Abstract
Internet of Things (IoT) is swiftly evolving into a disruptive technology in recent years. For enhancing customer experience and accelerating job execution, IoT task offloading enables mobile end devices to release heavy computation and storage to the resource-rich nodes in collaborative Edges or Clouds. However, how different service architecture and offloading strategies quantitatively impact the end-to-end performance of IoT applications is still far from known particularly given a dynamic and unpredictable assortment of interconnected virtual and physical devices. This paper exploits potential network performance that manifests within the edge-cloud environment, then investigates and compares the impacts of two types of architectures: Loosely-Coupled (LC) and Orchestrator-Enabled (OE). Further, it introduces three customized offloading strategies in order to handle various requirements for IoT latency-sensitive applications. Through comparative experiments, we observed that the computational requirements exerts more influence on the IoT application’s performance compared to the communication requirement. However, when the system scales up to accommodate more IoT devices, communication bandwidth will turn to be the dominant resource and becomes the essential factor that will directly impact the overall performance. Thus, orchestration is a necessary procedure to encompass optimized solutions under different constraints for optimal offloading placement.
Highlights
There has been substantial growth in the number of connected devices in the digitalized era
Since Edge nodes and Internet of Things (IoT) devices have not been conveyed at this point, EdgeCloudSim [35] is a simulation environment, as a practically feasible experiment platform, which supports to mimic diverse IoT scenarios and Edge-Cloud architectures
In the Edge-Cloud environment, there are a number of IoT/ mobile devices that have a number of applications
Summary
There has been substantial growth in the number of connected devices in the digitalized era. The number of connected digital devices will have doubled between 2014 and 2019 [1] This disruptive time is known as the era of the Internet of Things (IoT), which has attracted the attention of both academia and industry. Each IoT device has a unique address and can communicate with other devices, which requires transferring, processing and storing the data generated by these devices. This immense growth requires platforms to support the increased amount of IoT devices as well as organize and process the produced data, since IoT devices are limited in terms of power and computational capabilities, i.e., Central Processing Unit (CPU) and memory [2]. Cloud servers store the data and provide parallelized capability of computation
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