scholarly journals Design and Implement of Astronomical Cloud Computing Environment In China-VO

2016 ◽  
Vol 12 (S325) ◽  
pp. 353-356
Author(s):  
Changhua Li ◽  
Chenzhou Cui ◽  
Linying Mi ◽  
Boliang He ◽  
Dongwei Fan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
National Development ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Large Scale Dataset ◽  
Storage Resource ◽  
The Mainland Of China ◽  
Academy Of Sciences ◽  
And Storage

AbstractAstronomy cloud computing environment is a cyber-Infrastructure for Astronomy Research initiated by Chinese Virtual Observatory (China-VO) under funding support from NDRC (National Development and Reform commission) and CAS (Chinese Academy of Sciences). Based on virtualization technology, astronomy cloud computing environment was designed and implemented by China-VO team. It consists of five distributed nodes across the mainland of China. Astronomer can get compuitng and storage resource in this cloud computing environment. Through this environments, astronomer can easily search and analyze astronomical data collected by different telescopes and data centers , and avoid the large scale dataset transportation.

scholarly journals Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Junshu Wang ◽  
Guoming Zhang ◽  
Wei Wang ◽  
Ka Zhang ◽  
Yehua Sheng
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Medical Service ◽  
Rapid Development ◽  
Medical Knowledge ◽  
Language Models ◽  
Computing Environment ◽  
Computing Power ◽  
Cloud Computing Environment ◽  
Proposed Model

AbstractWith the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.

scholarly journals A File Storage Service on a Cloud Computing Environment for Digital Libraries

2012 ◽  
Vol 31 (4) ◽  
pp. 34 ◽  
Author(s):  
Victor Jesus Sosa-Sosa ◽  
Emigdio M. Hernandez-Ramirez
Keyword(s):  
Cloud Computing ◽  
Digital Libraries ◽  
Data Availability ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Reference Guide ◽  
File Storage ◽  
Evaluation Of Performance ◽  
Storage Service ◽  
And Storage

This paper introduces a file storage service that is implemented on a private/hybrid cloud computing environment. The entire system was implemented using open source software. The characteristic of elasticity is supported by virtualization technologies allowing to increase and to decrease the computing and storage resources based on their demand. An evaluation of performance and resource consumption was made using several levels of data availability and fault tolerance. The set of modules included in this storage environment can be taken as a reference guide for IT staff that wants to have some experience building a modest cloud storage infrastructure.

Process virtualization of large-scale lidar data in a cloud computing environment

2013 ◽  
Vol 60 ◽  
pp. 109-116 ◽  
Author(s):  
Haiyan Guan ◽  
Jonathan Li ◽  
Liang Zhong ◽  
Yu Yongtao ◽  
Michael Chapman
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Lidar Data ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Process Virtualization

scholarly journals Cache optimization cloud scheduling (COCS) algorithm based on last level caches

2019 ◽  
Vol 8 (3) ◽  
pp. 184
Author(s):  
K. Vinod Kumar ◽  
Ranvijay Ranvijay
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Average Power ◽  
Cloud Services ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Cache Optimization ◽  
Dynamic Voltage ◽  
Increasing Demand ◽  
And Storage

<p><span>Recently, the utilization of cloud services like storage, various software, networking resources has extremely enhanced due to widespread demand of these cloud services all over the world. On the other hand, it requires huge amount of storage and resource management to accurately cope up with ever-increasing demand. The high demand of these cloud services can lead to high amount of energy consumption in these cloud centers. Therefore, to eliminate these drawbacks and improve energy consumption and storage enhancement in real time for cloud computing devices, we have presented Cache Optimization Cloud Scheduling (COCS) Algorithm Based on Last Level Caches to ensure high cache memory Optimization and to enhance the processing speed of I/O subsystem in a cloud computing environment which rely upon Dynamic Voltage and Frequency Scaling (DVFS). The proposed COCS technique helps to reduce last level cache failures and the latencies of average memory in cloud computing multi-processor devices. This proposed COCS technique provides an efficient mathematical modelling to minimize energy consumption. We have tested our experiment on Cybershake scientific dataset and the experimental results are compared with different conventional techniques in terms of time taken to accomplish task, power consumed in the VMs and average power required to handle tasks.</span></p>

A survey of prediction-based resource scheduling techniques for physics-based scientific applications

2018 ◽  
Vol 32 (25) ◽  
pp. 1850295 ◽  
Author(s):  
Gurleen Kaur ◽  
Anju Bala
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Scientific Applications ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Community Needs ◽  
Data Platform ◽  
Disciplinary Expertise ◽  
Resource Prediction ◽  
Real World Problems

The state-of-the-art physics alliances have augmented various opportunities to solve complex real-world problems. These problems require both multi-disciplinary expertise as well as large-scale computational experiments. Therefore, the physics community needs a flexible platform which can handle computational challenges such as volume of data, platform heterogeneity, application complexity, etc. Cloud computing provides an incredible amount of resources for scientific users on-demand, thus, it has become a potential platform for executing scientific applications. To manage the resources of Cloud efficiently, it is required to explore the resource prediction and scheduling techniques for scientific applications which can be deployed on Cloud. This paper discusses an extensive analysis of scientific applications, resource predictions and scheduling techniques for Cloud computing environment. Further, the trend of resource prediction-based scheduling and the existing techniques have also been studied. This paper would be helpful for the readers to explore the significance of resource prediction-based scheduling techniques for physics-based scientific applications along with the associated challenges.

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