Cost Model Based Configuration Management Policy in OBS Networks

2005 ◽  
pp. 57-66
Author(s):  
Hyewon Song ◽  
Sang-Il Lee ◽  
Chan-Hyun Youn
Keyword(s):  
Cost Model ◽  
Configuration Management ◽  
Management Policy ◽  
Model Based

Configuration management policy based on cost model in OBS networks

2005 ◽  
Author(s):  
Hye-Won Song ◽  
Sang-Il Lee ◽  
Dong-Wook Kang ◽  
Chari-Hyun Youn
Keyword(s):  
Cost Model ◽  
Configuration Management ◽  
Management Policy

Scalable model-based configuration management of security services in complex enterprise networks

2010 ◽  
Vol 41 (3) ◽  
pp. 307-338 ◽  
Author(s):  
João Porto de Albuquerque ◽  
Heiko Krumm ◽  
Paulo Lício de Geus ◽  
René Jeruschkat
Keyword(s):  
Configuration Management ◽  
Enterprise Networks ◽  
Security Services ◽  
Model Based

An integrated full cost model based on extended exergy accounting toward sustainability assessment of industrial production processes

2019 ◽  
Vol 21 (10) ◽  
pp. 1993-2004 ◽  
Author(s):  
Weiqing Meng ◽  
Beibei Hu ◽  
Nan Sun ◽  
Xunqiang Mo ◽  
Mengxuan He ◽  
...  
Keyword(s):  
Industrial Production ◽  
Sustainability Assessment ◽  
Cost Model ◽  
Full Cost ◽  
Model Based ◽  
Production Processes

QoS-constraint Configuration Management Policy in Grid over GMPLS Networks

2006 ◽  
Author(s):  
Hyewon Song ◽  
Chan-hyun Youn ◽  
Changhee Han ◽  
Dong Nam ◽  
Gwang-ja Jin ◽  
...  
Keyword(s):  
Configuration Management ◽  
Management Policy

scholarly journals Expedited Tensor Program Compilation Based on LightGBM

2021 ◽  
Vol 2078 (1) ◽  
pp. 012019
Author(s):  
Gonghan Liu ◽  
Yue Li ◽  
Xiaoling Wang
Keyword(s):  
Deep Learning ◽  
Cost Model ◽  
Effective Solution ◽  
Learning Framework ◽  
Model Based ◽  
Deep Networks ◽  
Speed Up ◽  
Real Hardware

Abstract If the traditional deep learning framework needs to support a new operator, it usually needs to be highly optimized by experts or hardware vendors to be usable in practice, which is inefficient. The deep learning compiler has proved to be an effective solution to this problem, but it still suffers from unbearably long overall optimization time. In this paper, aiming at the XGBoost cost model in Ansor, we train a cost model based on LightGBM algorithm, which accelerates the optimization time without compromising the accuracy. Experimentation with real hardware shows that our algorithm provides 1.8× speed up in optimization over XGBoost, while also improving inference time of the deep networks by 6.1 %.

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