Edge computing has emerged as an important paradigm to address the evolving demands of latency-sensitive applications and the adaptation of Internet of Things (IoT) devices, offering near edge data processing to increase performance, reduce bandwidth usage, and increase data privacy. However, benchmarking these heterogeneous edge environments remains a challenge due to their distributed nature and diverse hardware configurations. This paper proposes "Scalable Heterogeneous Edge Automation Benchmarking"(SHEAB), a novel containerized and automated framework designed to evaluate edge computing systems comprehensively. SHEAB integrates containerization for portability, automation for efficiency, and a multi-layered security architecture - including firewalls, VPNs, and secure shell connections - to ensure robust data integrity across varied edge servers. This research advances the field by providing a scalable, secure, and adaptable benchmarking solution, with future directions aimed at researching hardware capability assessments and increasing AI-driven edge-computing testing and benchmarking.