{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T23:09:32Z","timestamp":1779145772736,"version":"3.51.4"},"reference-count":23,"publisher":"Optica Publishing Group","issue":"6","license":[{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/doi.org\/10.1364\/OA_License_v2#VOR"},{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/opg.optica.org\/policies\/opg-tdm-policy.json"}],"content-domain":{"domain":["opg.optica.org"],"crossmark-restriction":false},"short-container-title":["J. Opt. Commun. Netw."],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:p>\n                    Reconfigurable optical data-center networks (DCNs) often suffer from a fundamental timescale mismatch: while the baseline topology is planned over coarse epochs (e.g., minutes), practical workloads generate bursty, skewed demands that cause severe tail congestion within seconds. To bridge this gap, we propose DTPE, a distributed topology perturbation engine that operates continuously alongside the baseline. Instead of triggering global reconfigurations, DTPE empowers rack-level agents to negotiate local, degree-budget-aware circuit trades using a small amount of reserved port slack. This allows the network to absorb emerging hotspots through rapid, adjacency-preserving perturbations without involving the central controller. Extensive simulations across varying traffic patterns and planning algorithms (greedy, demand-gravity, and Couder) demonstrate that DTPE effectively suppresses the maximum-link-utilization (MLU) tail. Specifically, it reduces the overload probability by orders of magnitude (e.g., from \u223c12.2% to below 1.0% for\n                    <jats:italic>N<\/jats:italic>\n                    =32) while scaling robustly under realistic degree constraints. Finally, we validate the physical feasibility of the proposed scheme via a trace-driven replay on a four-rack OCS testbed, confirming that such fine-grained reconfiguration actions can be executed on real hardware to mitigate congestion delays.\n                  <\/jats:p>","DOI":"10.1364\/jocn.590053","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T18:00:09Z","timestamp":1776103209000},"page":"581","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DTPE: a distributed topology perturbation engine for tail-risk mitigation in degree-constrained optical DCNs"],"prefix":"10.1364","volume":"18","author":[{"given":"Xinyue","family":"Gu","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04d996474","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9484-7752","authenticated-orcid":true,"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04d996474","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4251-788X","authenticated-orcid":true,"given":"Zuqing","family":"Zhu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"285","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"jocn-18-6-581-R1","doi-asserted-by":"crossref","first-page":"OW3J.3","DOI":"10.1364\/OFC.2012.OW3J.3","article-title":"Optically interconnected data center networks","volume-title":"Optical Fiber Communication Conference (OFC)","author":"Xu","year":"2012"},{"key":"jocn-18-6-581-R2","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1109\/TNET.2013.2253120","type":"journal-article","volume":"22","author":"Chen","year":"2014","journal-title":"IEEE\/ACM Trans. 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All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}