Edge‐Assisted Adaptive Heterogeneous Resource Allocation Optimization for Large‐Scale Live Video Analytics

ABSTRACT This letter proposes an edge‐assisted adaptive heterogeneous resource allocation scheme based on atomic service chain (ASC) to jointly optimize adaptability for users, flexibility for networks, and profitability for providers. Particularly, we build an edge‐AI integrated computing power paradigm that integrates awareness, forwarding, storage, computing and processing capabilities. Moreover, we formulate the heterogeneous resource allocation problem into a non‐linear non‐convex integer optimization problem and propose an edge‐AI integrated ASC‐based resource allocation approach for large‐scale live video analytics to maximize average network utility with QoS support and minimize network congestion while considering the profitability. Experimental results demonstrate that the designed edge‐assisted ASC‐based adaptive heterogeneous resource allocation approach outperforms the monolithic model‐based scheme.