원문정보
초록
영어
Mobile edge computing (MEC) plays a crucial role in improving the performance of resource-constrained mobile devices by offloading computation-intensive tasks to nearby edge servers. However, existing methods often neglect the critical consideration of future task requirements when making offloading decisions. In this paper, we propose an innovative approach that addresses this limitation. Our method leverages recurrent neural networks (RNNs) to predict task sizes for future time slots. Incorporating this predictive capability enables more informed offloading decisions that account for upcoming computational demands. We employ genetic algorithms (GAs) to fine-tune fitness functions for current and future time slots to optimize offloading decisions. Our objective is twofold: minimizing total processing time and reducing energy consumption. By considering future task requirements, our approach achieves more efficient resource utilization. We validate our method using a real-world dataset from Google-cluster. Experimental results demonstrate that our proposed approach outperforms baseline methods, highlighting its effectiveness in MEC systems.
목차
1. INTRODUCTION
2. SYSTEM MODEL AND PROBLEM FORMULATION
2.1 System Model:
2.2 Communication Model:
2.3 Computational Model:
2.4 Problem Formulation:
3. METHODOLOGY
3.1 Task Size Prediction Model:
3.2 Genetic Algorithm for Task Offloading Decision-making:
4. EXPERIMENTAL RESULTS
4.1 Experimental Setup
4.2 Task Size Prediction Performance
4.3 Impact of Task Size:
4.4 Impact of Number of MDs:
5. CONCLUSION
ACKNOWLEDGEMENT
REFERENCES