Treffer: Field-Deployable Kubernetes Cluster for Enhanced Computing Capabilities in Remote Environments.

Featured Application: The portable Kubernetes cluster supports on-site machine learning and web services in remote or resource-limited environments. It is applicable to field research, disaster response, military, and security operations requiring local autonomous, reliable, and secure computing capabilities. The cluster is intended to operate as a standalone computing device in a remote or isolated deployment, hosting local web services for information dissemination and enabling on-site retraining of AI models (e.g., for new enemy platform types) using data collected in the field. This paper presents a portable cluster architecture based on a lightweight Kubernetes distribution designed to provide enhanced computing capabilities in isolated environments. The architecture is validated in two operational scenarios: (1) machine learning operations (MLOps) for on-site learning, fine-tuning and retraining of models and (2) web hosting for isolated or resource-constrained networks, providing resilient service delivery through failover and load balancing. The cluster leverages low-cost Raspberry Pi 4B units and virtualized nodes, integrated with Docker containerization, Kubernetes orchestration, and Kubeflow-based workflow optimization. System monitoring with Prometheus and Grafana offers continuous visibility into node health, workload distribution, and resource usage, supporting early detection of operational issues within the cluster. The results show that the proposed dual-mode cluster can function as a compact, field-deployable micro-datacenter, enabling both real-time Artificial Intelligence (AI) operations and resilient web service delivery in field environments where autonomy and reliability are critical. In addition to performance and availability measurements, power consumption, scalability bottlenecks, and basic security aspects were analyzed to assess the feasibility of such a platform under constrained conditions. Limitations are discussed, and future work includes scaling the cluster, evaluating GPU/TPU-enabled nodes, and conducting field tests in realistic tactical environments. [ABSTRACT FROM AUTHOR]

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