Abstract: To address the issues of unfair resource allocation and low utilization caused by resource preemption among different workloads in Function-as-a-Service (FaaS) edge computing, a time-aware dominant resource fair scheduling algorithm is proposed. Firstly, the limitations of existing dominant resource fair scheduling algorithms when applied to edge function computing services are analyzed. Then, the algorithm incorporates the runtime weight of function instances and utilizes a time-aware queue in conjunction with a dominant resource fair queue to achieve fair allocation of resources required for function execution and maximize cluster resource utilization. Finally, the algorithm is implemented based on the scheduler of a mainstream open-source function computing service platform. Test results using public workload datasets show that the proposed algorithm improves CPU utilization by up to 18.1% and memory utilization by up to 21.8%, while reducing execution time by up to 26.1%. This effectively enhances the fairness of resource allocation and improves resource utilization in edge function computing services.

Key words: edge computing, function-as-a-service (FaaS), time-aware, dominant resource fair scheduling