Machine-Learning-Based Load Balancing for Community Ice Code Component in CESM

2015 ◽  
pp. 79-91 ◽  
Author(s):  
Prasanna Balaprakash ◽  
Yuri Alexeev ◽  
Sheri A. Mickelson ◽  
Sven Leyffer ◽  
Robert Jacob ◽  
...  
Keyword(s):  
Machine Learning ◽  
Load Balancing

Machine learning for load balancing in the Linux kernel

2020 ◽  
Author(s):  
Jingde Chen ◽  
Subho S. Banerjee ◽  
Zbigniew T. Kalbarczyk ◽  
Ravishankar K. Iyer
Keyword(s):  
Machine Learning ◽  
Load Balancing ◽  
Linux Kernel

Troodon: A machine-learning based load-balancing application scheduler for CPU–GPU system

2019 ◽  
Vol 132 ◽  
pp. 79-94 ◽  
Author(s):  
Yasir Noman Khalid ◽  
Muhammad Aleem ◽  
Usman Ahmed ◽  
Muhammad Arshad Islam ◽  
Muhammad Azhar Iqbal
Keyword(s):  
Machine Learning ◽  
Load Balancing

scholarly journals APPLIED MACHINE LEARNING IN LOAD BALANCING

2020 ◽  
Vol 18 (2) ◽  
pp. 76
Author(s):  
Junaidi Junaidi ◽  
Prasetyo Wibowo ◽  
Dini Yuniasri ◽  
Putri Damayanti ◽  
Ary Mazharuddin Shiddiqi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Load Balancing ◽  
Applied Machine Learning

scholarly journals Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3779 ◽  
Author(s):  
Cesar Gomez ◽  
Abdallah Shami ◽  
Xianbin Wang
Keyword(s):  
Machine Learning ◽  
Load Balancing ◽  
Energy Cost ◽  
Smart Cities ◽  
Machine Learning Techniques ◽  
Delivery Ratio ◽  
Viable Solution ◽  
Markov Decision ◽  
Important Solution ◽  
Unbalanced Network

With the dramatic increase of connected devices, the Internet of things (IoT) paradigm has become an important solution in supporting dense scenarios such as smart cities. The concept of heterogeneous networks (HetNets) has emerged as a viable solution to improving the capacity of cellular networks in such scenarios. However, achieving optimal load balancing is not trivial due to the complexity and dynamics in HetNets. For this reason, we propose a load balancing scheme based on machine learning techniques that uses both unsupervised and supervised methods, as well as a Markov Decision Process (MDP). As a use case, we apply our scheme to enhance the capabilities of an urban IoT network operating under the LoRaWAN standard. The simulation results show that the packet delivery ratio (PDR) is increased when our scheme is utilized in an unbalanced network and, consequently, the energy cost of data delivery is reduced. Furthermore, we demonstrate that better outcomes are attained when some techniques are combined, achieving a PDR improvement of up to about 50% and reducing the energy cost by nearly 20% in a multicell scenario with 5000 devices requesting downlink traffic.

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