Quality of Service
Quality of Service (QoS) research focuses on ensuring reliable and efficient delivery of services, primarily by predicting and optimizing performance metrics like latency, throughput, and reliability across diverse networks and applications. Current research emphasizes the use of machine learning, particularly deep learning models such as graph convolutional networks, tensor networks, and reinforcement learning, to improve QoS prediction accuracy and resource allocation in dynamic environments. These advancements are crucial for enhancing user experience in various domains, including web services, vehicular networks, and multimedia streaming, and for optimizing resource utilization in increasingly complex systems.
Papers
Environmental Awareness Dynamic 5G QoS for Retaining Real Time Constraints in Robotic Applications
Gerasimos Damigos, Akshit Saradagi, Sara Sandberg, George Nikolakopoulos
Towards Fair and Firm Real-Time Scheduling in DNN Multi-Tenant Multi-Accelerator Systems via Reinforcement Learning
Enrico Russo, Francesco Giulio Blanco, Maurizio Palesi, Giuseppe Ascia, Davide Patti, Vincenzo Catania
BERRY: Bit Error Robustness for Energy-Efficient Reinforcement Learning-Based Autonomous Systems
Zishen Wan, Nandhini Chandramoorthy, Karthik Swaminathan, Pin-Yu Chen, Vijay Janapa Reddi, Arijit Raychowdhury
Joint Service Caching, Communication and Computing Resource Allocation in Collaborative MEC Systems: A DRL-based Two-timescale Approach
Qianqian Liu, Haixia Zhang, Xin Zhang, Dongfeng Yuan