Scipediacontent: Scipediacontent moved page Review 790119736269 to Veeramani et al 2025b

2025-12-16T10:05:04Z

Scipediacontent moved page Review 790119736269 to Veeramani et al 2025b

JSanchez: JSanchez moved page Draft Sanchez Pinedo 731450279 to Review 790119736269

2025-12-16T09:28:52Z

JSanchez moved page Draft Sanchez Pinedo 731450279 to Review 790119736269

JSanchez: Created page with " == Abstract ==
Efficient management of workloads in diverse container clusters requires maintaining a balance between Service Level Agreement (SLA) compliance, Quality of..."

2025-12-16T09:28:50Z

Created page with " == Abstract == <p>Efficient management of workloads in diverse container clusters requires maintaining a balance between Service Level Agreement (SLA) compliance, Quality of..."

New page

== Abstract ==

<p>Efficient management of workloads in diverse container clusters requires maintaining a balance between Service Level Agreement (SLA) compliance, Quality of Service (QoS), energy efficiency, and security, despite differences in resources and architectures. This research introduces Federated Q-Learning–based Lightweight Task Orchestrator (F-Q-LiTO), a compact and intelligent orchestration framework that combines predictive modeling, approximate data structures, and security filtering to enable adaptive task placement across distributed environments. Unlike complex deep reinforcement learning models such as Deep Reinforcement Model (DeepRM) or traditional heuristic schedulers like Kubernetes BinPacking, F-Q-LiTO uses tabular Q-learning enhanced with federated aggregation, which significantly reduces computational and communication overhead, making it ideal for edge computing environments with limited resources. The framework incorporates a Long Short-Term Memory (LSTM)–based predictor for proactive resource forecasting, a Count-Min Sketch for scalable resource utilization estimation, and an XOR filter for efficient and lightweight security enforcement. Experimental results demonstrate that F-Q-LiTO achieves 98.6% task completion, 96.8% SLA satisfaction, and reduces energy consumption to 180.5 kilowatt-hours (kWh). It outperforms DeepRM and Kubernetes by achieving 34% fewer missed deadlines and up to 30% lower energy imbalance. The system converges quickly—by Episode 6—and maintains cluster fairness (Jain’s Fairness Index= 0.98) along with priority-aware placement accuracy of 93.2%. Security analysis shows that F-Q-LiTO successfully blocks 98.5% of unauthorized task placements while using only 0.3 megabytes (MB) of memory. Overall, F-Q-LiTO demonstrates that a federated and lightweight reinforcement learning approach can deliver scalable, secure, and QoS-aware orchestration for modern edge and multi-cloud computing environments without compromising performance or efficiency.OPEN ACCESS Received: 24/07/2025 Accepted: 14/10/2025 Published: 15/12/2025</p>

== Document ==
<pdf>Media:Draft_Sanchez Pinedo_731450279-2739-document.pdf</pdf>

← Older revision	Revision as of 10:05, 16 December 2025
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Veeramani et al 2025b - Revision history

Scipediacontent: Scipediacontent moved page Review 790119736269 to Veeramani et al 2025b

JSanchez: JSanchez moved page Draft Sanchez Pinedo 731450279 to Review 790119736269

JSanchez: Created page with " == Abstract == Efficient management of workloads in diverse container clusters requires maintaining a balance between Service Level Agreement (SLA) compliance, Quality of..."

JSanchez: Created page with " == Abstract ==
Efficient management of workloads in diverse container clusters requires maintaining a balance between Service Level Agreement (SLA) compliance, Quality of..."