Efficient Resources Allocation and Computation Offloading Model for AP-based Edge Cloud

摘要

Multi-access edge computing (MEC) plays an important role in taking the cloud computing resources closer to the end-user at the network edge. The main challenge in such systems is to build models to efficiently predict and offload computational resources to maintain load balancing among edge clouds. In this paper, we present an Efficient Resources Allocation and Computation Offloading (ERACO) model with 36 edge nodes based on the large-scale wireless access point (AP) in a campus environment. We first collected and sufficiently analyzed this large WiFi dataset, covering more than 8,500 wireless APs and serves 44,000 active end-users within an area of 3.1 km^{2 over three months. A multi-class classification algorithm was then used with this data to predict the usage of computing resources at each edge node. To reasonably offload these resources and decrease the latency between each edge node, we then propose the optimization strategy to formulate the complicated offloading problem as a multi-objective latency optimization problem using the alternating direction method of multipliers (ADMM). Experimental results show that the models decrease user latency by up to 30%, compared to state-of-the-art methods for a variety of applications.}