CICI: UCSS: Trusted Resource Allocation in Volunteer Edge-Cloud Computing Workflows

CICI：UCSS：志愿者边缘云计算工作流程中的可信资源分配

• 批准号: 2232889
• 负责人: Prasad Calyam
• 金额: $ 60万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2232889&HistoricalAwards=false
• 关键词: CICI; UCSS; Trusted; Resource; Allocation

The unprecedented growth in networked smart edge devices (e.g., scientific instruments, edge servers, sensors) and related data sources has caused a data deluge in scientific application communities. The data processing is increasingly relying on machine learning algorithms and cloud platforms to cope with the heterogeneity, scale, and velocity of the data. At the same time, there is an abundance of low-cost computation resources that can be used for “volunteer edge-cloud computing” (VEC), where collaborators in a community with various backgrounds contribute their resources to form a distributed infrastructure to execute scientific workflows. The VEC paradigm has the potential to benefit many scientific workflow communities in diverse disciplines (e.g., geography, neuroscience, manufacturing, agriculture). This CICI project investigates the barriers for wider adoption of VEC caused due to lack of trust amongst the collaborating peers in terms of expected performance, and security of the VEC nodes, as well as workflow policies. This project’s goal is to develop a VECTrust architecture and a related framework for support of trusted resource allocation in VEC environments for scientific data-intensive workflows. Driven by application community requirements for VEC in the KBCommons science gateway for multi-omics data integration, a threat model is being developed to inform the security of the resource allocation options for the collaborating peers. In addition, a trust model is being developed that uses a two-stage probabilistic model to define trustworthiness of VEC cluster resources using metrics relevant to workflow execution factors that include: performance, agility, cost and security. Through usability studies, a prescriptive algorithm is being investigated to ensure that domain scientists who may lack the knowledge, background or resources to secure workflows can easily and seamlessly adopt VEC clusters. Project outreach activities are focused on creation and dissemination of open-source and collaborative trust-enabling VEC resource orchestration tools, datasets and educational materials to train next-generation talent.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.

联网智能边缘设备（例如，科学仪器、边缘服务器、传感器）和相关数据源已经在科学应用社区中引起了数据泛滥。数据处理越来越依赖于机器学习算法和云平台来科普数据的异构性、规模和速度。与此同时，有大量低成本的计算资源可用于“志愿者边缘云计算”（VEC），其中社区中具有各种背景的协作者贡献他们的资源以形成分布式基础设施来执行科学工作流程。VEC范式有可能使不同学科的许多科学工作流程社区受益（例如，地理学、神经科学、制造业、农业）。这个CICI项目调查的障碍，更广泛地采用VEC造成的合作伙伴之间缺乏信任的预期性能，安全的VEC节点，以及工作流策略。该项目的目标是开发一个VECTrust架构和一个相关的框架，用于支持科学数据密集型工作流程的VEC环境中的可信资源分配。受应用社区对多组学数据集成KBCommons科学网关中VEC的要求的驱动，正在开发一个威胁模型，以告知协作对等体的资源分配选项的安全性。此外，正在开发的信任模型，使用两阶段的概率模型来定义可信度的VEC集群资源使用相关的工作流执行因素，包括：性能，敏捷性，成本和安全性的指标。通过可用性研究，正在研究一种规定性算法，以确保可能缺乏保护工作流的知识、背景或资源的领域科学家可以轻松无缝地采用VEC集群。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

VECFlex: Reconfigurability and Scalability for Trustworthy Volunteer Edge-Cloud supporting Data-intensive Scientific Computing

• DOI: 10.1109/ucc56403.2022.00027
• 发表时间: 2022-12
• 期刊: 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)
• 作者: Mauro Lemus Alarcon;Minh Nguyen;Ashish Pandey;S. Debroy;P. Calyam