scholarly journals VR-Cluster: Dynamic Migration for Resource Fragmentation Problem in Virtual Router Platform

2016 ◽  
Vol 2016 ◽  
pp. 1-14
Author(s):  
Xianming Gao ◽  
Baosheng Wang ◽  
Xiaozhe Zhang
Keyword(s):  
Resource Management ◽  
Network Architecture ◽  
High Performance ◽  
Network Virtualization ◽  
Mapping Algorithms ◽  
Mapping Algorithm ◽  
Advantages And Disadvantages ◽  
Cluster Dynamic ◽  
Resource Fragmentation ◽  
Management Plane

Network virtualization technology is regarded as one of gradual schemes to network architecture evolution. With the development of network functions virtualization, operators make lots of effort to achieve router virtualization by using general servers. In order to ensure high performance, virtual router platform usually adopts a cluster of general servers, which can be also regarded as a special cloud computing environment. However, due to frequent creation and deletion of router instances, it may generate lots of resource fragmentation to prevent platform from establishing new router instances. In order to solve “resource fragmentation problem,” we firstly propose VR-Cluster, which introduces two extra function planes including switching plane and resource management plane. Switching plane is mainly used to support seamless migration of router instances without packet loss; resource management plane can dynamically move router instances from one server to another server by using VR-mapping algorithms. Besides, three VR-mapping algorithms including first-fit mapping algorithm, best-fit mapping algorithm, and worst-fit mapping algorithm are proposed based on VR-Cluster. At last, we establish VR-Cluster protosystem by using general X86 servers, evaluate its migration time, and further analyze advantages and disadvantages of our proposed VR-mapping algorithms to solve resource fragmentation problem.

scholarly journals CGRA MODULO SCHEDULING FOR ACHIEVING BETTER PERFORMANCE AND INCREASED EFFICIENCY

2021 ◽  
Vol 12 (4) ◽  
pp. 1400-1413
Author(s):  
Siva Sankara Phani.T , Et. al.
Keyword(s):  
High Performance ◽  
High Energy ◽  
Coarse Grained ◽  
Modulo Scheduling ◽  
Stable Algorithm ◽  
Mapping Algorithms ◽  
Mapping Algorithm ◽  
Rearrangement Mechanism ◽  
Planning Algorithm ◽  
Better Than

Coarse-Grained Reconfigurable Architectures (CGRA) is an effective solution for speeding up computer-intensive activities due to its high energy efficiency and flexibility sacrifices. The timely implementation of CGRA loops was one of the hardest problems in the analysis. Modulo scheduling (MS) was productive in order to implement loops on CGRAs. The problem remains with current MS algorithms, namely to map large and irregular circuits to CGRAs over a fair period of compilation with restricted computational and high-performance routing tools. This is mainly due to an absence of awareness of major mapping limits and a time consuming approach to solving temporary and space-related mapping using CGRA buffer tools. It aims to boost the performance and robust compilation of the CGRA modulo planning algorithm. The problem with the CGRA MS is divided into time and space and the mechanisms between the two problems have to be reorganized. We have a detailed, systematic mapping fluid that addresses the algorithms of the time mapping problem with a powerful buffer algorithm and efficient connection and calculation limitations. We create a fast-stable algorithm for spatial mapping with a retransmission and rearrangement mechanism. With higher performance and quicker build-up time, our MS algorithm can map loops to CBGRA. The results show that, given the same compilation budget, our mapping algorithm results in a better rate for compilation. The performance of this method will be increased from 5% to 14%, better than the standard CGRA mapping algorithms available.

scholarly journals Towards a Generic Neural Network Architecture for Approximating Tone Mapping Algorithms

2021 ◽  
Vol 2021 (1) ◽  
pp. 93-96
Author(s):  
Jake McVey ◽  
Graham Finlayson
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Input Image ◽  
Tone Mapping ◽  
Neural Network Architecture ◽  
Mapping Algorithms ◽  
Mapping Algorithm ◽  
Processing Pipeline ◽  
The Neural Network ◽  
Number Of Iterations

