Network performance improvement through differentiated survivability services in WDM networks

2008 ◽  
Vol 7 (6) ◽  
pp. 564 ◽  
Author(s):  
George Markidis ◽  
Anna Tzanakaki
Keyword(s):  
Performance Improvement ◽  
Network Performance ◽  
Wdm Networks

Network performance improvement in survivable WDM networks considering physical layer constraints

2009 ◽  
Author(s):  
Anna Tzanakaki ◽  
Kostas Georgakilas ◽  
Kostas Katrinis ◽  
Lena Wosinska ◽  
Amornrat Jirattigalachote ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Network Performance ◽  
Physical Layer ◽  
Wdm Networks

Power savings versus network performance in dynamically provisioned WDM networks

2012 ◽  
Vol 50 (5) ◽  
pp. 48-55 ◽  
Author(s):  
Pawel Wiatr ◽  
Paolo Monti ◽  
Lena Wosinska
Keyword(s):  
Network Performance ◽  
Wdm Networks

scholarly journals Multistructure-Based Collaborative Online Distillation

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 357
Author(s):  
Liang Gao ◽  
Xu Lan ◽  
Haibo Mi ◽  
Dawei Feng ◽  
Kele Xu ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Network Performance ◽  
State Of The Art ◽  
Autonomous Driving ◽  
Supplementary Information ◽  
Training Method ◽  
Machine Learning Methods ◽  
Information Diversity ◽  
Knowledge Distillation ◽  
Memory Resources

Recently, deep learning has achieved state-of-the-art performance in more aspects than traditional shallow architecture-based machine-learning methods. However, in order to achieve higher accuracy, it is usually necessary to extend the network depth or ensemble the results of different neural networks. Increasing network depth or ensembling different networks increases the demand for memory resources and computing resources. This leads to difficulties in deploying depth-learning models in resource-constrained scenarios such as drones, mobile phones, and autonomous driving. Improving network performance without expanding the network scale has become a hot topic for research. In this paper, we propose a cross-architecture online-distillation approach to solve this problem by transmitting supplementary information on different networks. We use the ensemble method to aggregate networks of different structures, thus forming better teachers than traditional distillation methods. In addition, discontinuous distillation with progressively enhanced constraints is used to replace fixed distillation in order to reduce loss of information diversity in the distillation process. Our training method improves the distillation effect and achieves strong network-performance improvement. We used some popular models to validate the results. On the CIFAR100 dataset, AlexNet’s accuracy was improved by 5.94%, VGG by 2.88%, ResNet by 5.07%, and DenseNet by 1.28%. Extensive experiments were conducted to demonstrate the effectiveness of the proposed method. On the CIFAR10, CIFAR100, and ImageNet datasets, we observed significant improvements over traditional knowledge distillation.

Increasing genetics referrals: The National Cancer Institute Community Cancer Centers Program (NCCCP) network performance improvement project.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 111-111
Author(s):  
Jeffrey Cohn ◽  
Angela Carrigan ◽  
Selvi Palaniappan ◽  
Silvana Rivero ◽  
Kathleen M. Castro
Keyword(s):  
Genetic Counseling ◽  
Data Collection ◽  
Performance Improvement ◽  
Network Performance ◽  
Significant Group ◽  
Improvement Project ◽  
Cancer Centers ◽  
Sustainable Improvement ◽  
Site Staff ◽  
Improvement Interventions

111 Background: A NCCCP goal is to improve quality of care through increasing guideline adherence at participating community cancer centers. 14 NCCCP sites participated in a performance improvement project to (1) increase genetics referral and counseling services by 10% in accordance with established guidelines for breast or colorectal cancer. The project hypothesized that data collection and feedback improves referral rates by better targeting eligible populations for improvement interventions. Methods: Sites recorded the source and outcome of each breast/colon referral using a tool developed by the NCCCP. Aggregate data were shared on monthly project calls where site staff shared best practices/issues with peers and received feedback. Results: Data were collected Jul 2011-Apr 2012 and analyzed at 2-week intervals. The number of colorectal and breast patients referred for genetics counseling remained stable through the 9-month period. Despite no change in the rates of referrals the data were hypotheses generating. Colorectal cases of all ages were being correctly referred in accordance with established guidelines; project calls demonstrated sites’ efforts to improve referral processes, such as including genetics counselors in the multidisciplinary team discussions, implementing universal screening for Lynch syndrome, and developing new linkages with referring staff. Conclusions: The project was a successful proof of concept study showing the feasibility of pooling genetic counseling data across diverse community cancer centers. Robust data collection is inadequate to drive sustainable improvement; specific process changes are also required. Future NCCCP collaborative initiatives are needed to ensure all eligible patients have access to genetic counseling referral services as there remain a significant group of patients eligible for genetic counseling who are not being seen. Funded by HHSN261200800001E.

Locality-Route Pre-Configuration Mechanism for Latency Optimization in NoCs

2014 ◽  
Vol 571-572 ◽  
pp. 381-388
Author(s):  
Xian Tuo Tang ◽  
Guang Fu Zeng ◽  
Feng Wang ◽  
Zuo Cheng Xing ◽  
Chao Chao Feng
Keyword(s):  
Performance Improvement ◽  
Network Performance ◽  
Input Port ◽  
Network Simulator ◽  
Communication Performance ◽  
Spatial Locality ◽  
Temporal And Spatial ◽  
Splash 2

By exploiting communication temporal and spatial locality represented in actual applications, the paper proposes a locality-route pre-configuration mechanism (i.e. LRPC) on top of the Pseudo-Circuit scheme, to further accelerate network performance. Under the original Pseudo-circuit scheme, LRPC attempts to preconfigure another sharable crossbar connection at each input port within a single router when the pseudo circuit is invalid currently, so as to produce more available sharable route for packets transfer, and hence to enhance the reusability of the sharable route as well as communication performance. Our evaluation results using a cycle-accurate network simulator with traces from Splash-2 Benchmark show 5.4% and 31.6% improvement in overall network performance compared to Pseudo-Circuit and BASE_LR_SPC routers, respectively. Evaluated with synthetic workload traffic, at most 10.91% and 33.72% performance improvement can be achieved by the LRPC router under the Uniform-random, Bit-complement and Transpose traffic as compared to Pseudo-Circuit and BASE_LR_SPC routers.

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