Cloud computing is a new paradigm of computing. This paradigm delivers services over the internet and eliminates requirements for local data storage. Instead of purchasing hardware and software, cloud computing enables users to use storage or applications as a service. Scheduling is the process of allocating the available resources in cloud environment. Scientific workflows consist of a large number of tasks. Workflow scheduling is a critical issue in cloud computing that targets to complete workflow execution by considering different parameters such as execution time, user deadlines, execution cost, and Quality of Service (QoS), etc. In this article, we present a Multi-resource Load Balancing Algorithm (MrLBA) cloud computing environment. The algorithm is based on Ant Colony Optimization (ACO). The proposed algorithm targets makespan, cost while keeping a well load-balanced system. The algorithm is validated with experimental results on benchmark workflows. The results show that MrLBA reduces both execution time and cost and efficiently utilizes available resources by maintaining balanced load among resources.

云计算是一种新的计算范式。这种范式通过 Internet 提供服务并消除对本地数据存储的要求。云计算无需购买硬件和软件，而是使用户能够将存储或应用程序用作服务。调度是在云环境中分配可用资源的过程。科学工作流程由大量任务组成。工作流调度是云计算中的一个关键问题，其目标是通过考虑执行时间、用户截止日期、执行成本和服务质量 (QoS) 等不同参数来完成工作流执行。在本文中，我们提出了一个多资源负载平衡算法 (MrLBA) 云计算环境。该算法基于蚁群优化 (ACO)。所提出的算法针对完工时间，成本，同时保持良好的负载平衡系统。该算法通过基准工作流的实验结果进行了验证。结果表明，MrLBA 减少了执行时间和成本，并通过保持资源之间的平衡负载来有效利用可用资源。