scholarly journals Online Learning Approach for Predictive Real-Time Energy Trading in Cloud-RANs

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2308
Author(s):  
Wan Nur Suryani Firuz Wan Wan Ariffin ◽  
Xinruo Zhang ◽  
Mohammad Reza Nakhai ◽  
Hasliza A. Rahim ◽  
R. Badlishah Ahmad
Keyword(s):  
Online Learning ◽  
Real Time ◽  
Energy Cost ◽  
Communication Systems ◽  
Learning Algorithm ◽  
Current Time ◽  
Energy Trading ◽  
Radio Access ◽  
Cost Efficient ◽  
Online Learning Algorithm

Constantly changing electricity demand has made variability and uncertainty inherent characteristics of both electric generation and cellular communication systems. This paper develops an online learning algorithm as a prescheduling mechanism to manage the variability and uncertainty to maintain cost-aware and reliable operation in cloud radio access networks (Cloud-RANs). The proposed algorithm employs a combinatorial multi-armed bandit model and minimizes the long-term energy cost at remote radio heads. The algorithm preschedules a set of cost-efficient energy packages to be purchased from an ancillary energy market for the future time slots by learning both from cooperative energy trading at previous time slots and by exploring new energy scheduling strategies at the current time slot. The simulation results confirm a significant performance gain of the proposed scheme in controlling the available power budgets and minimizing the overall energy cost compared with recently proposed approaches for real-time energy resources and energy trading in Cloud-RANs.

scholarly journals Efficient and Modularized Training on FPGA for Real-time Applications

2020 ◽  
Author(s):  
Shreyas Kolala Venkataramanaiah ◽  
Xiaocong Du ◽  
Zheng Li ◽  
Shihui Yin ◽  
Yu Cao ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Online Learning ◽  
Real Time ◽  
Learning Algorithm ◽  
Training Procedure ◽  
Tremendous Amount ◽  
Complex Training ◽  
Energy Constrained ◽  
Online Learning Algorithm ◽  
Training Modules

Training of deep Convolution Neural Networks (CNNs) requires a tremendous amount of computation and memory and thus, GPUs are widely used to meet the computation demands of these complex training tasks. However, lacking the flexibility to exploit architectural optimizations, GPUs have poor energy efficiency of GPUs and are hard to be deployed on energy-constrained platforms. FPGAs are highly suitable for training, such as real-time learning at the edge, as they provide higher energy efficiency and better flexibility to support algorithmic evolution. This paper first develops a training accelerator on FPGA, with 16-bit fixed-point computing and various training modules. Furthermore, leveraging model segmentation techniques from Progressive Segmented Training, the newly developed FPGA accelerator is applied to online learning, achieving much lower computation cost. We demonstrate the performance of representative CNNs trained for CIFAR-10 on Intel Stratix-10 MX FPGA, evaluating both the conventional training procedure and the online learning algorithm.

A Hierarchical RBF Online Learning Algorithm for Real-Time 3-D Scanner

2010 ◽  
Vol 21 (2) ◽  
pp. 275-285 ◽  
Author(s):  
S. Ferrari ◽  
F. Bellocchio ◽  
V. Piuri ◽  
N.A. Borghese
Keyword(s):  
Online Learning ◽  
Real Time ◽  
Learning Algorithm ◽  
Online Learning Algorithm

Convergence analysis of online learning algorithm with two-stage step size

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Weilin Nie ◽  
Cheng Wang
Keyword(s):  
Online Learning ◽  
Optimization Problems ◽  
Learning Algorithm ◽  
Computational Cost ◽  
Reproducing Kernels ◽  
Step Size ◽  
Two Stage ◽  
Logarithmic Term ◽  
Convergence Performance ◽  
Online Learning Algorithm

Abstract Online learning is a classical algorithm for optimization problems. Due to its low computational cost, it has been widely used in many aspects of machine learning and statistical learning. Its convergence performance depends heavily on the step size. In this paper, a two-stage step size is proposed for the unregularized online learning algorithm, based on reproducing Kernels. Theoretically, we prove that, such an algorithm can achieve a nearly min–max convergence rate, up to some logarithmic term, without any capacity condition.

scholarly journals ONLINE LEARNING FOR IMAGE PROCESSING IN NETWORKED SETTING

2017 ◽  
Vol 10 (13) ◽  
pp. 284
Author(s):  
Ankush Rai ◽  
Jagadeesh Kannan R
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Online Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
The Past ◽  
Slow Development ◽  
Online Learning Algorithm

In the past decade development of machine learning algorithm for network settings has witnessed little advancements owing to slow development of technologies for improving bandwidth and latency.  In this study we present a novel online learning algorithm for network based computational operations in image processing setting

Kernel Online Learning Algorithm With Scale Adaptation

2018 ◽  
Vol 65 (11) ◽  
pp. 1788-1792 ◽  
Author(s):  
Shiyuan Wang ◽  
Lujuan Dang ◽  
Badong Chen ◽  
Chengxiu Ling ◽  
Lidan Wang ◽  
...  
Keyword(s):  
Online Learning ◽  
Learning Algorithm ◽  
Scale Adaptation ◽  
Online Learning Algorithm
Sign in / Sign up

Export Citation Format

Share Document