The need for integration of applications and services in business processes from enterprises has increased with the advancement of cloud and mobile applications. Enterprises started dealing with high volumes of data from the cloud and from mobile applications, besides their own. This is the reason why integration tools must adapt themselves to handle with high volumes of data, and to exploit the scalability of cloud computational resources without increasing enterprise operations costs. Integration platforms are tools that integrate enterprises’ applications through integration processes, which are nothing but workflows composed of a set of atomic tasks connected through communication channels. Many integration platforms schedule tasks to be executed by computational resources through the First-in-first-out heuristic. This article proposes a Queue-priority algorithm that uses a novel heuristic and tackles high volumes of data in the task scheduling of integration processes. This heuristic is optimized by the Particle Swarm Optimization computational method. The results of our experiments were confirmed by statistical tests, and validated the proposal as a feasible alternative to improve integration platforms in the execution of integration processes under a high volume of data.

随着云和移动应用程序的进步，企业业务流程中对应用程序和服务集成的需求不断增加。企业开始处理来自云和移动应用程序的大量数据，除了他们自己的数据。这就是为什么集成工具必须适应处理大量数据，并在不增加企业运营成本的情况下利用云计算资源的可扩展性的原因。集成平台是通过集成流程集成企业应用的工具，集成流程只不过是由一组通过通信渠道连接的原子任务组成的工作流。许多集成平台通过先进先出启发式调度计算资源执行的任务。本文提出了一种队列优先级算法，该算法使用一种新颖的启发式算法来处理集成流程任务调度中的大量数据。该启发式通过粒子群优化计算方法进行了优化。我们的实验结果得到了统计测试的证实，并验证了该提案是在大量数据下改进集成流程执行集成平台的可行替代方案。