Internet of things delay application driven measurement and optimization technology in edge computing environment

Abstract

Edge computing applications have the characteristics of huge scale and sensitive quality of service. However, due to the “long tail delay” problem of user access requests across the heterogeneous environment of edge networks, wide area networks and data centers, the quality of experience of edge users has seriously decreased. Therefore, a time reduction rule calculation algorithm based on Internet of Things (IoT) delay application-driven measurement mechanism is proposed, which can be applied to multi-source heterogeneous information fusion, big data fusion and information fusion security. The system architecture features of edge computing applications are reviewed, and the causes and classifications of long tail delays are analyzed. The main theories and methods of network delay measurement are introduced, and the optimization techniques for long tail delay are summarized. Finally, the online optimization operation environment is proposed thoughts and challenges. The research results show that the GXDGC algorithm proposed is effective for the application of driving measurement technology in IoT delay. Users’ access to online real-time big data needs to span complex heterogeneous network environments such as edge networks, wide area networks, data center networks, etc. Due to the superposition of delays, any increase in delays in online real-time big data processing will inevitably lead to end-to-end long-tail delays. Therefore, it is necessary to design an integrated optimization mechanism to control end-to-end online real-time big data network delays.