RCFS: rate and cost fair CPU scheduling strategy in edge nodes

With the rapid advancement of 5G mobile networks and Internet of Things technology, an increasing number of data-intensive applications are generating massive amounts of information, such as face recognition, video stream analysis, and augmented reality. These applications not only demand significant computational resources but also require high real-time performance, posing challenges to the existing cloud-computing model. Deploying these data-intensive applications in a mobile edge computing environment can reduce response time for processing user tasks and meet low-latency requirements. However, conventional CPU scheduling strategies fail to effectively enhance the performance of data-intensive applications on edge nodes with limited computing resources. In this study, we focus on real-time application feature awareness and propose a strategy considering data arrival rate and cost fair CPU scheduling (RCFS) with two components: CPU resource allocation based on process weights and process scheduling based on distributed weighted round-robin. In compared to the default scheduling strategy in Linux, named Completely Fair Scheduler, experiments on edge nodes show that the RCFS strategy can effectively enhance CPU utilization, reduce running time of data-intensive applications, and improve the system’s data throughput. In the best-case scenario, the RCFS strategy achieved a remarkable increase in CPU utilization of data-intensive applications by up to 98.5