Summary of Fully Distributed Fog Load Balancing with Multi-agent Reinforcement Learning, by Maad Ebrahim and Abdelhakim Hafid
Fully Distributed Fog Load Balancing with Multi-Agent Reinforcement Learning
by Maad Ebrahim, Abdelhakim Hafid
First submitted to arxiv on: 15 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a fully distributed load-balancing solution using Multi-Agent Reinforcement Learning (MARL) to optimize waiting times and resource utilization in Fog networks. The MARL agents leverage transfer learning for life-long self-adaptation to dynamic changes, outperforming centralized solutions and baselines by minimizing end-to-end execution delay. Additionally, the paper explores the impact of a realistic frequency on environmental state observation, highlighting the trade-off between realism and performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in the Internet of Things (IoT). As more devices connect to the internet, we need faster ways to process data. Fog Computing is a solution that makes resources available across different locations. But we also need to manage these resources efficiently so that all devices can access them quickly. The authors propose a new way to do this using something called Multi-Agent Reinforcement Learning (MARL). This method helps distribute workload more evenly and faster than before. It’s like having many smart helpers working together to get things done. |
Keywords
» Artificial intelligence » Reinforcement learning » Transfer learning
