Edge computing promises lower processing latencies and better privacy control than cloud computing for task offloading as edge devices are positioned closer to users. Realizing this promise depends on building strong theoretical and engineering foundations of computing based on an edge continuum connecting edge to other resources. In the SPEC-RG Cloud Group, we conducted a systematic study of computing models for task offloading and found that these models have many shared characteristics. Despite these commonalities, no systematic model or architecture for task offloading currently exists. In this paper, we address this need by proposing a reference architecture for task offloading in the edge continuum and synthesize its components using well-understood abstractions, services, and resources from cloud computing. We provide domain-specific architectures for deep learning and industrial IoT and show how this unified computing model opens up application development as developers are no longer limited to the single set of constraints posed by current isolated computing models. Additionally, we demonstrate the utility of the architecture by designing a deployment and benchmarking framework for edge continuum applications and investigate the performance of various edge continuum deployments. The framework allows for fine-grained discovery of the edge continuum deployment space, including the emulation of complex networks, all with minimal user input required. To enhance the performance analysis capabilities of the benchmark, we introduce an analytical first-order performance model that can be used to explore multiple application deployment scenarios such as local processing on endpoints or offloading between cloud or edge. The deployment and benchmarking framework is open-sourced and available at https://github.com/atlarge-research/continuum.
翻译:在SPEC-RG Cloud Group中,我们对任务卸载的计算模型进行了系统研究,发现这些模型有许多共同特征。尽管存在这些共同特征,但目前没有任务卸载的系统模型或架构。在本文件中,我们提出一个任务卸载在边缘连续系统的参考架构,并利用云计算中精密的抽象、服务和资源来合成其组件。我们为深层学习和工业 IoT 提供了针对特定域的架构,并展示了这种统一的计算模型在开发者不再局限于当前孤立的计算模型所构成的单一制约下是如何开启应用程序开发的。此外,我们通过为边缘连续应用程序设计部署和基准框架,并调查各种边缘连续部署的性能,从而满足了这一需求。这个框架允许在云层连续部署空间上进行精细的发现,包括深层抽象的抽象的抽象、服务和云层计算中的资源。我们为深度的部署提供了特定域结构,并展示了这种统一的应用模式是如何开启的。我们所使用的复杂应用网络的测试能力,可以用来进行最起码的分析。