Holistic Traffic Control Through Q-Learning and Enhanced Deep Learning for Distributed Co-Inference
Year: 2024
Type: article
Abstract: Services for AI tasks have garnered a lot of attention as an integral aspect of intelligent services in the new era.However, implementing such a system in a stable and distributed manner, while simultaneously coordinating the use of cloud computing and remote edge devices, is challenging due to the pressing need for energy and computing resources.The primary contribution of this study lies in the development of a distributed co-inference architecture that harnesses the collective intelligence of interconnected agents to optimize traffic flow in real-time.By combining Q-learning with enhanced deep learning, our approach enables traffic signals and routing decisions to adapt dynamically to changing traffic patterns and environmental conditions.The security, responsiveness, and dependability of intelligent systems deployed close to end-users are improved by deploying deep learning systems.Another critical aspect where latency and accuracy in models are traded off is deep learning model optimization.Finding the best offloading policy and model for deep learning services requires an end-to-end decision-making solution that takes into account computation-communication problems.This study presents a holistic network optimization approach for scheduling AI services based on artificial intelligence.By adjusting for differences in computational resources and network congestion, the suggested deep Q-learning technique maximizes the throughput of AI tasks in general.This research introduces a virtual queue for analyzing the system's Lyapunov stability and employs a multi-hop Directed Acyclic Graph (DAG) to explain Q-learning of Reinforcement learning-based co-inference network topology.To optimize the total task processing rate, the study develops an Optimized self-adaptive glow worm swarm optimization method (SA-GSO) based on deep Q-learning.It then proposes a prioritybased data forwarding approach for efficiency.The study concludes by simulating the distributed co-inference system's platform.We attest to the superiority of our idea by comparing it to other standards.
DOI: 10.18280/jesa.570215
Authors: Suryachandra Palli, Ghamya Kotapati, Kranthi Kumar Lella, Jagadeeswara Rao Palisetti, Dorababu Sudarsa, Syed Ziaur Rahman, Ramesh Vatambeti
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4395958656