Task scheduling in cloud-based survivability applications using swarm optimization in IoT

Internet of Things (IoT) is dramatically growing in support of the recent revolutionary cloud-based survivability applications. It has to meet the performance expectations for these applications in real-time while optimizing the available cloud resources. In this paper, we propose a cooperative resource scheduling in energy-constrained applications for a reliable and fault-tolerant performance. We present a task scheduling algorithm based on robust canonical particle swarm optimization (CPSO) and fully informed particle swarm optimization (FIPS) algorithm to solve the problem of resource allocation in both homogeneous and heterogeneous cloud-based IoT applications. Our objective is to satisfy the quality of service in terms of throughput and delay by performing optimal task scheduling while considering the different experienced data traffic categories. Our results show that throughput and delay can be significantly improved while using the FIPS approach in comparison to the CPSO optimization algorithm.