计算结构中工作流调度的调查和注释书目：社区、关键词和文章评论--扩展技术报告
A Survey and Annotated Bibliography of Workflow Scheduling in Computing Infrastructures: Community, Keyword, and Article Reviews -- Extended Technical Report
摘要：
工作流在当今的计算基础设施中很普遍。工作流模型支持各种不同的领域，从机器学习到金融，从天文学到化学。不同的服务质量（QoS）的要求和用户和供应商的其他愿望，使工作流调度一个棘手的问题，特别是因为资源供应商需要尽可能有效地与他们的资源竞争。对于一个新手甚至是一个有经验的研究人员来说，筛选大量的文章可能是一项艰巨的任务。关于差异技术、政策、新兴领域和机会的问题出现了。调查是解决这些问题的一种很好的方式，但调查很少公布其所依据的工具和数据。此外，这些文章背后的社区很少被研究。我们试图在这项工作中解决这些缺点。我们集中在工作流调度中的四个方面：1）工作流形式主义，2）工作流分配，3）资源供应，以及4）应用程序和服务。每个部分都包含一个或多个分类法、社区视图、重要和新兴关键字以及未来工作的方向。我们引入并开源了一种我们用来组合和存储文章元数据的工具。使用这些元数据，我们1）获得重要的关键字整体和每年，每个社区，2）确定关键字的重要性增长，3）深入了解每个社区内的结构和关系，以及4）执行系统的文献调查每个部分，以验证和补充我们的分类。
英文摘要：
Workflows are prevalent in today's computing infrastructures. The workflow model support various different domains, from machine learning to finance and from astronomy to chemistry. Different Quality-of-Service (QoS) requirements and other desires of both users and providers makes workflow scheduling a tough problem, especially since resource providers need to be as efficient as possible with their resources to be competitive. To a newcomer or even an experienced researcher, sifting through the vast amount of articles can be a daunting task. Questions regarding the difference techniques, policies, emerging areas, and opportunities arise. Surveys are an excellent way to cover these questions, yet surveys rarely publish their tools and data on which it is based. Moreover, the communities that are behind these articles are rarely studied. We attempt to address these shortcomings in this work. We focus on four areas within workflow scheduling: 1) the workflow formalism, 2) workflow allocation, 3) resource provisioning, and 4) applications and services. Each part features one or more taxonomies, a view of the community, important and emerging keywords, and directions for future work. We introduce and make open-source an instrument we used to combine and store article meta-data. Using this meta-data, we 1) obtain important keywords overall and per year, per community, 2) identify keywords growing in importance, 3) get insight into the structure and relations within each community, and 4) perform a systematic literature survey per part to validate and complement our taxonomies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
