CampEdge: Distributed Computation Offloading Strategy Under Large-Scale AP-Based Edge Computing System for IoT Applications

With the development of multiaccess edge computing (MEC) technology at the network edge, efficient resources allocation and offloading between the resource-constrained edge clouds to maintain load balancing become a looming problem recently. However, most existing researches aimed at optimizing the allocation of computing resources are based on simulation and apply only for some typical Internet-of-Things (IoT) applications on mobile devices (i.e., they all lack of practicality and generality). In this article, we present a novel edge computing platform (CampEdge) with 36 edge nodes based on the wireless access point (AP) for adaptive resources allocation and computation offloading in a complicated dynamic campus environment. We first collected and sufficiently analyzed a large real-world WiFi data set, covering more than 8500 wireless APs and serves 44 000 active end users within an area of 3.1 km(2) over three months. A multiclass classification algorithm based on the random forest was then used with this data to accurately predict the state of resources usage at each edge node (i.e., in a busy state or normal state). To reasonably offload and transmit end-users' computational tasks to these edge nodes and optimize the total latency cost among the whole offloading process, we then illustrate a distributed computation offloading optimization strategy to formulate this complicated problem as a multiobjective latency optimization problem based on alternating direction method of multipliers (ADMM). Furthermore, we briefly discuss the convergence of our system. In experiments, CampEdge was shown to decrease user latency by up to 30%, compared to state-of-the-art methods. The proposed strategy was also shown to adaptively conform to a variety of compute-intensive and time-sensitive IoT applications for end users.

Automated summary

With the development of MEC technology, the article introduces CampEdge platform with 36 edge nodes for adaptive resource allocation and computation offloading. Using a multiclass random forest algorithm and a distributed computation offloading optimization strategy, the system aims to optimize total latency cost and decrease user latency by up to 30%, showing adaptability to different IoT applications.