Internet of Things (IoT) paradigm raises challenges for devising efficient strategies that offload applications to the fog or the cloud layer while ensuring the optimal response time for a service. Traditional computation offloading policies assume the response time is only dominated by the execution time. However, the response time is a function of many factors including contextual parameters and application characteristics that can change over time. For the computation offloading problem, the majority of existing literature presents efficient solutions considering a limited number of parameters (e.g., computation capacity and network bandwidth) neglecting the effect of the application characteristics and dataflow configuration. In this paper, we explore the impact of the computation offloading on total application response time in three-layer IoT systems considering more realistic parameters, e.g., application characteristics, system complexity, communication cost, and dataflow configuration. This paper also highlights the impact of a new application characteristic parameter defined as Output-Input Data Generation (OIDG) ratio and dataflow configuration on the system behavior. In addition, we present a proof-of-concept end-to-end dynamic computation offloading technique, implemented in a real hardware setup, that observes the aforementioned parameters to perform real-time decision-making.

物联网 (IoT) 范式提出了挑战，需要制定有效的策略，将应用程序卸载到雾层或云层，同时确保服务的最佳响应时间。 传统的计算卸载策略假设响应时间仅由执行时间决定。然而，响应时间是许多因素的函数，包括随时间变化的上下文参数和应用程序特性。 对于计算卸载问题，大多数现有文献提出了考虑有限数量的参数（例如，计算能力和网络带宽）而忽略应用特性和数据流配置的影响的有效解决方案。在本文中，我们考虑了更现实的参数，例如应用程序特性、系统复杂性、通信成本和数据流配置，探讨了计算卸载对三层物联网系统中总应用程序响应时间的影响。 本文还重点介绍了定义为输出输入数据生成 (OIDG) 比率和数据流配置的新应用特征参数对系统行为的影响。 此外，我们提出了一种概念验证端到端动态计算卸载技术，在真实的硬件设置中实现，观察上述参数以执行实时决策。