GPU-Aware Scheduling and Dynamic Resource Orchestration in Heterogeneous Edge-Cloud Environments

The expansion of cloud services and the increasing demand for hardware-accelerated applications have led to a significant surge in GPU resource requirements. To reduce cloud latency and bandwidth consumption, offloading these workloads to the network edge is essential. However, orchestrating GPU-intensive tasks in heterogeneous edge environments remains a significant challenge, as standard container orchestrators like Kubernetes exhibit a native blindness toward hardware accelerators. In this paper, we propose a custom scheduler that extends the default Kubernetes scheduler behavior through different plugins designed for heterogeneous environments. By integrating GPU status and architectural awareness, our scheduler optimizes resource utilization, as demonstrated through experimental evaluation with a microservices-based workload.