SHF: Small: Rethinking Virtualization at the Edge to Support Highly-efficient and Low-power Applications

与云计算类似，边缘计算也需要虚拟化的支持，以便于有效管理和高效利用。然而，简单地将传统的多层虚拟化从云迁移到边缘环境并不能充分利用边缘平台的灵活性、差异化和弹性。首先，云和边缘系统之间存在硬件约束和能量敏感性的显著差距。第二，过多的虚拟化层引入了间接和不必要的开销，导致系统结构不合理，效率低下。第三，边缘运行时和各种虚拟化层之间的语义差距，以及缺乏协调，阻碍了边缘系统上虚拟化的采用。本研究旨在解决这些问题，并寻求提高虚拟化边缘系统的性能、可预测性和能效。该研究还将紧密结合到课程教学和项目开发中，并通过产业合作、招募和指导少数民族学生以及在当地K-12学校开展外联活动来进一步扩大其影响。该项目将调查边缘计算虚拟化效率低下的原因，并探索释放性能和改善能耗的潜力。具体来说，该项目重点关注虚拟化边缘系统的多层协作方法，并需要两个研究重点。首先，它将重新思考边缘硬件和软件层之间的协作，使软件堆栈执行主动适应边缘-硬件动态，包括能耗，实时资源利用率，硬件异构性等，其次，它将使虚拟化应用层和OS系统层之间的软件协作成为可能，从而真正实现边缘虚拟化的效率和灵活性该奖项反映了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。

Similar to cloud computing, edge computing also needs the support of virtualization in order to facilitate the effective management and efficient utilization. However, simply migrating traditional multi-layered virtualization from the cloud to the edge environment cannot fully utilize the flexibility, differentiation, and elasticity in edge platforms. First, there exists a significant gap of hardware constraint and energy sensitivity between cloud and edge systems. Second, too many virtualized layers introduce indirect and unnecessary expenses, resulting in unreasonable system structure and low efficiency. Third, the semantic gap between the edge runtime and various virtualization layers, as well as lack of coordination, hinders the adoption of virtualization on edge systems. This research addresses these issues and seeks to improve the performance, predictability, and energy-efficiency of virtualized edge systems. The research will also be tightly integrated into course teaching, project development, and further broaden its impacts through industrial collaboration, recruiting and mentoring minority students, and outreach activities in local K-12 schools.This project will investigate the reasons for the inefficiency of virtualization in edge computing and explore the potential to unleash performance and improve energy consumption. Specifically, the project focuses on a multi-layered collaborative approach to virtualized edge systems and entails two research thrusts. First, it will rethink the collaboration between the edge hardware and software layers, so that the software-stack execution actively adapts to the edge-hardware dynamics, including energy consumption, real-time resource utilization, hardware heterogeneity, etc. Second, it will empower software collaboration between the virtualized application layer and OS system layer that can truly realize the efficiency and flexibility of virtualization on edge platforms.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.