Energy consumption perception-based side cloud collaborative dynamic unloading scheduling method

Abstract

The invention discloses an energy consumption perception-based side cloud collaborative dynamic unloading scheduling method. The method comprises the following steps: establishing an edge cloud collaborative system model with a plurality of IoT devices, an edge server and a cloud data center; constructing a communication model, a calculation overhead model and a task dependence model; constructinga cost function of the task unloading decision and a corresponding constraint condition thereof; dynamically obtaining a task unloading strategy by using a reinforcement learning method; and dynamically scheduling the tasks according to the dependency relationship among the tasks to realize energy consumption perception-based side cloud collaborative dynamic unloading scheduling. The method is used for solving the problem that a conventional algorithm cannot effectively obtain a task unloading strategy in an Internet of Things scene with a dynamically changed wireless network state, and meanwhile, the method can effectively reduce the execution energy consumption of a calculation task.

Description

technical field [0001] The invention belongs to the technical field of computer applications, and in particular relates to an energy consumption-aware edge-cloud collaborative dynamic unloading scheduling method. Background technique [0002] With the rapid development of the Internet of Things, more and more devices are connected through the Internet of Things technology and thus generate massive data and demands. However, due to the limited battery life of most Internet of Things (IoT) devices, constrained computing resources cannot meet the demands of increasingly complex applications. Edge computing is considered as a promising computing paradigm to solve this critical problem. In edge computing, computing resources are deployed close to devices, such as smart gateways, access points, base stations, etc., and integrated as edge servers. Resource-constrained devices can offload computing tasks to edge servers through single-hop wireless transmission, and then edge serve...

AI Technical Summary

Problems solved by technology

Another example, the document "A Method and Device for Dynamic Offloading of Internet of Things Services Based on Edge Computing" (CN109510869A) proposes a method to adapt to the dynamic arrival of Internet of Things services to maximize the offloading revenue, but this method does not consider the Internet of Things scenario The common inter-task dependencies in the Internet of Things (IoT) scenarios do not take into account the vital energy consumption indicators in the IoT scenario

Images