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On Scheduling of High-Throughput Scientific Workflows under Budget Constraints in Multi-Cloud Environments

机译:多云环境下预算约束下的高通量科学工作流调度

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摘要

With the rapid development and deployment of cloud computing infrastructures, many applications in various scientific domains are increasingly utilizing cloud resources for big data storage and analysis. Particularly, it has become a significant challenge to manage and execute big data scientific workflows in multi-cloud environments to process streaming datasets. In this paper, within a three-layer workflow architecture with inter-and intra-cloud data transfer, we formulate a scientific workflow mapping problem under budget constraints to achieve the maximum throughput of streaming workflow applications. We propose a scheduling algorithm to identify the global bottleneck and maximize the throughput under budget constraints. Extensive simulation results show that the proposed algorithm exhibits superior performance over existing heuristic algorithms in scheduling streaming workflow applications.
机译:随着云计算基础架构的快速发展和部署,各个科学领域中的许多应用程序都在越来越多地利用云资源进行大数据存储和分析。特别是,在多云环境中管理和执行大数据科学工作流以处理流数据集已成为一项重大挑战。在本文中,在具有云间和云内数据传输的三层工作流架构中,我们制定了预算约束下的科学工作流映射问题,以实现流式工作流应用程序的最大吞吐量。我们提出一种调度算法,以识别全局瓶颈并在预算约束下最大化吞吐量。大量的仿真结果表明,与现有的启发式算法相比,该算法在调度流工作流应用中表现出更好的性能。

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