A survey on scheduling techniques in computing and network convergence
S Tang, Y Yu, H Wang, G Wang, W Chen… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …
computing power. This trend results in the urgent need for higher-level computing resource …
[HTML][HTML] IRATS: A DRL-based intelligent priority and deadline-aware online resource allocation and task scheduling algorithm in a vehicular fog network
Cloud computing platforms support the Internet of Vehicles, but the main bottlenecks are
high latency and massive data transmission in cloud-based processing. Vehicular fog …
high latency and massive data transmission in cloud-based processing. Vehicular fog …
[PDF][PDF] How reinforcement learning systems fail and what to do about it
P Hamadanian, M Schwarzkopf, S Sen - The 2nd Workshop on Machine …, 2022 - par.nsf.gov
Recent research has turned to Reinforcement Learning (RL) to solve challenging decision
problems, as an alternative to hand-tuned heuristics. RL can learn good policies without the …
problems, as an alternative to hand-tuned heuristics. RL can learn good policies without the …
[HTML][HTML] Alexander Mattick: Beam Tracking as a Time-Varying Reinforcement Learning Problem
C Mutschler - cmutschler.de
The envisioned transition to 5G and 6G technologies have started to transform the properties
of established communication networks. In the core of this transformation lies the capability …
of established communication networks. In the core of this transformation lies the capability …