Cloud computing provides an extensible infrastructure for executing workflows that demand high processing and storage capacity. Tasks are distributed and resources selected during scheduling where choices have a significant impact on data protection. Some workflow scheduling algorithms apply security services such as authentication, integrity verification, and encryption for both sensitive and non-sensitive tasks. However, this approach requires long makespan and monetary cost for execution. In this paper, we introduce a scheduling approach that considers the user annotation of workflow tasks according to the sensitiveness. We also optimize the scheduling using a multi-population genetic algorithm for minimizing cost while meeting a deadline. Extensive experiments using three workflow applications with different ratios of sensitive tasks and data size were performed to evaluate in terms of cost, makespan, risk, and wastage. The results showed that our approach can protect sensitive tasks more appropriately while achieving a better cost compared to other approaches in the literature.

云计算为执行需要高处理和存储容量的工作流提供了可扩展的基础架构。在计划期间分配任务并选择资源，其中选择对数据保护有重大影响。一些工作流程调度算法将安全服务（例如身份验证，完整性验证和加密）应用于敏感和非敏感任务。但是，这种方法需要较长的制造时间和金钱成本才能执行。在本文中，我们介绍了一种调度方法，该方法根据敏感度考虑了工作流任务的用户注释。我们还使用多种群遗传算法优化调度，以在满足截止日期的同时最大程度地降低成本。使用具有不同比例的敏感任务和数据大小的三个工作流应用程序进行了广泛的实验，以评估成本，有效期，风险和浪费。结果表明，与文献中的其他方法相比，我们的方法可以更适当地保护敏感任务，同时实现更高的成本。