A Cloud Computing Platform for Automatic Blotch Detection in Large Scale Old Media Archives

2015 ◽  
pp. 519-528
Author(s):  
Kiok Ahn ◽  
Mingi Kim ◽  
Md. Monirul Hoque ◽  
Oksam Chae
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Computing Platform ◽  
Cloud Computing Platform ◽  
Blotch Detection

Predicting Node Failures in an Ultra-Large-Scale Cloud Computing Platform

2020 ◽  
Vol 29 (2) ◽  
pp. 1-24
Author(s):  
Yangguang Li ◽  
Zhen Ming (Jack) Jiang ◽  
Heng Li ◽  
Ahmed E. Hassan ◽  
Cheng He ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Computing Platform ◽  
Cloud Computing Platform ◽  
Node Failures

Batch Job Based Auto-Scaling System on Cloud Computing Platform

2013 ◽  
Vol 756-759 ◽  
pp. 2386-2390
Author(s):  
Yuan Yuan Guo ◽  
Jing Li ◽  
Xin Chun Liu ◽  
Wei Wei Wang
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Information Science ◽  
Service Providers ◽  
Setup Time ◽  
Affecting Factors ◽  
Computing Platform ◽  
Cloud Computing Service ◽  
Cloud Computing Platform ◽  
Auto Scaling

With the quick development of information science, it becomes much harder to deal with a large scale of data. In this case, cloud computing begins to become a hot topic as a new computing model because of its good scalability. It enables customers to acquire and release computing resources from and to the cloud computing service providers according to current workload. The scaling ability is achieved by system automatically according to auto scaling policies reserved by customers in advance, and it can greatly decrease users operating burden. In this paper, we proposed a new architecture of auto-scaling system, used auto-scaling technology on batch jobs based system and considered tasks deadlines and VM setup time as affecting factors on auto-scaling policy besides substrate resource utilities.

scholarly journals A Cloud Computing Platform for Scalable Relative and Absolute Binding Free Energy Prediction: New Opportunities and Challenges for Drug Discovery

2020 ◽  
Author(s):  
Zhixiong Lin ◽  
Junjie Zou ◽  
Chunwang Peng ◽  
Shuai Liu ◽  
Zhipeng Li ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Free Energy ◽  
Drug Discovery ◽  
Large Scale ◽  
Binding Free Energy ◽  
Energy Prediction ◽  
Biological Targets ◽  
Computing Platform ◽  
Cloud Computing Platform ◽  
Hit Identification

<p>Free energy perturbation (FEP) has become widely used in drug discovery programs for binding affinity prediction between candidate compounds and their biological targets. Simultaneously limitations of FEP applications also exist, including but not limited to, the high cost, long waiting time, limited scalability and application scenarios. To overcome these problems, we have developed a scalable cloud computing platform (XFEP) for both relative and absolute free energy predictions with refined simulation protocols. XFEP enables large-scale FEP calculations in a more efficient, scalable and affordable way, e.g. the evaluation of 5,000 compounds can be performed in one week using 50-100 GPUs with a computing cost approximately corresponding to the cost for one new compound synthesis. Together with artificial intelligence (AI) techniques for goal-directed molecule generation and evaluation, new opportunities can be explored for FEP applications in the drug discovery stages of hit identification, hit-to-lead, and lead optimization with R-group substitutions, scaffold hopping, and completely different molecule evaluation. We anticipate scalable FEP applications will become widely used in more drug discovery projects to speed up the drug discovery process from hit identification to pre-clinical candidate compound nomination. </p>

scholarly journals An e-book hub service based on a cloud platform

2012 ◽  
Vol 13 (5) ◽  
pp. 39 ◽  
Author(s):  
Jinn-Shing Cheng ◽  
Echo Huang ◽  
Chuan-Lang Lin
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Information And Communication Technologies ◽  
Large Scale ◽  
Storage Capacity ◽  
Digital Content ◽  
Computing Platform ◽  
Cloud Computing Service ◽  
E Learning ◽  
Cloud Computing Platform

Due to the constant performance upgrades and regular price reductions of mobile devices in recent years, users are able to take advantage of the various  devices to obtain digital content regardless of the limitations of time and place. The increasing use of e-books has stimulated new e-learning approaches. This research project developed an e-book hub service on a cloud computing platform in order to overcome the limitations of computing capability and storage capacity that are inherent in many mobile devices. The e-book hub service also allows users to automatically adjust the rendering of multimedia pages at different resolutions on terminal units such as smartphones, tablets, PCs, and so forth. We implemented an e-book hub service on OpenStack, which is a free and open-source cloud computing platform supported by multiple large firms. The OpenStack platform provides a large-scale distributed computing environment that allows users to build their own cloud systems in a public, private, or hybrid environment. Our e-book hub system offers content providers an easy-to-use cloud computing service with unlimited storage capacity, fluent playback, high usability and scalability, and high security characteristics to produce, convert, and manage their e-books. The integration of information and communication technologies has led the traditional publishing industry to new horizons with abundant digital content publications. Results from this study may help content providers create a new service model with increased profitability and enable mobile device users to easily get digital content, thereby achieving the goal of e-learning.<br /><br />

scholarly journals Practical Implementation of New Algorithm for Restricting Data Fusion in Cloud Computing with Use of Information Kalman Filtering

2021 ◽  
Vol 15 ◽  
pp. 115-121
Author(s):  
Mohamadreza Mohamadzadeh
Keyword(s):  
Cloud Computing ◽  
Kalman Filter ◽  
Kalman Filtering ◽  
Large Scale ◽  
Practical Implementation ◽  
Computing Platform ◽  
Information Filter ◽  
Hardware Configuration ◽  
Cloud Computing Platform ◽  
Large Scale Networks

These days’ lots of technologies migrate from traditional systems into cloud and similar technologies; also we should note that cloud can be used for military and civilian purposes [3]. On the other hand, in such a large scale networks we should consider the reliability and powerfulness of such networks in facing with events such as high amount of users that may login to their profiles simultaneously, or for example if we have the ability to predict about what times that we would have the most crowd in network, or even users prefer to use which part of the Cloud Computing more than other parts – which software or hardware configuration. With knowing such information, we can avoid accidental crashing or hanging of the network that may be cause by logging of too much users. In this paper we propose Kalman Filter that can be used for estimating the amounts of users and software’s that run on cloud computing or other similar platforms at a certain time. After introducing this filter, at the end of paper, we talk about some potentials of this filter in cloud computing platform. In this paper we demonstrate about how we can use Kalman filter in estimating and predicting of our target, by the means of several examples on Kalman filter. Also at the end of paper we propose information filter for estimation and prediction about cloud computing resources.

Sign in / Sign up

Export Citation Format

Share Document