DevOps Driven Software Performance Assurance for Large-scale Software Systems

DevOps 驱动的大型软件系统的软件性能保证

• 批准号: RGPIN-2021-03483
• 负责人: Shang, Weiyi
• 金额: $ 2.55万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750124
• 关键词: DevOps; Driven; Software; Performance; Assurance

The rise of large-scale software systems (e.g., Amazon Web Services) has become an important role in people's daily lives. Failures in such systems are more often associated with performance issues, rather than with feature bugs. Therefore, performance assurance activities are an essential step in the release cycle of large-scale software systems. Performance assurance activities aim to identify and eliminate performance issues during the development and operation of such large-scale software systems. Examples of performance issues are response time degradation, higher than expected resource utilization and memory leaks. Such issues may compromise the user experience, increase the operating cost of the system, and cause field failures. Failure in detecting such issues would result in significant financial and reputational repercussions. Performance assurance for large-scale software systems is facing great challenges in the era of DevOps. On one hand, detecting, locating, understanding and fixing performance issues are often conducted at a late stage in the development circle, with large amounts of resources required. On the other hand, the fast-paced release cycles of DevOps may not permit enough resources (e.g., long running time and dedicated environment) for the costly performance assurance activities. Moreover, existing approaches for performance assurance activities do not bridge the knowledge gap between software development and operations, which is of great significance in DevOps. The goal of the proposed research is to provide practitioners with novel software performance assurance approaches for large-scale software systems in the era of DevOps. To achieve our goal, I plan to improve the practice of performance assurance in order to be easily adopted by the engineering of large-scale software systems that follow DevOps practices. In particular, I propose to proactively ensure software performance during software development, such that practitioners can improve software performance and address performance issues in a timely and cost-effective manner. In addition, I propose to address software performance issues directly during the operation of the software in the field, with the end users. Finally, I propose the design of a systematic infrastructure and an analytical framework that bridge the knowledge of software performance with information uncovered from both the development and operation. Large-scale empirical studies will be performed on large open source and industrial systems, to understand the benefits and limitations of our work. The proposed research is a pioneering attempt to advance the practice to ensure the performance of large software systems that serve millions of users worldwide. Furthermore, the proposed research will expose, train and enable highly qualified personnel (HQP) to contribute to the state-of-the-art in software engineering research.

大规模软件系统的兴起（例如，Amazon Web Services）已经成为人们日常生活中的重要角色。此类系统中的故障通常与性能问题有关，而不是与功能错误有关。因此，性能保证活动是大型软件系统发布周期中的一个重要步骤。性能保证活动旨在查明和消除此类大型软件系统开发和运行期间的性能问题。性能问题的示例包括响应时间降低、高于预期的资源利用率和内存泄漏。这些问题可能会损害用户体验，增加系统的运营成本，并导致现场故障。如果不能发现这些问题，将造成重大的财务和声誉影响。在DevOps时代，大规模软件系统的性能保证面临着巨大的挑战。一方面，检测、定位、理解和修复性能问题通常在开发周期的后期阶段进行，需要大量的资源。另一方面，DevOps的快节奏发布周期可能不允许足够的资源（例如，长运行时间和专用环境），以进行成本高昂的性能保证活动。此外，现有的性能保证活动方法并没有弥合软件开发和运营之间的知识差距，这在DevOps中具有重要意义。所提出的研究的目标是为实践者提供新的软件性能保证方法，在DevOps时代的大型软件系统。为了实现我们的目标，我计划改进性能保证的实践，以便易于被遵循DevOps实践的大型软件系统的工程所采用。特别是，我建议在软件开发过程中积极确保软件性能，以便从业人员能够及时和具有成本效益地提高软件性能和解决性能问题。此外，我提议在软件在外地运行期间直接与最终用户解决软件性能问题。最后，我提出了一个系统的基础设施和分析框架，桥梁的知识，软件性能与信息发现的开发和操作的设计。将在大型开源和工业系统上进行大规模的实证研究，以了解我们工作的好处和局限性。拟议的研究是一个开拓性的尝试，以推进实践，以确保为全球数百万用户服务的大型软件系统的性能。此外，拟议的研究将暴露，培训和使高素质的人员（HQP），以促进国家的最先进的软件工程研究。