Computer
Ayman Basheer Yousif; Hassan Hassan Jaleel; Ghaida Muttasher
Abstract
Network traffic has risen in recent years to the point that it is obviously and astonishingly in 2020, with the increase predicted to double in the following days. Up to 23 Teraa bit every month is an incredible amount. The Active Queue Management (AQM) algorithm is one of the most significant study ...
Read More ...
Network traffic has risen in recent years to the point that it is obviously and astonishingly in 2020, with the increase predicted to double in the following days. Up to 23 Teraa bit every month is an incredible amount. The Active Queue Management (AQM) algorithm is one of the most significant study areas in network congestion control; nevertheless, new self-learning network management algorithms are needed on nodes to cope with the huge quantity of traffic and minimize queuing latency, used reinforcement learning for automatic adaptive parameter with the AQM algorithm for effective network management, and present a novel AQM algorithm that focuses on deep reinforcement learning to deal with latency and the trade-off between queuing delay and throughput; choose Deep Q-Network (DQN) as the foundation for our scheme and equate it with Random Early Detection (RED) Results based on Network simulation (NS3) simulation suggest that the DQN algorithm has good and better results were obtained from RED, where the difference reached a drop rate of 2%, and this percentage is considered good, in addition to the percentage of throughput and the packet transfer rate of 3% is better in the proposed algorithm.
Rawaa Ammar Razooqi; hassan Jaleel; Gaida Muttasher
Abstract
The Savvy Manufacturing plant could be a concept that communicates the conclusion goal of fabricating digitization. A Smart Factory, within the most common sense, profoundly digitized shop floor that collects and offers information persistently through associated computers, gadgets, and generations. ...
Read More ...
The Savvy Manufacturing plant could be a concept that communicates the conclusion goal of fabricating digitization. A Smart Factory, within the most common sense, profoundly digitized shop floor that collects and offers information persistently through associated computers, gadgets, and generations. In this work, the factory is represented by five types of sensors. The reading of the sensor values is sent to one of the Edge servers and cloud computing. One Edge server is selected based on calculating the time it takes for each server. The highest least time priority is chosen to receive the data coming from the sensors. This paper suggests a way to reduce the delay by using the edge server in addition to cloud computing by using methods that overcome any malfunction in one of the servers via another one that can work without the need to stop the factory systems.