http://www.colloquiam.com/wd/index.php?action=history&feed=atom&title=Veeramani_et_al_2025a 2026-05-10T23:03:43Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 http://www.colloquiam.com/wd/index.php?title=Veeramani_et_al_2025a&diff=327834&oldid=prev 2025-11-27T10:08:47Z

<p>Scipediacontent moved page <a href="/public/Review_540979533270" class="mw-redirect" title="Review 540979533270">Review 540979533270</a> to <a href="/public/Veeramani_et_al_2025a" title="Veeramani et al 2025a">Veeramani et al 2025a</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 10:08, 27 November 2025</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table>

Scipediacontent http://www.colloquiam.com/wd/index.php?title=Veeramani_et_al_2025a&diff=327808&oldid=prev 2025-11-27T08:57:04Z

<p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_245648300" class="mw-redirect" title="Draft Sanchez Pinedo 245648300">Draft Sanchez Pinedo 245648300</a> to <a href="/public/Review_540979533270" class="mw-redirect" title="Review 540979533270">Review 540979533270</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 08:57, 27 November 2025</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table>

JSanchez http://www.colloquiam.com/wd/index.php?title=Veeramani_et_al_2025a&diff=327807&oldid=prev 2025-11-27T08:56:30Z

<p>Created page with &quot; == Abstract == &lt;p&gt;The demand for intelligent, scalable, and energy-conscious container orchestration has increased due to the growth of microservice-based designs and multi-...&quot;</p> <p><b>New page</b></p><div><br /> == Abstract ==<br /> <br /> &lt;p&gt;The demand for intelligent, scalable, and energy-conscious container orchestration has increased due to the growth of microservice-based designs and multi-tenant workloads. A novel federated reinforcement learning framework for adaptive task scheduling in heterogeneous container clusters, F-Q-LiTO (Federated Q-Learning-Based Lightweight Intelligent Task Orchestrator), is proposed in this research. In contrast to traditional orchestrators, F-Q-LiTO uses federated Q-learning to decentralise decision-making, guaranteeing convergence across dispersed nodes while maintaining data locality and minimising synchronisation overhead. The system has several lightweight components, including energy-conscious placement penalties, XOR filters for secure container fingerprinting, Count-Min Sketches (CMS) for constant-space resource estimation, and workload forecasting based on the Long Short-Term Model (LSTM) for proactive migration. In comparison to DeepPlace, F-Q-LiTO reduced task deadline misses by around 34% and achieved an average SLA satisfaction of 96.8% when tested on simulated multitenant workloads with over 1000 tasks. Ablation studies confirm that federated coordination and predictive migration materially improve performance. Global Q-values converged within six episodes, and SHAPbased explanations identify CPU forecast, SLA urgency, and node energy state as dominant decision factors. F-Q-LiTO demonstrates practical, interpretable, and low-latency orchestration suitable for dynamic edge&amp;ndash; cloud deployments.OPEN ACCESS Received: 01/08/2025 Accepted: 09/10/2025 Published: 27/11/2025&lt;/p&gt;<br /> <br /> <br /> <br /> == Document ==<br /> &lt;pdf&gt;Media:Draft_Sanchez Pinedo_245648300-2101-document.pdf&lt;/pdf&gt;</div>

JSanchez