Offloading Decision Optimization in the Cloud Edge Collaborative Computing Scenario of Smart City Early Warning System

To address the problems of insufficient computing and storage capacity of sensor devices (SD) and poor real-time processing of early warning data in smart city early warning system, a task offloading model of cloud-edge collaborative urban early-warning system was proposed based on edge computing technology. The cloud-edge collaborative cache strategy was introduced, and the delay model, energy consumption model and load imbalance model were designed in turn. The task offloading problem was transformed into a multi-objective optimization problem, and an offloading decision scheme based on MOEA/D was given, which was verified by comparative experiments. Results show that this offloading scheme can achieve load balancing with less total delay and total energy consumption, and is superior to other benchmark schemes.