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Internet of Things service unloading decision-making methods based on edge computing and deep reinforcement learning

A decision-making method and reinforcement learning technology, applied in computing, program control design, multi-program device, etc., can solve problems such as insufficient change sensitivity and lack of dynamic adaptability, and achieve excellent performance, strong resource management and control arrangement capabilities, and stability good sex effect

Pending Publication Date: 2020-09-08
STATE GRID CORP OF CHINA +1
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  • Claims
  • Application Information

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Problems solved by technology

However, the unloading path is not sensitive enough to the change of the c

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  • Internet of Things service unloading decision-making methods based on edge computing and deep reinforcement learning
  • Internet of Things service unloading decision-making methods based on edge computing and deep reinforcement learning
  • Internet of Things service unloading decision-making methods based on edge computing and deep reinforcement learning

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[0029] First of all, it should be noted that the service offloading in the embodiment of the present invention is a kind of computing offloading, which refers to allocating intelligent service tasks with a large amount of computation to one or more computing nodes with sufficient computing resources for processing, and then distributing the computing tasks to one or more computing nodes that have sufficient computing resources for processing. Some computing results are retrieved from the computing nodes and computing resources are released. In the prior art, computing nodes are generally proxy servers. The computing offloading technology is first applied to Mobile Cloud Computing (MCC), and in some mobile edge computing (MEC), the decision of computing offloading can have the following three schemes. 1. local execution: the entire computation is done locally in the UE; 2. full offloading: the entire computation is offloaded and processed by the MEC; 3. partial offloading: a pa...

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Abstract

The invention provides Internet of Things service unloading decision-making methods based on edge computing and deep reinforcement learning, and belongs to the technical field of internet-of-things application. According to the Internet of Things service unloading decision-making methods, the Internet of Things is constructed into SDIoT containing a plurality of regions, wherein the regions comprise region SDN controllers configured with service unloading decision-making models, and the region SDN controllers output service unloading decisions of an intelligent service in the regions accordingto the configured service unloading decision-making models. According to the invention, the inherent network structuralization problem in the traditional IoT is solved, the SDN technology is utilizedto separate a network control plane from a data forwarding plane, the whole network centralized view is obtained, the network security performance is ensured, and the powerful resource management andcontrol arrangement capability brought by a programmable interface is further obtained.

Description

technical field [0001] The invention belongs to the technical field of Internet of Things application, and in particular relates to an Internet of Things service offloading decision-making method combined with edge computing, SDN technology and deep reinforcement learning algorithm. Background technique [0002] With the rapid development of the Internet of Things (IoT) and the continuous emergence of various emerging applications, such as smart home, smart city, and smart transportation, users have higher and higher requirements for network quality of service (QoS). Cloud computing has powerful computing capabilities. Devices can offload computing tasks and transmit computing tasks to remote cloud servers for execution, thereby achieving the purpose of alleviating computing and storage limitations and prolonging the battery life of devices. However, offloading computing tasks to cloud servers cannot meet the needs of latency-sensitive services. For this reason, edge comput...

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Application Information

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IPC IPC(8): H04L29/08G06F9/50G16Y40/20
CPCH04L67/1031G06F9/5072G06F9/5027G16Y40/20H04L67/1001
Inventor 胡文建苏汉张益辉赵会峰何利平李霞孙玲张颖陈瑞华郭家伟马岩杨宇皓徐良燕吴晓云孙静陈方赵灿王琳王珂王飞杨阳郭思炎王代远孙莹晖张郁张伟吴涛
Owner STATE GRID CORP OF CHINA
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