Many real-world scientific workflows can be represented by a Directed Acyclic Graph (DAG), where each node represents a task and a directed edge signifies a dependency between two tasks. Due to the increasing computational resource requirements of these workflows, they are deployed on multi-cloud systems for execution. In this paper, we propose a scheduling algorithm that allocates resources to the tasks present in the workflow using an efficient list-scheduling approach based on the parameters cost, processing time, and reliability. Next, for a given a task-resource mapping, we propose a cipher assignment algorithm that assigns security services to edges responsible for transferring data in time-optimal manner subject to a given security constraint. The proposed algorithms have been analyzed to understand their time and space requirements. We implement the proposed scheduling and cipher assignment algorithm and experimented with two real-world scientific workflows namely Epigenomics and Cybershake. We compare the performance of the proposed scheduling algorithm with the state-of-art evolutionary methods. We observe that our method outperforms the state-of-art methods always in terms of cost and reliability, and is inferior in terms of makespan in some cases.
翻译:许多现实世界中的科学工作流可以用有向无环图(DAG)表示,其中每个节点表示一个任务,有向边表示两个任务之间的依赖关系。由于这些工作流的计算资源需求增加,它们被部署在多云系统上执行。本文提出了一种调度算法,它使用基于成本、处理时间和可靠性的有效的列表调度方法来分配工作流中的任务资源。接下来,针对给定的任务-资源映射,我们提出了一种密码分配算法,它在给定安全约束的情况下以时优的方式为负责传输数据的边分配安全服务。对所提出的算法进行了分析,以了解它们所需的时间和空间要求。我们实现了所提出的调度和密码分配算法,并针对Epigenomics和Cybershake两个真实世界的科学工作流进行了实验。我们将所提出的调度算法与最先进的演化方法进行了比较。我们观察到,我们的方法在成本和可靠性方面始终优于最先进的方法,但在某些情况下在机器上花费的时间方面劣于最先进的方法。