arXiv subjects

Haci Ismail Aslan

Publications and source records attributed to Haci Ismail Aslan.

3 recordsLinked to original sources

arXiv2026

Optimizing Memory Allocation in Distributed Clusters with Predictive Modeling

In modern distributed systems, efficient resource allocation is a vital aspect to maintain scalability, reduce operational costs, and ensure fast execution even across heterogeneous workloads. Predictive models for resource usage are essential tools for optimizing allocation and preventing system bottlenecks. Predictive memory allocation has asymmetric costs as a key challenge: underallocation causes failures while overallocation wastes memory. We propose a regression method based on a LightGBM and XGBoost ensemble trained to predict high conditional quantiles. To further account for the high cost of underallocations we add a multiplicative safety factor. With our method we are able to reduce the number of under-allocated jobs from 4.17% to 2.89% and average overallocation from 148% to 44.51% on a real-world dataset of build jobs provided by SAP. We further explore the pareto frontier between optimization for underallocation and for overallocation.

cs.DC↗

arXiv2025

QONNECT: A QoS-Aware Orchestration System for Distributed Kubernetes Clusters

Modern applications increasingly span across cloud, fog, and edge environments, demanding orchestration systems that can adapt to diverse deployment contexts while meeting Quality-of-Service (QoS) requirements. Standard Kubernetes schedulers do not account for user-defined objectives such as energy efficiency, cost optimization, and global performance, often leaving operators to make manual, cluster-by-cluster placement decisions. To address this need, we present QONNECT, a vendor-agnostic orchestration framework that enables declarative, QoS-driven application deployment across heterogeneous Kubernetes and K3s clusters. QONNECT introduces a distributed architecture composed of a central Knowledge Base, Raft-replicated Resource Lead Agents, and lightweight Resource Agents in each cluster. Through a minimal YAML-based interface, users specify high-level QoS goals, which the system translates into concrete placement and migration actions. Our implementation is evaluated on a federated testbed of up to nine cloud-fog-edge clusters using the Istio Bookinfo microservice application. The system demonstrates dynamic, policy-driven microservice placement, automated failover, QoS-compliant rescheduling, and leader re-election after node failure, all without manual intervention. By bridging the gap between declarative deployment models and operational QoS goals, QONNECT transforms the cloud-edge continuum into a unified, self-optimizing platform.

cs.DC↗

arXiv2025

$β$-GNN: A Robust Ensemble Approach Against Graph Structure Perturbation

Graph Neural Networks (GNNs) are playing an increasingly important role in the efficient operation and security of computing systems, with applications in workload scheduling, anomaly detection, and resource management. However, their vulnerability to network perturbations poses a significant challenge. We propose $β$-GNN, a model enhancing GNN robustness without sacrificing clean data performance. $β$-GNN uses a weighted ensemble, combining any GNN with a multi-layer perceptron. A learned dynamic weight, $β$, modulates the GNN's contribution. This $β$ not only weights GNN influence but also indicates data perturbation levels, enabling proactive mitigation. Experimental results on diverse datasets show $β$-GNN's superior adversarial accuracy and attack severity quantification. Crucially, $β$-GNN avoids perturbation assumptions, preserving clean data structure and performance.

cs.LG↗