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About Lecture 13 Task Offloading Based On LSTM Prediction and Deep Reinforcement Learning
The document proposes an Online Predictive Offloading (OPO) algorithm based on Deep Reinforcement Learning and Long Short-Term Memory networks to solve the task offloading problem in edge computing. The algorithm models the problem as minimizing long-term costs from latency and energy consumption. It uses an LSTM network to predict task and edge server load characteristics in real-time, improving the convergence of the DRL algorithm. The DRL decision model then allocates resources in advance based on the predictions to reduce latency and improve offloading performance.
Detailed Information
| Author: | ['Dipesh Agrawal'] |
|---|---|
| Publication Year: | 2024 |
| Pages: | 30 |
| Language: | English |
| Format: | |
| Price: | FREE |
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