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 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 />

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

Minimization of Energy Using Heuristic Resource Allocation and Migration for Cloud Computing

2021 ◽  
Vol 12 (1) ◽  
pp. 74-83
Author(s):  
Manjunatha S. ◽  
Suresh L.
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Large Scale ◽  
Data Centers ◽  
Service Providers ◽  
Cost Effective ◽  
Cooling Systems ◽  
Operational Costs ◽  
Cloud Computing Service ◽  
And Migration

Data center is a cost-effective infrastructure for storing large volumes of data and hosting large-scale service applications. Cloud computing service providers are rapidly deploying data centers across the world with a huge number of servers and switches. These data centers consume significant amounts of energy, contributing to high operational costs. Thus, optimizing the energy consumption of servers and networks in data centers can reduce operational costs. In a data center, power consumption is mainly due to servers, networking devices, and cooling systems, and an effective energy-saving strategy is to consolidate the computation and communication into a smaller number of servers and network devices and then power off as many unneeded servers and network devices as possible.

A User’s Profile Model for Cloud Computing Service

2013 ◽  
Vol 774-776 ◽  
pp. 1729-1733 ◽  
Author(s):  
Wu Bin Ma ◽  
Ming Xin Liu ◽  
Su Deng ◽  
Hong Bin Huang
Keyword(s):  
Cloud Computing ◽  
User Models ◽  
Profile Model ◽  
Computing Platform ◽  
Model Based ◽  
Cloud Computing Service ◽  
Cloud Computing Platform

Personalization of model-based cloud computing platform based on specialized user models has become more important in order to preserve the effectiveness of their service as the amount of available content increases. In this paper, a users profile model is imported to the cloud computing. We propose a method of modeling users profile for cloud computing, and establish a simple users profile. At last, we use this model in the Google App Engine for searching some service to prove that importing the users profile model is useful and efficient for cloud computing service.

A Trust Model of P2P in Cloud Computing Environment

2013 ◽  
Vol 278-280 ◽  
pp. 1962-1965
Author(s):  
Song Fei ◽  
Xiao Jing Wang ◽  
Zhe Cui
Keyword(s):  
Cloud Computing ◽  
Trust Model ◽  
Computing Environment ◽  
Trust Mechanism ◽  
Cloud Computing Environment ◽  
Computing Platform ◽  
Cloud Computing Service ◽  
Cross Platform ◽  
Cloud Computing Platform ◽  
P2p Technology

Proposed a new trust model based on P2P technology in the cloud computing environment. The model takes into account more than one cloud computing platform, that is, considering the different cloud computing service provider provide the service of a cross-cloud platform. Such cross-platform cloud (Cross Cloud) can be called the composite cloud computing platform or cloud associated cloud computing platform.The nodes in the cloud computing environment are divided into two categories: customers and providers. According to the different roles of these two nodes, we designed a different trust mechanism, to divide the trust domain with independent single cloud, considered node independence and manageability of domain to process trust choice and trust update, and proposed a new kind of cloud computing service - trust recommendation service.

Technique of Constructing Cloud Computing Platform Based on Ubuntu Enterprise Cloud

2012 ◽  
Vol 482-484 ◽  
pp. 713-716
Author(s):  
Ran Li ◽  
Jian Hua Fan ◽  
Xiao Bo Wang
Keyword(s):  
Cloud Computing ◽  
Service Provision ◽  
Private Cloud ◽  
Computing Platform ◽  
Cloud Computing Service ◽  
Cloud Server ◽  
Cloud Computing Platform ◽  
High Level ◽  
And Storage ◽  
Private Cloud Computing

Cloud computing is an effective approach for organizing computing resource and improving computing capability. In this paper, we designed and contstucted a private cloud computing platform based on ubuntu enterprise cloud. The cloud computing platform supports various clients dynamicly connet to cloud server and get cloud computing service via interface platform provided. Various kinds of virtual machine instance of different settings running on physical servers provide users computing and storage capability on demand. A series of high level functions guarantee computing service provision of platform.

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>

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