The highly distributed infrastructure and the hetero geneous services and dynamic workloads of future 6G networks impose severe requirements on resource orchestration. We in troduce KG-Orchestrator, a unified framework which combines a Neo4j knowledge graph and a multi-GNN ensemble (Graph SAGE, HashGNN, GAT, GCN) to pe...
Debashish ROY, Alaa AlZailaa, Kostas Ramantas et al.· Zenodo (CERN European Organi...· 0 citations
The highly distributed infrastructure and the hetero geneous services and dynamic workloads of future 6G networks impose severe requirements on resource orchestration. We in troduce KG-Orchestrator, a unified framework which combines a Neo4j knowledge graph and a multi-GNN ensemble (Graph SAGE, HashGNN, GAT, GCN) to pe...
Debashish ROY, Alaa AlZailaa, Kostas Ramantas et al.· Zenodo (CERN European Organi...· 0 citations
The increasing demand for intelligent, low-latency services in edge–cloud continuum systems poses new challenges for dynamic and efficient task offloading. We propose a Multi Agent Reinforcement Learning (MARL) framework for dis tributed task offloading under partial observability, where each device offloads only a por...
Muhammad Rafid, Golshan Famitafreshi, V. Avgerinos et al.· Zenodo (CERN European Organi...· 0 citations
The increasing demand for intelligent, low-latency services in edge–cloud continuum systems poses new challenges for dynamic and efficient task offloading. We propose a Multi Agent Reinforcement Learning (MARL) framework for dis tributed task offloading under partial observability, where each device offloads only a por...
Muhammad Rafid, Golshan Famitafreshi, V. Avgerinos et al.· Zenodo (CERN European Organi...· 0 citations
Task offloading in End–Edge–Cloud computing enables resource-constrained User Devices (UDs) to execute computation-intensive applications with reduced latency and energy consumption. However, existing task offloading schemes generally overlook the heterogeneous privacy requirements of users, leading to inefficient priv...
Muhammad Rafid, Golshan Famitafreshi, Kostas Ramantas et al.· Zenodo (CERN European Organi...· 0 citations
Task offloading in End–Edge–Cloud computing enables resource-constrained User Devices (UDs) to execute computation-intensive applications with reduced latency and energy consumption. However, existing task offloading schemes generally overlook the heterogeneous privacy requirements of users, leading to inefficient priv...
Muhammad Rafid, Golshan Famitafreshi, Kostas Ramantas et al.· Zenodo (CERN European Organi...· 0 citations
The rapid evolution of sixth-generation (6G) wireless networks is increasing the demand for AI-enabled edge intel ligence that can operate across devices with different capabil ities, resource constraints, and availability. Existing distributed learning paradigms, including Federated Learning (FL) and Split Learning (S...
SrushtiSurpur, Panagiotis Marantis, Kostas Ramantas et al.· Zenodo (CERN European Organi...· 0 citations
The rapid evolution of sixth-generation (6G) wireless networks is increasing the demand for AI-enabled edge intel ligence that can operate across devices with different capabil ities, resource constraints, and availability. Existing distributed learning paradigms, including Federated Learning (FL) and Split Learning (S...
SrushtiSurpur, Panagiotis Marantis, Kostas Ramantas et al.· Zenodo (CERN European Organi...· 0 citations
We demonstrate a system that replaces the default Kubernetes scheduler with an inference and topology-aware Reinforcement Learning policy. The system time-slices each GPU, consumes live Prometheus telemetry and places inference workloads across Far-Edge, Edge and Cloud tiers using an action masked agent (MaskablePPO, M...
Ioannis Kyriakopoulos, Kostas Ramantas, Christos V. Verikoukis· Zenodo (CERN European Organi...· 0 citations
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