K-RAF: A Kubernetes-based Resource Augmentation Framework for Edge Devices

Internet of Things (IoT) (or edge) devices are typically resource-constrained in terms of CPU, memory, and storage. Thus, it is viable for the devices to request resource provisioning to an edge server in the presence of growing data and heavy computation, as the edge server provides better accessibility than cloud servers. Consequently, the edge devices often perform computation and storage provisioning to the edge servers in large-scale data operations. However, the conventional methods for provisioning edge devices take into little consideration the characteristics of resources that jobs executed at the devices rely on. In particular, fully migrating computation jobs from the device to the server may waste valuable resources of the server without considering the computation and I/O characteristics of the jobs, thereby making the devices' resources idle. To overcome these limitations, we propose a novel Kubernetes-based resource augmentation framework, termed K-RAF, for provisioning edge devices with limited capabilities and accelerating the devices' job processing. Our experiment demonstrates that utilizing GPU acceleration, on average, K-RAF can run tasks 306 times faster than local computation on an edge device. Also, we show that utilizing the task distribution between an edge device and K-RAF can offer an average speedup of about 40% compared to K-RAF alone.