Edge computing and containerized microservices offer scalable and low-latency solutions for applications like Industrial Internet of Things (IIoT). However, optimizing resource allocation and fine-tuning performance in Kubernetes-orchestrated edge environments remain challenging due to diverse workloads and infrastructure complexities. This paper introduces a lifecycle experimentation system that streamlines workload deployment in such environments and enables users to fine-tune their containerized applications before production deployments. The proposed solution simplifies access to infrastructure resources and enables monitoring of new workloads while providing modular components for workload creation and metrics monitoring. Furthermore, the operation of the proposed system is verified by implementing a demo IIoT application, thereby showing our proposed system’s potential as a practical tool for effectively simplifying and optimizing edge-based IIoT deployments.