Tone curves are a key feature in any image processing pipeline, and are used to change the pixel values of an input image to find an output image that looks better. Perhaps the most widely deployed tone curve algorithm is Contrast Limited Histogram Equalisation (CLHE). CLHE is an iterative algorithm that tone maps an input image so that the histogram of the output is (approximately) maximally uniform subject to the constraint that the tone curve has bounded slope (neither too large or too small).In this paper, we build upon a neural network framework [1] that was recently developed to deliver CLHE in fewer iterations (each layer in the neural network is analogous to a single iteration of CLHE, but the network has fewer layers than the number of iterations needed by CLHE). The key contribution of this paper is to show that the same network architecture can be used to implement a more complex (and more powerful) tone mapping algorithm. Experiments validate our method.

Nickel sulfide nanoworm network architecture as a binder-free high-performance non-enzymatic glucose sensor

2021 ◽  
Vol 188 (2) ◽  
Author(s):  
Alan Meng ◽  
Xiaocheng Hong ◽  
Haiqin Zhang ◽  
Wenli Tian ◽  
Zhenjiang Li ◽  
...  
Keyword(s):  
Network Architecture ◽  
High Performance ◽  
Nickel Sulfide ◽  
Glucose Sensor ◽  
Binder Free

scholarly journals Effective ion pathways and 3D conductive carbon networks in bentonite host enable stable and high-rate lithium–sulfur batteries

2021 ◽  
Vol 10 (1) ◽  
pp. 20-33
Author(s):  
Lian Wu ◽  
Yongqiang Dai ◽  
Wei Zeng ◽  
Jintao Huang ◽  
Bing Liao ◽  
...  
Keyword(s):  
Network Architecture ◽  
High Performance ◽  
Lithium Ion ◽  
Reversible Capacity ◽  
High Rate ◽  
Lithium Sulfur Batteries ◽  
Carbon Networks ◽  
Carbon Coated ◽  
Lithium Sulfur ◽  
Initial Capacity

Abstract Fast charge transfer and lithium-ion transport in the electrodes are necessary for high performance Li–S batteries. Herein, a N-doped carbon-coated intercalated-bentonite (Bent@C) with interlamellar ion path and 3D conductive network architecture is designed to improve the performance of Li–S batteries by expediting ion/electron transport in the cathode. The interlamellar ion pathways are constructed through inorganic/organic intercalation of bentonite. The 3D conductive networks consist of N-doped carbon, both in the interlayer and on the surface of the modified bentonite. Benefiting from the unique structure of the Bent@C, the S/Bent@C cathode exhibits a high initial capacity of 1,361 mA h g−1 at 0.2C and achieves a high reversible capacity of 618.1 m Ah g−1 at 2C after 500 cycles with a sulfur loading of 2 mg cm−2. Moreover, with a higher sulfur loading of 3.0 mg cm−2, the cathode still delivers a reversible capacity of 560.2 mA h g−1 at 0.1C after 100 cycles.

scholarly journals BengaliNet: A Low-Cost Novel Convolutional Neural Network for Bengali Handwritten Characters Recognition

2021 ◽  
Vol 11 (15) ◽  
pp. 6845
Author(s):  
Abu Sayeed ◽  
Jungpil Shin ◽  
Md. Al Mehedi Hasan ◽  
Azmain Yakin Srizon ◽  
Md. Mehedi Hasan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Character Recognition ◽  
High Performance ◽  
Low Cost ◽  
Traditional Learning ◽  
Neural Network Architecture ◽  
Handwritten Character Recognition ◽  
Handwritten Character

