Ant colony optimization-based multi-objective task scheduling algorithm for supercomputing internet workflow

Supercomputing Internet is based on supercomputers and high-speed Internet, achieving the interconnection of data and computing resources between supercomputing centers in various regions, solving the problem of uneven distribution of computing resources, and providing diversified computing services. Workflow tasks consist of a large number of related tasks, and task scheduling of workflows is crucial in the supercomputing Internet. Good algorithms help provide users with higher quality services. Traditional heuristic methods usually focus on shortening the makespan and tend to prioritize the use of resources with stronger computing power. However, this method is prone to falling into local optima and increases cost overhead. In this article, we model the makespan of workflows and the cost of supercomputing resources as multi-objective optimization problems and propose a multi-objective ant colony algorithm (MOACO). The experimental results show that the algorithm effectively reduces makespan and cost, and fully utilizes available resources.