DNN task offloading method and terminal in an edge-cloud hybrid computing environment

Abstract

The present invention provides a DNN task offloading method and terminal in an edge-cloud hybrid computing environment, according to the type and number of computing nodes, the number of DNN tasks to be offloaded, and the number of layers of each DNN task to be offloaded , establish the objective function based on the minimization of the total cost, and determine the corresponding constraints, taking into account the influence of different types of nodes’ computing power, delay constraints and other conditions, to ensure the feasibility of the optimal solution obtained, when finding the optimal solution , also introduced the crossover operation and mutation operation in the genetic algorithm into the particle swarm algorithm and gave a specific algorithm, effectively avoiding the problem that the particle swarm algorithm is easy to fall into local optimum in the process of seeking the optimal solution.

Description

A DNN task offloading method and terminal in an edge-cloud hybrid computing environment technical field The present invention relates to the field of task offloading, particularly relate to a DNN task offloading under a kind of edge-cloud hybrid computing environment load method. Background technique [0002] In recent years, the number of intelligent applications has increased rapidly, among which DNN (Deep Neural Networks, deep neural network network) has achieved great success in many fields such as computer vision, speech recognition, natural language processing, etc. The model is huge, and the resources on the mobile device are limited. Large-scale DNN applications are often deployed on remote cloud servers. The distance between the server and the mobile device is long, and scheduling a large number of DNN applications to the remote cloud server will cause response time It is not easy to ensure the security of user data during long-distance transmission, and it ...

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Problems solved by technology

[0002] In recent years, the number of intelligent applications has increased rapidly. Among them, DNN (Deep Neural Networks, deep neural network) has achieved great success in many fields such as computer vision, speech recognition, and natural language processing. However, due to the huge model of DNN, mobile devices Due to the limited resources of the end, large-scale DNN applications are often deployed on remote cloud servers. Due to the long distance between the cloud server and the mobile device end, scheduling a large number of DNN applications to the remote cloud server will cause problems such as long response time and serious network congestion. It is not easy to guarantee the security of user data in long-distance transmission, which will inevitably lead to leakage of user privacy

[0003] After the emergence of edge computing, migrating DNN to edge nodes near the mobile device can greatly reduce the delay, and compared with the mobile device, the edge node has stronger computing power and advantages in computing resources, which can improve the execution performance of DNN applications , while reducing the overhead of the cloud server, it can also better protect user privacy. However, in the actual operation process, due to the complex hierarchical structure of DNN, the amount of data transmitted between layers and the complexity of computing tasks at different layers are different. Huge, and the edge network has a complex topology, which brings great difficulty to the deployment of DNN tasks and easily leads to higher system costs for edge computing

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