Geo-distributed clouds have emerged as a new generation of cloud computing paradigm, in which each cloud is operated and managed by independent cloud service providers (CSPs). By enhancing cooperation among CSPs, it can offer efficient cross-cloud services. In geo-distributed clouds, the resources offered by CSPs are heterogeneous with different billing mechanisms and the data required by workflow applications are geographically distributed with locality characteristics. As such, it is significantly challenging for cloud users to select the appropriate resources to execute their workflow applications. In this paper, we model the constrained multi-objective workflow scheduling problem (CMWSP) in geo-distributed clouds as a constrained multi-objective optimization problem that minimizes both workflow makespan and resource rental costs. To solve the CMWSP, we propose a multi-objective multi-workflow scheduling mechanism (MOMWS), which integrates workflow preprocessing, evolutionary multi-objective optimization and intensification strategy while explicitly considering the data locality characteristics, deadline requirements, and rental period reuse. Specifically, we first design a task preprocessing algorithm for workflow applications to reduce transferred data volume by merging tasks with the same original datasets. Based on this algorithm, we introduce a priority assignment algorithm to decide the scheduling sequence of workflow applications. We next propose a makespan and cost-aware workflow scheduling algorithm to determine a set of high-quality approximations of the Pareto front to the CMWSP. Based on real-world CSPs and workflow applications, extensive experiments are carried out to demonstrate the effectiveness and efficiency of MOMWS.

中文翻译：

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基于多目标优化的工作流调度，适用于地理分布式云中具有数据局部性和截止日期约束的应用程序

地理分布式云已成为新一代云计算范例，其中每个云均由独立的云服务提供商（CSP）运营和管理。通过加强CSP之间的合作，可以提供高效的跨云服务。在地理分布式云中，CSP提供的资源是异构的，具有不同的计费机制，并且工作流应用程序所需的数据在地理上分布且具有局部性特征。因此，云用户选择合适的资源来执行其工作流应用程序是一项巨大的挑战。在本文中，我们将地理分布式云中的约束多目标工作流调度问题（CMWSP）建模为约束多目标优化问题，最大限度地减少工作流完工时间和资源租赁成本。为了解决CMWSP，我们提出了一种多目标多工作流调度机制（MOMWS），该机制集成了工作流预处理、进化多目标优化和强化策略，同时明确考虑数据局部性特征、截止日期要求和租赁期重用。具体来说，我们首先为工作流应用程序设计一种任务预处理算法，通过将任务与相同的原始数据集合并来减少传输的数据量。基于该算法，我们引入了优先级分配算法来决定工作流应用程序的调度顺序。接下来，我们提出了一种完工时间和成本感知的工作流调度算法，以确定帕累托前沿到 CMWSP 的一组高质量近似值。基于现实世界的 CSP 和工作流程应用，进行了大量的实验来证明 MOMWS 的有效性和效率。