DCS.1988.12538"}],"container-title":["Queue"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3526967","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3526967","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:17Z","timestamp":1750186937000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3526967"}},"subtitle":["CPT can provide actionable and precise latency analysis."],"short-title":[],"issued":{"date-parts":[[2022,2,28]]},"references-count":17,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,2,28]]}},"alternative-id":["10.1145\/3526967"],"URL":"https:\/\/doi.org\/10.1145\/3526967","relation":{},"ISSN":["1542-7730","1542-7749"],"issn-type":[{"value":"1542-7730","type":"print"},{"value":"1542-7749","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,28]]},"assertion":[{"value":"2022-03-29","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}