CAREER: Anahita: A Resilient and Agile Fog Framework for Large-scale Disaster Incidence Response

职业：Anahita：用于大规模灾害事件响应的弹性和敏捷的雾框架

• 批准号: 1943338
• 负责人: Saptarshi Debroy
• 金额: $ 52.34万
• 依托单位: CUNY Hunter College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943338&HistoricalAwards=false
• 关键词: CAREER; Anahita; Resilient; Agile; Fog

Fog computing is distinguished from Cloud computing by not using resources contained in large remote data centers, but instead leveraging computation, storage, and network resources that are available as close to the user as possible. These computation resources may be, for example, a part of the communications network (Edge resources) that the user is connected to, rather than using that network simply to connect to the Cloud. For rapid situational awareness to first responders during large-scale disaster incidence response, Fog computing-based response systems can be more effective than Cloud-based systems because of this proximity. However, design of effective Fog-based systems for disaster response is non-trivial as generic Fog computing methods and models assume Cloud-like environment which cannot be applied for disaster scenarios. This project proposes fundamental research, design, and development of a Fog framework that is novel in tackling the unique challenges of Fog resource management for disaster incidence response. These will benefit: 1) disaster management efforts by the first responders, 2) incident response management planning and policy makers, 3) cloud, cyber infrastructure, network management, and future Internet research communities, and 4) faculty and students in cloud and network management classrooms and labs. The educational activities will help motivate and train students at Hunter College and The City University of New York (CUNY) to pursue computer science where the student body predominantly consists of women and first-generation college students.The proposed Fog framework will be resilient against an unpredictable system environment and agile in provisioning resources for mission-critical and real-time applications. The project will use a novel sociological based organizational theory for emergency management inspired schema to characterize the fluctuations and their impact as adversarial and trust models. Using adversarial queuing theory, the project will shift the paradigm from traditional long-term cloud and edge resource optimization towards transient fog utility optimization under challenged environments. At the same time, the project will generate genetic algorithms and deep learning algorithms that aim to optimize fluctuation avoidance without compromising limited fog resources. The outcomes of such optimizations in terms of algorithms will be implemented through design and development of a software-defined fog architecture with unified resource broker services.The data generated in this project will initially be stored in the CUNY Institute CoSSMO data storage computer, and, after publication of the results, will be made available through digital repositories such as FigShare.com and drum.lib.edu. The project related algorithms and software tools will, after publication, be made available under an open source license on GitHub.com. The proposed Fog testbed will be made available to the broader cloud research community through integration with NSF supported COSMOS testbed. Published scholarly articles will be made available through web repositories such as arXiv.org. All project related resources can be access via project website:http://www.cs.hunter.cuny.edu/~S.Debroy99/projects-disaster.html.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.

雾计算与云计算的区别在于，雾计算不使用大型远程数据中心中包含的资源，而是利用尽可能靠近用户的可用计算、存储和网络资源。例如，这些计算资源可能是用户连接到的通信网络（边缘资源）的一部分，而不是简单地使用该网络连接到云。为了在大规模灾难响应期间向急救人员提供快速态势感知，基于雾计算的响应系统由于这种接近性而比基于云的系统更有效。然而，设计有效的基于雾的灾难响应系统并非易事，因为通用的雾计算方法和模型假设云环境，无法应用于灾难场景。该项目提出了雾框架的基础研究、设计和开发，该框架在应对灾害发生响应的雾资源管理的独特挑战方面具有新颖性。这些将受益于：1) 急救人员的灾难管理工作，2) 事件响应管理规划和政策制定者，3) 云、网络基础设施、网络管理和未来互联网研究社区，以及 4) 云和网络领域的教职员工和学生管理教室和实验室。这些教育活动将有助于激励和培训亨特学院和纽约市立大学 (CUNY) 的学生追求计算机科学，因为学生群体主要由女性和第一代大学生组成。拟议的 Fog 框架将能够抵御不可预测的系统环境，并为关键任务和实时应用程序灵活配置资源。该项目将使用一种新颖的基于社会学的组织理论来进行应急管理启发模式，以对抗性和信任模型来描述波动及其影响。利用对抗性排队理论，该项目将把范式从传统的长期云和边缘资源优化转向充满挑战的环境下的瞬态雾效用优化。同时，该项目将生成遗传算法和深度学习算法，旨在在不影响有限雾资源的情况下优化波动避免。这种算法优化的结果将通过设计和开发具有统一资源代理服务的软件定义雾架构来实现。该项目中生成的数据最初将存储在纽约市立大学研究所 CoSSMO 数据存储计算机中，并且，结果发布后，将通过FigShare.com和drum.lib.edu等数字存储库提供。该项目相关的算法和软件工具将在发布后根据开源许可证在 GitHub.com 上提供。拟议的 Fog 测试床将通过与 NSF 支持的 COSMOS 测试床集成，提供给更广泛的云研究社区。已发表的学术文章将通过 arXiv.org 等网络存储库提供。所有项目相关资源都可以通过项目网站访问：http://www.cs.hunter.cuny.edu/~S.Debroy99/projects-disaster.html。该奖项反映了 NSF 的法定使命，并被认为值得通过以下方式支持：使用基金会的智力价值和更广泛的影响审查标准进行评估。

项目成果

EFFECT-DNN: Energy-efficient Edge Framework for Real-time DNN Inference

EFFECT-DNN：用于实时 DNN 推理的节能边缘框架

• DOI: 10.1109/wowmom57956.2023.00015
• 发表时间: 2023-06-01
• 期刊: 2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)
• 作者: Xiaojie Zhang;Motahare Mounesan;S. Debroy
• 通讯作者: S. Debroy

Resource Management in Mobile Edge Computing: A Comprehensive Survey

• DOI: 10.1145/3589639
• 发表时间: 2023-03-28
• 期刊: ACM Computing Surveys
• 影响因子: 16.6
• 作者: Xiaojie Zhang;S. Debroy
• 通讯作者: S. Debroy