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Collaborative Edge-Cloud Computing for Efficient Resource Utilisation

Nowadays, the computing requirements for running different applications keep increasing. When setting up a system to run these apps, there are multiple possibilities to take into account. For instance, there is edge computing. Edge computing reduces latency and increases data privacy by bringing computation closer to the data source. In contrast to cloud servers, another option, edge devices frequently have constrained resource availability. The effectiveness and performance of applications can be greatly enhanced by balancing the resource use between edge and cloud computing.

Achieving this balance depends on knowing how edge and cloud computing resources may work together to serve varied application requirements. The framework that will be used to create this edge-cloud architecture is Kubernetes. However, Kubernetes does not inherently incorporate a comprehensive mechanism for balancing workloads between the edge and cloud infrastructures.

Evaluating Edge-Cloud Cooperation with Kubernetes

Thus, we researched how workloads could be offloaded between edge and cloud environments using Kubernetes, taking into account computing requirements, existing edge workloads, and other pertinent criteria. This approach aimed to reduce latency while ensuring stable or improved application performance. The goal of this research was to investigate a cooperative edge-cloud computing system that met application requirements while effectively utilizing resources.

We investigated the performance of several Kubernetes cluster setups, including edge-only, cloud-only, and edge-cloud configurations. To test these setups, we deployed a demo application on the Kubernetes clusters. Using a load testing program, we examined the performance and latency of these setups. Finally, we proposed an improved edge-cloud autoscaler setup for Kubernetes and compared its performance with the previously evaluated configurations.

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Reinier Goeman

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