Distributed computing, such as Cloud, provides traditional workflow applications with completely new deployment architecture offering high performance and scalability. However, when executing the workflow tasks in a distributed computing environment, significant scheduling overheads are generated. Task clustering is a key technology to optimize process execution. Unreasonable task clustering can lead to the problems of runtime and dependency imbalance, which reduces the degree of parallelism during task execution. In order to solve the problem of runtime imbalance, we propose Runtime Balance Clustering Algorithm (RBCA), which employs the Backtracking approach to make the runtime of each cluster more balanced. In addition, to address the problem of dependency imbalance, we also propose Dependency Balance Clustering Algorithm (DBCA), which defines the dependency correlation to measure the similarity between tasks in terms of data dependencies. The tasks with high dependency correlation are clustered together so as to avoid the dependency imbalance to most extent. We conducted extensive experiments on the WorkflowSim platform and compared our algorithms with the existing task clustering algorithms. The results show that RBCA and DBCA significantly reduce the execution time of the whole workflow.

诸如云之类的分布式计算为传统的工作流应用程序提供了全新的部署架构，从而提供了高性能和可扩展性。但是，在分布式计算环境中执行工作流任务时，会产生大量的调度开销。任务群集是优化流程执行的关键技术。不合理的任务群集会导致运行时和依赖关系不平衡的问题，从而降低了任务执行过程中的并行度。为了解决运行时不平衡的问题，我们提出了运行时平衡聚类算法（RBCA），该算法采用回溯法使每个集群的运行时更加平衡。此外，为了解决依赖性不平衡的问题，我们还提出了依赖性平衡聚类算法（DBCA），它定义了依赖关系，以根据数据依赖关系来衡量任务之间的相似性。具有高依赖性相关性的任务被聚集在一起，从而最大程度地避免了依赖性失衡。我们在WorkflowSim平台上进行了广泛的实验，并将我们的算法与现有的任务聚类算法进行了比较。结果表明，RBCA和DBCA大大减少了整个工作流程的执行时间。