Scientific workflow scheduling is one of the most challenging problems in cloud computing because of the large-scale computing tasks and massive data volumes involved. A cloud system is a distributed system that follows the on-demand resource provisioning and pay-per-use billing model. Therefore, practical scheduling approaches are essential for good workflow performance and low overheads. This paper proposes a novel workflow allocation approach, the Geo-aware Multiagent Task Allocation Approach (GMTA), which aims to optimize large-scale scientific workflow execution in container-based clouds. GMTA is an agent-based workflow allocation method that includes a market-like agent negotiation mechanism and a dynamic workflow restructuring strategy. It decreases workflow makespans and traffic overheads by reasonable task replications. Furthermore, the performance of GMTA is verified on real scientific workflows in the CloudSim environment.

中文翻译：

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GMTA：基于容器的云中科学工作流的地理感知多代理任务分配方法

由于涉及大规模的计算任务和海量的数据量，科学的工作流调度是云计算中最具挑战性的问题之一。云系统是一种分布式系统，遵循按需资源供应和按使用付费的计费模式。因此，实用的调度方法对于良好的工作流性能和低开销至关重要。本文提出了一种新颖的工作流分配方法，即地理感知多代理任务分配方法（GMTA），旨在优化基于容器的云中的大规模科学工作流执行。GMTA 是一种基于代理的工作流分配方法，包括类似市场的代理协商机制和动态工作流重组策略。它通过合理的任务复制减少了工作流的跨度和流量开销。此外，