Optimizing security and cost of workflow execution using task annotation and genetic-based algorithm

Abstract

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.