<p>In cloud-edge collaboration systems, real-time data generated by massive heterogeneous terminal devices, such as smart sensors, industrial controllers, and wearable devices, needs to be processed with low latency through distributed servers. However, the heterogeneity of servers, such as differences in computing power, storage, and dedicated acceleration chips, and placement constraints, such as location-sensitive devices only being able to access specific servers, make the multi-resource allocation problem highly complex. At the same time, as an external resource independent of the server, the limited bandwidth of wireless channels needs to be shared by all devices in competition, further exacerbating the difficulty of ensuring fairness. The existing multi-resource allocation mechanism does not consider the placement constraints of servers and the collaborative scheduling of communication computing resources. In addition, in cloud-edge collaboration systems, “least picky users", which can access all edge servers, coexist with “picky users", which can only access some nodes, and a new mechanism needs to be designed to avoid excessive resource allocation bias towards devices with limited access capabilities. This article proposes a multi-resource allocation mechanism based on any price share (APS) (Babaioff et al. in Math Oper Res 49(4):2180–2211, 2023), called APSF, which achieves fair allocation of computing, storage, and communication resources in cloud-edge collaborative systems with placement constraints and an external resource. Through theoretical proof and large-scale simulation verification, the APSF mechanism significantly improves performance while ensuring important properties such as Pareto optimality, sharing incentive, strategy-proofness, local envy-freeness, and bottleneck fairness.</p>