Abstract:
The wide applications of edge computing has brought dawn to terminals with limited computing resources and energy supply. Terminal completes its task through computing of...Show MoreMetadata
Abstract:
The wide applications of edge computing has brought dawn to terminals with limited computing resources and energy supply. Terminal completes its task through computing offloading to reduce energy consumption and improve performance. In order to improve the efficiency of resource scheduling and utilization, this article proposes a delay-aware resource reservation strategy based on the prediction of spatial–temporal correlation task offloading demands. Due to the strong time-varying characteristics of terminal task offloading demand, this article designs a spatiotemporal task offloading demand prediction model (STOD). It divides the region into multiple subregions and models them as a graph structure so as to predict the task offloading demand of each region separately by considering the complex spatial and temporal dependencies of regional task offloading demands. With this model, we propose a regional edge server resource reservation (ESRR) algorithm to minimize the terminal task offloading delay. The experimental results based on the real data sets show that ESRR can reduce the average offloading time consumption by 30% under different scenarios and verify its feasibility and effectiveness.
Published in: IEEE Internet of Things Journal ( Volume: 10, Issue: 15, 01 August 2023)
Funding Agency:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Resource Reservation ,
- Energy Consumption ,
- Temporal Correlation ,
- Spatial Dependence ,
- Graph Structure ,
- Edge Server ,
- Resource Scheduling ,
- Regional Demand ,
- Task Offloading ,
- Demand Prediction ,
- Spatial-temporal Correlation ,
- Cloud Computing ,
- Recurrent Neural Network ,
- Attention Mechanism ,
- Time Slot ,
- Traffic Flow ,
- Demand For Resources ,
- Prediction Intervals ,
- Graph Convolutional Network ,
- Traffic Data ,
- Roadside Units ,
- Amount Of Tasks ,
- Spatiotemporal Correlation ,
- Graph Convolution ,
- Dijkstra’s Algorithm ,
- Dynamic Correlation ,
- Demand Forecasting ,
- Multiple Graphs ,
- Number Of Terminals ,
- Smart Transportation
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Resource Reservation ,
- Energy Consumption ,
- Temporal Correlation ,
- Spatial Dependence ,
- Graph Structure ,
- Edge Server ,
- Resource Scheduling ,
- Regional Demand ,
- Task Offloading ,
- Demand Prediction ,
- Spatial-temporal Correlation ,
- Cloud Computing ,
- Recurrent Neural Network ,
- Attention Mechanism ,
- Time Slot ,
- Traffic Flow ,
- Demand For Resources ,
- Prediction Intervals ,
- Graph Convolutional Network ,
- Traffic Data ,
- Roadside Units ,
- Amount Of Tasks ,
- Spatiotemporal Correlation ,
- Graph Convolution ,
- Dijkstra’s Algorithm ,
- Dynamic Correlation ,
- Demand Forecasting ,
- Multiple Graphs ,
- Number Of Terminals ,
- Smart Transportation
- Author Keywords