Efficient Detection of Communication-related Performance Anti-patterns in Microservices

Efficient Detection of Communication-related Performance Anti-patterns in Microservices | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 16 August 2025 V1 Latest version Share on Efficient Detection of Communication-related Performance Anti-patterns in Microservices Authors : Masoumeh Nourollahi 0009-0004-3079-7265 [email protected] , Naser Ezzati-Jivan , Adel Belkheiri , and Michel Dagenais Authors Info & Affiliations https://doi.org/10.22541/au.175533132.24109345/v1 186 views 159 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Modern microservice-based applications are inherently complex due to their distributed nature and intricate service interactions, making performance diagnosis challenging. A significant source of degradation in such systems is Software Performance Anti-patterns, which can arise from inefficient coding practices, poor architectural design, or suboptimal deployment strategies. Existing Anti-pattern detection methods often rely on costly, intrusive data collection, limiting their practicality in production environments. We propose a low-cost, non-intrusive approach for detecting communication-related Anti-patterns by combining selectively captured networking-related system call traces with distributed observability traces. This hybrid tracing enables detailed correlation between low-level communication metrics and high-level service interactions, supporting accurate detection of Anti-patterns such as Blob and Empty-semi-trucks. To minimize overhead, only essential system call events are collected, while distributed tracing data is used for root cause analysis at the service and operation level. We design a machine learning pipeline, supporting super- vised, semi-supervised, and unsupervised detection, achiev- ing up to 91% accuracy with just 2.74% data collection over- head. Offline model training further supports seamless in- tegration into Continuous Integration/Continuous Deploy- ment (CI/CD) workflows for early performance regression detection. Our approach is validated on the DeathStarBench microservice benchmark across 14 scenarios under both clean and noisy conditions. Results show high detection accuracy, effective root cause identification (over 80% match to manual analysis), and minimal runtime impact. This work offers a practical, scalable, and accurate solution for SPA detection in complex microservice environments. Keywords: software performance anti-patterns, tracing, system calls, kernel tracing, distributed tracing, semi-supervised learning Supplementary Material File (nourollahimasoumeh.pdf) Download 3.20 MB Information & Authors Information Version history V1 Version 1 16 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords distributed tracing kernel tracing semi-supervised learning software performance anti-patterns system calls tracing Authors Affiliations Masoumeh Nourollahi 0009-0004-3079-7265 [email protected] Polytechnique Montreal View all articles by this author Naser Ezzati-Jivan Brock University View all articles by this author Adel Belkheiri Polytechnique Montreal View all articles by this author Michel Dagenais Polytechnique Montreal View all articles by this author Metrics & Citations Metrics Article Usage 186 views 159 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Masoumeh Nourollahi, Naser Ezzati-Jivan, Adel Belkheiri, et al. Efficient Detection of Communication-related Performance Anti-patterns in Microservices. Authorea . 16 August 2025. DOI: https://doi.org/10.22541/au.175533132.24109345/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.175533132.24109345/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00ec70a3f17c13d',t:'MTc3OTY1MTY4Mg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();