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Author

Ahmet Kaplan

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Jul 2026

Safety-Gated Autoscaling: A Multi-Layered Defense Architecture for Kubernetes Vertical Resource Optimization

Kubernetes is the standard platform for orchestrating containerized applications, yet resource management remains difficult. To stay safe, engineers over-provision CPU and memory, leaving reserved but unused capacity that is the main source of wasted cost. The built-in Horizontal and Vertical Pod Autoscalers are reactive: they act only after a threshold is crossed, which causes lag, over-provisioning, and can mask software defects by granting a leaking workload more memory. Predictive autoscalers focus on improving forecasting accuracy or run inside proprietary infrastructure, and anomaly detection is used only to alert, never to block a harmful action. The Intelligent Cluster Optimizer is an open-source Kubernetes operator that right-sizes container workloads with safety as a first-class concern. Its central contribution is a five-layer safety pipeline where a memory-leak detector, based on linear regression with R^2 scoring, acts as a blocking gate: if a leak is detected the recommendation is rejected, so the optimizer never hides a bug by enlarging a broken container. The pipeline combines SLA monitoring, a circuit breaker, HPA/PDB conflict detection, and a policy engine, with rollback and dry-run mode for human approval. Recommendations are produced by percentile analysis and Holt-Winters forecasting, balanced through multi-objective Pareto optimization at the per-container level. We validated the system with 1118 automated tests at 80.3% coverage and a live deployment on Google Kubernetes Engine, where right-sizing produced estimated cost savings of 20--40% in what-if projections and the leak gate reached 83% detection accuracy.

Azra Karakaya, Erva Şengül, Ahmet Kaplan · 0 citations
Conference Jul 2026

Bearing Fault Diagnosis under Small-Sample Conditions Based on Unsupervised Representation Learning

Early detection of bearing faults in rotating machinery is essential for predictive maintenance. Although deep learning-based methods have achieved strong results in fault diagnosis, they usually require large amounts of labeled data. In industrial settings, however, faulty samples are limited, which restricts the applicability of fully supervised approaches. In this study, a bearing fault diagnosis framework based on unsupervised representation learning is proposed for limited-label scenarios. Firstly, a convolutional autoencoder is trained on raw vibration signals without using labels to learn informative latent representations. After that, these learned representations are classified using only a limited number of labeled samples. The proposed method is evaluated on the CWRU bearing dataset under same-load and cross-load settings with both single and dual-channel inputs. Experimental results show that the proposed framework achieves strong performance under low-label conditions and that the dual-channel setup further improves classification performance.

Ahmet Kaplan, Kürşat İnce, Murat Beken · 0 citations

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