PSO-VMD-Informer: A New Edge Load Prediction Method

In edge computing systems, efficient load prediction plays an important role in optimizing resource allocation and efficient utilization of edge service providers, as well as maintaining stable service quality. Due to the high dynamics of edge computing environments, the load data characteristics of...

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Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.174983-174995
Main Authors: Ye, Hengzhou, Tang, Peikang, Wen, Haoxiang, Li, Shiying
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Cloud computing
Computational modeling
Data models
Decomposition
Deep learning
Edge computing
Encoders-Decoders
Feature extraction
load forecasting
Load modeling
Particle swarm optimization
Predictive models
Resource allocation
Resource management
Servers
variational mode decomposition
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Summary:In edge computing systems, efficient load prediction plays an important role in optimizing resource allocation and efficient utilization of edge service providers, as well as maintaining stable service quality. Due to the high dynamics of edge computing environments, the load data characteristics of different edge servers are significantly different, which seriously affects the accuracy and generalization ability of load prediction. This paper proposes a new edge load prediction method, called PSO-VMD-Informer. This method first uses Variational Mode Decomposition (VMD) to decompose the edge load data sequence into stable intrinsic mode functions to effectively extract nonlinear features; then the Particle Swarm Optimization (PSO) algorithm is used to optimize the intrinsic mode function and penalty factor of VMD to improve the subsequence extraction quality and enhance the generalization ability of the model; finally, the Informer model is used to predict the edge load using the encoder-decoder architecture and self-attention mechanism. A large number of experimental results based on real edge load datasets show that compared with other mainstream methods, the PSO-VMD-Informer model shows the best performance on different edge server data and different prediction lengths, especially significantly outperforming other models in the MAE indicator. Compared with the other three mainstream methods, the proposed method reduces MAE, RMSE, and MAPE by 41.61%, 27.43%, and 11.21% respectively.
ISSN:2169-3536
DOI:10.1109/ACCESS.2024.3503584