As it is the seventh most-spoken language and fifth most-spoken native language in the world, the domain of Bengali handwritten character recognition has fascinated researchers for decades. Although other popular languages i.e., English, Chinese, Hindi, Spanish, etc. have received many contributions in the area of handwritten character recognition, Bengali has not received many noteworthy contributions in this domain because of the complex curvatures and similar writing fashions of Bengali characters. Previously, studies were conducted by using different approaches based on traditional learning, and deep learning. In this research, we proposed a low-cost novel convolutional neural network architecture for the recognition of Bengali characters with only 2.24 to 2.43 million parameters based on the number of output classes. We considered 8 different formations of CMATERdb datasets based on previous studies for the training phase. With experimental analysis, we showed that our proposed system outperformed previous works by a noteworthy margin for all 8 datasets. Moreover, we tested our trained models on other available Bengali characters datasets such as Ekush, BanglaLekha, and NumtaDB datasets. Our proposed architecture achieved 96–99% overall accuracies for these datasets as well. We believe our contributions will be beneficial for developing an automated high-performance recognition tool for Bengali handwritten characters.

An Air Texturing Process for Hybridization of Different Reinforcement Filament Yarns by Commingling Process

2006 ◽  
Vol 532-533 ◽  
pp. 333-336 ◽  
Author(s):  
Bok Choon Kang ◽  
Chathura Nalendra Herath ◽  
Jong Kwang Park ◽  
Yong Hwang Roh
Keyword(s):  
High Performance ◽  
Process Development ◽  
Glass Fibers ◽  
Air Pressure ◽  
Practical Applications ◽  
Advantages And Disadvantages ◽  
Significant Parameter ◽  
Air Jet ◽  
Yarn Count ◽  
Air Nozzle

Carbon, aramid and glass fibers are inherently superior to conventional textile fibers in terms of mechanical properties and other characteristics. However, each material has its inherent advantages and disadvantages and it is usually recommended to hybridize them to fully benefit of their high performance in practical applications to many products. This paper is concerned with an air texturing process for hybridization of different reinforcement filament yarns. A normal air texturing machine was selected for process development and modified to suit testing purposes. The modified process for hybridization was introduced mainly in terms of air-jet nozzles employed in experiments. With the proposed air texturing process machine, three types of air-nozzle were applied to the experimental work. Three different filament materials were employed in experiments and they are carbon (CF), aramid (AF), and glass (GF). As matrix materials, polyether-ether (PEEK), polyester (PES), and polypropylene (PP) were selected and experimented. Hybrid yarns produced form the proposed process was evaluated optically in terms of bulkiness, arranging, breaking, and mixing, respectively. The experimental results were also summarized in terms of relationships between applied air pressure and yarn count, and variation in count. As a whole, it was concluded from the experiments that the proposed texturing process could be successfully applied to the practical hybridization of different reinforcement filament yarns. It was also revealed from the experiments that the air pressure in the proposed process is not a significant parameter on the pressing in terms of yarn count.

Best Practices for Building High Performance in Human Resource Management

2013 ◽  
Vol 33 (2) ◽  
pp. 39-50 ◽  
Author(s):  
Ketsaraporn Suttapong ◽  
Suwit Srimai ◽  
Pongsakorn Pitchayadol
Keyword(s):  
Resource Management ◽  
Best Practices ◽  
Human Resource Management ◽  
Human Resource ◽  
High Performance

scholarly journals Crafting Efficient Neural Graph of Large Entropy

2019 ◽  
Author(s):  
Minjing Dong ◽  
Hanting Chen ◽  
Yunhe Wang ◽  
Chang Xu
Keyword(s):  
Information Flow ◽  
Network Architecture ◽  
High Performance ◽  
Global Information ◽  
High Quality ◽  
Network Pruning ◽  
Initial Network ◽  
Trade Offs ◽  
Graph Properties ◽  
Deep Cnn

Network pruning is widely applied to deep CNN models due to their heavy computation costs and achieves high performance by keeping important weights while removing the redundancy. Pruning redundant weights directly may hurt global information flow, which suggests that an efficient sparse network should take graph properties into account. Thus, instead of paying more attention to preserving important weight, we focus on the pruned architecture itself. We propose to use graph entropy as the measurement, which shows useful properties to craft high-quality neural graphs and enables us to propose efficient algorithm to construct them as the initial network architecture. Our algorithm can be easily implemented and deployed to different popular CNN models and achieve better trade-offs.

Sign in / Sign up

Export Citation Format

Share Document