We present a model for measuring the impact of offloading soft real-time jobs over multi-tier cloud infrastructures. The jobs originate in mobile devices and offloading strategies may choose to execute them locally, in neighbouring devices, in cloudlets or in infrastructure cloud servers. Within this specification, we put forward several such offloading strategies characterised by their differential use of the cloud tiers with the goal of optimizing execution time and/or energy consumption. We implement an instance of the model using Jay, a software framework for adaptive computation offloading in hybrid edge clouds. The framework is modular and allows the model and the offloading strategies to be seamlessly implemented while providing the tools to make informed runtime offloading decisions based on system feedback, namely through a built-in system profiler that gathers runtime information such as workload, energy consumption and available bandwidth for every participating device or server. The results show that offloading strategies sensitive to runtime conditions can effectively and dynamically adjust their offloading decisions to produce significant gains in terms of their target optimization functions, namely, execution time, energy consumption and fulfilment of job deadlines.

中文翻译：

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移动边缘云中软实时作业的能量感知自适应卸载

我们提出了一个模型，用于衡量卸载软实时作业对多层云基础架构的影响。作业源自移动设备，卸载策略可以选择在本地、相邻设备、小云或基础架构云服务器中执行它们。在本规范中，我们提出了几种这样的卸载策略，其特征是它们对云层的不同使用，目的是优化执行时间和/或能源消耗。我们使用 Jay 实现了该模型的一个实例，Jay 是一种用于在混合边缘云中进行自适应计算卸载的软件框架。该框架是模块化的，允许无缝实施模型和卸载策略，同时提供工具以根据系统反馈做出明智的运行时卸载决策，即通过内置的系统分析器收集运行时信息，例如每个参与设备或服务器的工作负载、能耗和可用带宽。结果表明，对运行时条件敏感的卸载策略可以有效且动态地调整其卸载决策，以在其目标优化功能方面产生显着收益，即执行时间、能源消耗和完成工作期限。