VNF Scheduling and Sampling Rate Maximization in Energy Harvesting IoT Networks

Abstract

This paper studies virtual network function (VNF) scheduling in energy harvesting virtualized Internet of Things (IoT) networks. Unlike prior works, sensor devices leverage imprecise computation to vary their computational workload to conserve energy at the expense of computation quality. In this respect, an optimization problem of interest is to maximize the minimum VNF computation/execution quality. To this end, this paper presents the first mixed integer linear program (MILP) that optimizes i) the VNFs executed by each sensor device, ii) the computational resources allocated to VNFs, iii) sampling rate or amount of data supplied by sensor devices to VNFs, iv) the routing of samples to VNFs and forwarding of computation results, and v) link scheduling. In addition, this paper also proposes a heuristic, called sampling control and computation scheduling (SCACS), for large-scale networks. The simulation results show that SCACS reaches 81.66% of the optimal quality. In addition, the application completion rate when using SCACS is at most 39% higher than a benchmark that randomly selects nodes to sample targets and execute VNFs.