An Adaptive Swarm Intelligence-Based Task Scheduling Framework for Heterogeneous IoT Systems.

Task scheduling is one of the most fundamental technologies in a cloud computing environment. The essence of resource scheduling is to determine the appropriate executor and provide the on-demand resources to meet the task overhead. Due to the heterogeneity of the Internet of things (IoT), it has been a challenging task to efficiently schedule various IoT application tasks. An efficient scheduling system not only has to comply with task dependency constraints of IoT applications, but also should ensure the load balance of heterogeneous processors and minimize the total task execution time (i.e. makespan). In this paper, a collaborative management and scheduling framework is proposed based on an Adaptive Priority-based Particle Swarm Optimization (AP-PSO) for processing and communication resource allocation in heterogeneous IoT systems. The proposed approach is well adapted to the characteristics of IoT, including the processor heterogeneity, non-negligible communication costs, the diverse demand for service resources, and time-varying states of resources. Makespans and costs are adopted as metrics to evaluate the proposed scheduling model. Experimental results demonstrate that our approach outperforms other counterpart methods at the aspects of makespans and costs.