探索Unikernel在ARM驱动的边缘计算中的可行性
Exploring the Viability of Unikernels for ARM-powered Edge Computing
AI总结：
针对ARM驱动的边缘计算，本文评估OSv、Nanos、Unikraft三种Unikernel系统与Docker的性能效率，揭示其资源消耗少、启动快的优势及优化方向，为边缘计算轻量级方案提供参考。
摘要：
物联网设备及其实时应用的快速扩展推动了对边缘计算的需求增长。为满足这一需求，在资源受限（功率、CPU和内存有限）的设备上运行此类应用需要高效且安全的解决方案。Unikernel凭借其极简设计和特定于应用的方法，在这些环境中为传统虚拟化和容器技术提供了有前景的替代方案。现有研究尚未深入探讨将Unikernel用于边缘计算的可行性。本文通过评估OSv、Nanos和Unikraft这三种主要Unikernel系统与Docker容器的性能和效率，研究了Unikernel在ARM驱动的边缘计算中的潜力。我们使用真实边缘计算应用进行实验，并利用启动时间、执行时间、内存使用、CPU开销和网络性能等关键指标，确定Unikernel在边缘设备约束下的表现。研究结果揭示了Unikernel在减少资源消耗和加快启动时间方面的潜在优势，同时指出了其在边缘部署中需要进一步优化的领域。本研究为考虑将Unikernel作为ARM架构边缘计算轻量级高效解决方案的研究人员和从业者提供了宝贵见解。
英文摘要：
The rapid expansion of IoT devices and their real-time applications have driven a growing need for edge computing. To meet this need, efficient and secure solutions are required for running such applications on resource-constrained devices with limited power, CPU, and memory. Unikernel, with its minimalistic design and application-specific approach, offers a promising alternative to traditional virtualization and container technologies in these environments. The existing research does not thoroughly examine the feasibility of using unikernel for edge computing. This paper investigates the potential of unikernel for ARM-powered edge computing by evaluating the performance and efficiency of three prominent unikernel systems such as OSv, Nanos, and Unikraft against Docker container. We experiment with real-world edge computing applications and utilize key metrics such as boot time, execution time, memory usage, CPU overhead, and network performance to determine how unikernel performs under the constraints of edge devices. Our findings reveal the potential advantages of unikernel in terms of reduced resource consumption and faster startup times while highlighting areas where they may need further optimization for edge deployment. This study provides valuable insights for researchers and practitioners considering unikernel as a lightweight, efficient solution for edge computing on ARM architectures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
