Dynamic Topo-Semantic Network Learning (DTSNL)
Abstract
This paper proposes a novel approach to distributed system management called Dynamic Topo-Semantic Network Learning (DTSNL). DTSNL leverages reinforcement learning to enable systems to automatically discover and adapt to changes in underlying hardware and software topology. The core idea is to deploy agents, each responsible for a specific network node or resource, which learn both task execution and topological awareness. These agents utilize sensor data, logs, and monitoring information to observe and understand the network topology. The learning objective is to minimize communication latency, maximize resource utilization, and dynamically adjust routing and communication protocols to accommodate topological changes such as node failures, network congestion, or new node additions. A key component is a "topology-aware" reward function that incentivizes agents to learn sensitivity and adaptability to these changes. DTSNL represents a significant advancement over existing network learning methods that typically assume static topologies, offering a robust and self-optimizing solution for complex and dynamic network environments.
