Multi-objective accelerated particle swarm optimization with a container-based scheduling for Internet-of-Things in cloud environment

Over the last decades, cloud computing leverages the capability of Internet-of-Thing (IoT)-based applications by providing computational power as a form of a container or virtual machines (VMs). Most of the existing scheduling strategies deploy the VM instances for each task which require maximum start-up time and consumes maximum energy for processing the tasks. However, containers are a lightweight process and start in less than a second. In this paper, we develop a new energy-efficient container-based scheduling (EECS) strategy for processing various types of IoT and non-IoT based tasks with quick succession. The proposed method use accelerated particle swarm optimization (APSO) technique for finding a suitable container for each task with minimum delay. Resource scheduling is another important objective in a cloud environment for better utilization of the resources in the cloud servers. The EECS strategy can deploy the containers on an optimal cloud server with an optimal scheduling strategy. The main objectives of EECS are to minimize the overall energy consumptions and computational time of the tasks with efficient resource utilization. The effect of the control parameters of the APSO technique is investigated thoroughly. Through comparisons, we show that the proposed method performs better than the existing ones in terms of various performance metrics including computational time, energy consumption, CO2 emission, Temperature emission, and resource utilization.