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An energy-aware edge-cloud collaborative dynamic offload scheduling method

A dynamic unloading and energy consumption sensing technology, applied in the transmission system, electrical components, etc., can solve the problems of not considering the dependencies between tasks, not considering the energy consumption indicators of the Internet of Things scene, etc., and achieve the effect of minimizing energy consumption

Active Publication Date: 2021-09-21
SOUTH CHINA UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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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

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  • An energy-aware edge-cloud collaborative dynamic offload scheduling method
  • An energy-aware edge-cloud collaborative dynamic offload scheduling method
  • An energy-aware edge-cloud collaborative dynamic offload scheduling method

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Embodiment

[0098] An energy-aware edge-cloud collaborative dynamic offload scheduling method, such as figure 1 shown, including the following steps:

[0099] S1: Establish an edge-cloud collaborative system model with multiple IoT devices, an edge server, and a cloud data center;

[0100] Considering that an IoT service S needs to be executed in the edge-cloud collaborative system model, and the IoT service S needs K IoT devices for collaborative computing; the IoT service S is modeled as K fine-grained computing tasks distributed among K different IoT devices, There is data dependence between computing tasks of different IoT devices;

[0101] like figure 2 As shown, the edge-cloud collaborative system model includes K IoT devices, an edge server, and a remote cloud server; each IoT device has a computing task to be executed, and the set of computing tasks The computing tasks are preemptive and indivisible work units; IoT devices can communicate directly through D2D links to transmi...

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Abstract

The invention discloses an energy consumption-aware edge-cloud collaborative dynamic unloading scheduling method. The method includes: establishing an edge-cloud collaborative system model with multiple IoT devices, an edge server, and a cloud data center; constructing a communication model, a computing overhead model, and a task dependency model; constructing a cost function for task offloading decisions and its corresponding Constraint conditions; use reinforcement learning method to dynamically obtain task offloading strategy; dynamically schedule tasks according to the dependencies between tasks, and realize energy consumption-aware edge-cloud collaborative dynamic offloading scheduling. The present invention is used to solve the problem that the existing algorithm cannot effectively obtain the task offloading strategy in the Internet of Things scene where the state of the wireless network changes dynamically, and at the same time, the method of the present invention can effectively reduce the execution energy consumption of the computing 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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L29/08
CPCH04L67/10H04L67/1095H04L67/51H04L67/60
Inventor 刘发贵黄振希
Owner SOUTH CHINA UNIV OF TECH
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