This chapter introduces MEC-RM (MEC resource manager), a middleware framework designed to facilitate the construction of edge servers equipped with a variety of hardware accelerators within multi-access edge computing (MEC)-based edge computing systems. The chapter begins by outlining the background, the challenges inherent to the deployment of heterogeneous edge infrastructures, and the objectives of our work, before proceeding to a detailed description of the proposed middleware and its application domains. MEC-RM provides a unified abstraction layer that enables seamless management of heterogeneous hardware resources, such as FPGAs and xPUs, allowing them to be treated coherently as server resources. Applications are thereby able to access and utilize these resources in a transparent and efficient manner through MEC-RM.
In addition, the chapter presents the design of the underlying scheduler that constitutes the core of MEC-RM, as well as extensions that support the coordination of multi-FPGA environments. The effectiveness of MEC-RM was validated through empirical evaluation in a practical use case scenario, namely, AI-driven robotic services within nursing facilities. Using a fall detection application as a representative workload, we demonstrated that application control mediated by MEC-RM achieves high responsiveness and enhanced energy efficiency. Furthermore, the functionality and benefits of MEC-RM were confirmed through implementation and testing in a real-world demonstration system.
The experimental results underscore the potential of MEC-RM as a key enabling technology for the expansion of AI-driven services across increasingly diverse and widespread edge environments. Finally, the chapter discusses future prospects for scaling edge services over broader geographic regions by leveraging the distributed control capabilities inherent in MEC-RM and concludes with a summary of key findings and avenues for future research.
