Exploring and modelling IoT offloading policies in edge cloud environments

Abstract

The Internet of Things (IoT) has recently become a popular technology that can play increasingly important roles in every aspect of our daily life. For collaboration between IoT devices and edge cloud servers, edge server nodes provide the computation and storage capabilities for IoT devices through the task offloading process for accelerating tasks with large resource requests. However, the quantitative impact of different offloading architectures and policies on IoT applications’ performance remains far from clear, especially with a dynamic and unpredictable range of connected physical and virtual devices. To this end, this work models the performance impact by exploiting a potential latency that exhibits within the environment of edge cloud. Also, it investigates and compares the effects of loosely-coupled (LC) and orchestrator-enabled (OE) architecture. The LC scheme can smoothly address task redistribution with less time consumption for the offloading sceneries with small scale and small task requests. Moreover, the OE scheme not only outperforms the LC scheme in the large-scale tasks requests and offloading occurs but also reduces the overall time by 28.19%. Finally, to achieve optimized solutions for optimal offloading placement with different constraints, orchestration is important.