An On-Site Elastic Autonomous Service Network with Efficient Task Assignment
With the increasing demand of the big data processing and the personalized services in the Internet, the current service platforms such as cloud are becoming hard to meet the requirement of multiple-level quality of services (QoS) within a short execution time. In this paper, an on-site elastic autonomous service network named SEA is proposed to cope with the above problems by utilizing the available resources from edge devices. The proposed SEA is constructed by the devices at the edge of the Internet and provides multiple-level QoS service to users. The architecture of the SEA is firstly designed to maintain the edge devices efficiently and monitor the status of the available resources. Then, an efficient scheduling algorithm based on discrete particle swarm optimization (PSO) is proposed to assign the tasks to proper devices in the SEA. The evaluation shows that the average execution time based on PSO is reduced more than 20% in comparison with a state-of-the-art algorithm. The simulation also shows that the hybrid SEA-cloud service is superior to the cloud-based service.