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

联网智能边缘设备(如科学仪器、边缘服务器、传感器)和相关数据源的空前增长导致了科学应用社区的数据泛滥。数据处理越来越依赖于机器学习算法和云平台来应对数据的异构性、规模和速度。与此同时，有大量的低成本计算资源可用于“志愿者边缘云计算”(VEC)，即具有不同背景的社区中的合作者贡献他们的资源，以形成执行科学工作流的分布式基础设施。VEC范式有可能使不同学科(如地理学、神经科学、制造业、农业)的许多科学工作流程社区受益。这个CiCi项目调查了由于合作伙伴之间在预期性能、VEC节点的安全性以及工作流策略方面缺乏信任而导致的更广泛采用VEC的障碍。该项目的目标是开发VECTrust架构和相关框架，以支持在VEC环境中为科学数据密集型工作流分配可信资源。在KBCommons用于多组学数据集成的科学网关中对VEC的应用程序社区需求的推动下，正在开发一个威胁模型，以向安全部门通报协作对等点的资源分配选项。此外，正在开发一个信任模型，该模型使用两阶段概率模型来定义VEC集群资源的可信性，使用与工作流执行因素相关的指标，包括：性能、敏捷性、成本和安全性。通过可用性研究，正在研究一种规范的算法，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。项目外展活动的重点是创建和传播开放源码和协作信任，使VEC资源协调工具、数据集和教育材料能够培训下一代人才。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.