Loading Cost-Aware Model Caching And Request Routing In Edge-enabled Wireless Sensor Networks

Abstract Existing works on caching in multi-access edge computing focus on service caching and request routing. However, loading cost and execution time influenced by resource sharing have not been well exploited. To fill this gap, we investigate the joint optimization problem over deep neural network (DNN) model caching and DNN request routing with edge collaboration in edge-enabled wireless sensor networks. A problem is formulated, with the objective of maximizing throughput, under constraints of budget, accuracy and latency etc. The proof of NP-hardness for the formulated problem is provided. To solve the problem, an approximation algorithm based on randomized rounding is presented. In addition, the approximation ratio for the presented algorithm is proved to be $1/(1-\sqrt{4\ln S/\xi^\dagger})$, where $S$ is the number of edge servers and $\xi^\dagger$ is the objective value from linear relaxation. Extensive experiments demonstrate that the system throughput for the presented algorithm can be improved by 58.8% on average, compared with that of the baseline algorithm.