Collaborative Research: EAGER: Towards a Design Methodology for Software-Driven Sustainability

合作研究：EAGER：迈向软件驱动的可持续性设计方法

• 批准号: 2233873
• 负责人: Pooyan Jamshidi
• 金额: $ 10万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2233873&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; Towards; Design

With the proliferation of computing technologies in our society, software plays an increasingly prominent role in contributing to and solving sustainability challenges. Despite its importance, however, sustainability remains a poorly understood concept among developers, with a general lack of tools, knowledge, and techniques that can be used to design and validate software systems that account for sustainability. This project aims to elevate sustainability as a first-class quality attribute of software that can be explicitly analyzed and designed for and to empower developers with methods for building sustainability into their systems. In addition, this project contributes novel design strategies and patterns that can influence the behavior of users towards sustainable use of computing products. The outcome of this project lays a foundation for further research in software engineering techniques and methodologies for sustainable computing.To develop an in-depth understanding of sustainability challenges in software engineering, the first step in this project involves an empirical investigation of software applications in emerging, sustainability-relevant domains such as cyber-physical systems, IoT and mobile systems. Next, based on the outcome of this study, the project employs well-established requirements engineering methods to define sustainability as a collection of quality attributes, scenarios, and metrics. Finally, building on this definition, the project develops a catalog of design strategies and patterns that developers can use to build sustainability into their system as an explicit goal. This project also aims to establish a community of researchers in software engineering and other disciplines to encourage increased future activities in sustainability research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着计算技术在我们社会中的扩散，软件在贡献和解决可持续性挑战方面发挥了越来越重要的作用。然而，尽管它的重要性，但在开发人员中，可持续性仍然是一个鲜为人知的概念，通常缺乏工具，知识和技术，这些工具，知识和技术可用于设计和验证可用于可持续性的软件系统。该项目旨在提升可持续性，作为软件的一流质量属性，可以明确分析和设计并为开发人员提供以将可持续性构建到系统中的方法。此外，该项目贡献了新颖的设计策略和模式，可以影响用户对计算产品的可持续使用的行为。该项目的结果为可持续计算的软件工程技术和方法论提供了进一步研究的基础。为了深入了解软件工程中可持续性挑战的深入了解，该项目的第一步涉及对新兴，可持续性 - 可持续发展，诸如网络实体系统，网络物理系统，IOT和移动系统等新兴，可持续性和可持续性领域的经验研究。接下来，根据本研究的结果，该项目采用了良好的需求工程方法来将可持续性定义为质量属性，场景和指标的集合。最后，基于此定义，该项目开发了设计策略和模式的目录，开发人员可以用来将其作为明确的目标建立可持续性。该项目还旨在建立一个软件工程研究人员和其他学科的研究人员社区，以鼓励增加可持续性研究的未来活动。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来支持的。

项目成果

CAMEO: A Causal Transfer Learning Approach for Performance Optimization of Configurable Computer Systems

• DOI: 10.1145/3620678.3624791
• 发表时间: 2023-01-01
• 期刊: PROCEEDINGS OF THE 2023 ACM SYMPOSIUM ON CLOUD COMPUTING, SOCC 2023
• 作者: Iqbal，Md Shahriar;Zhong，Ziyuan;Jamshidi，Pooyan
• 通讯作者: Jamshidi，Pooyan

FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks

• DOI: 10.1613/jair.1.14139
• 发表时间: 2020-01
• 期刊: J. Artif. Intell. Res.
• 作者: Md Shahriar Iqbal;Jianhai Su;Lars Kotthoff;Pooyan Jamshidi

Reconciling High Accuracy, Cost-Efficiency, and Low Latency of Inference Serving Systems

• DOI: 10.1145/3578356.3592578
• 发表时间: 2023-04
• 期刊: Proceedings of the 3rd Workshop on Machine Learning and Systems
• 作者: Mehran Salmani;Saeid Ghafouri;Alireza Sanaee;Kamran Razavi;M. Muhlhauser;Joseph Doyle;Pooyan Jamshidi;Mohsen Sharif Iran University of Science;Technology;Queen Mary University London;Technical University of Darmstadt;U. O. N. Carolina

IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency

• DOI: 10.5070/sr34163500
• 发表时间: 2023-08
• 期刊: ArXiv
• 作者: Saeid Ghafouri;Kamran Razavi;Mehran Salmani;Alireza Sanaee;T. Lorido-Botran;Lin Wang;Joseph Doyle;Pooyan Jamshidi

CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robots

• DOI: 10.1109/lra.2023.3280810
• 发表时间: 2023-01
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Md. Abir Hossen;Sonam Kharade;B. Schmerl;Javier C'amara;Jason M. O'Kane;E. Czaplinski;K. Dzurilla;D. Garlan;Pooyan Jamshidi