Container-Driven Scheduling Strategy for Scientific Workflows in Multi-vCPU Environments

In a distributed resource environment, container technology highly facilitates the deployment and execution of scientific workflow tasks. However, existing scientific workflow scheduling studies barely consider the multi-channel programming of computational resources, which makes it hard to simultaneously achieve effective container sharing and optimize task parallelism and resource utilization. In this paper, we propose a segmented workflow scheduling strategy based on container technology in multi-vCPU devices environment. It reduces the solution space size of the heuristic algorithm through a segmented scheduling approach. And it uses an adaptive discrete particle swarm optimization algorithm with genetic operators (ADPSOGA) to optimize the average completion time of each workflow under the constraint of device rental cost. In addition, we propose a dynamic scaling scheme between containers and devices to reuse containers and solve the problems related to resource contention when tasks are parallel in a device. Experimental results indicate that ADPSOGA outperforms other similar heuristics algorithms, and the segmented scheduling approach significantly improves the optimization-seeking efficiency of the algorithm.