As the next generation of diverse workloads like autonomous driving and augmented/virtual reality evolves, computation is shifting from cloud-based services to the edge, leading to the emergence of a cloud-edge compute continuum. This continuum promises a wide spectrum of deployment opportunities for workloads that can leverage the strengths of cloud (scalable infrastructure, high reliability) and edge (energy efficient, low latencies). Despite its promises, the continuum has only been studied in silos of various computing models, thus lacking strong end-to-end theoretical and engineering foundations for computing and resource management across the continuum. Consequently, developers resort to ad hoc approaches to reason about performance and resource utilization of workloads in the continuum. In this work, we conduct a first-of-its-kind systematic study of various computing models, identify salient properties, and make a case to unify them under a compute continuum reference architecture. This architecture provides an end-to-end analysis framework for developers to reason about resource management, workload distribution, and performance analysis. We demonstrate the utility of the reference architecture by analyzing two popular continuum workloads, deep learning and industrial IoT. We have developed an accompanying deployment and benchmarking framework and first-order analytical model for quantitative reasoning of continuum workloads. The framework is open-sourced and available at https://github.com/atlarge-research/continuum.

翻译：随着自动驾驶、增强现实/虚拟现实等多样化工作负载的演进，计算正从基于云的服务向边缘转移，催生了云-边计算连续体的出现。该连续体有望为各类工作负载提供广阔的部署机遇，使其既能利用云端（可扩展基础设施、高可靠性）与边缘（高能效、低延迟）的双重优势。尽管前景广阔，但该连续体此前仅在各类计算模型的孤立研究中被探讨，缺乏跨连续体进行资源管理与计算的端到端理论及工程基础。因此，开发者不得不采用临时方案来评估工作负载在连续体中的性能与资源利用率。本研究首次对多种计算模型开展系统性研究，识别其显著特征，并论证将其统一纳入计算连续体参考架构的合理性。该架构为开发者提供了端到端分析框架，用于推理资源管理、工作负载分布及性能分析。我们通过分析深度学习与工业物联网两类典型连续体工作负载，验证了参考架构的实用性。同时，我们开发了配套的部署与基准测试框架及一阶解析模型，用于对连续体工作负载进行定量分析。该框架已开源，访问地址为 https://github.com/atlarge-research/continuum。