Morpho: A decoupled MapReduce framework for elastic cloud computing

2014 ◽  
Vol 36 ◽  
pp. 80-90 ◽  
Author(s):  
Lu Lu ◽  
Xuanhua Shi ◽  
Hai Jin ◽  
Qiuyue Wang ◽  
Daxing Yuan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Mapreduce Framework

Algorithm of Text Categorization Based on Cloud Computing

2013 ◽  
Vol 311 ◽  
pp. 158-163 ◽  
Author(s):  
Li Qin Huang ◽  
Li Qun Lin ◽  
Yan Huang Liu
Keyword(s):  
Cloud Computing ◽  
Text Categorization ◽  
Experimental Results ◽  
Support Vector ◽  
Computing Environment ◽  
Mapreduce Framework ◽  
Cloud Computing Environment ◽  
Environment Map ◽  
Vector Machines ◽  
Parallel Text

MapReduce framework of cloud computing has an effective way to achieve massive text categorization. In this paper a distributed parallel text training algorithm in cloud computing environment based on multi-class Support Vector Machines(SVM) is designed. In cloud computing environment Map tasks realize distributing various types of samples and Reduce tasks realize the specific SVM training. Experimental results show that the execution time of text training decreases with the number of Reduce tasks increasing. Also a parallel text classifying based on cloud computing is designed and implemented, which classify the unknown type texts. Experimental results show that the speed of text classifying increases with the number of Map tasks increasing.

Integration Method for Information System with Cloud Computing

2011 ◽  
Vol 368-373 ◽  
pp. 3473-3476 ◽  
Author(s):  
Jie Liu ◽  
Xian Sheng Qin
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Information Integration ◽  
Medical Image ◽  
Integration Method ◽  
Image Data ◽  
Medical Data ◽  
Application Layer ◽  
Mapreduce Framework ◽  
Medical Image Data

This work proposes a method of information integration based Cloud Computing. Users can ask for services through application layer, using the open source Hadoop and implementing medical image data storage and analysis. The functional level of this system is on the basis of service. In our experiments, using the MapReduce framework, efficiently implement the DCM format medical data convert to JPEG format picture. We are working on the function to directly read the medical data which is stored in PACS.

Real Time Task Execution in Cloud Using MapReduce Framework

2017 ◽  
pp. 190-209 ◽  
Author(s):  
Sampa Sahoo ◽  
Bibhudatta Sahoo ◽  
Ashok Kumar Turuk ◽  
Sambit Kumar Mishra
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Data Storage ◽  
Fault Tolerant ◽  
Quality Attributes ◽  
Ease Of Use ◽  
Network Access ◽  
Mapreduce Framework ◽  
Huge Data ◽  
Mapreduce Scheduling

Cloud Computing era comes with the advancement of technologies in the fields of processing, storage, bandwidth network access, security of internet etc. The development of automatic applications, smart devices and applications, sensor based applications need huge data storage and computing resources and need output within a particular time limit. Now users are becoming more sensitive towards, delay in applications they are using. So, a scalable platform like Cloud Computing is required that can provide huge computing resource, and data storage required for processing such applications. MapReduce framework is used to process huge amounts of data. Data processing on a cloud based on MapReduce would provide added benefits such as fault tolerant, heterogeneous, ease of use, free and open, efficient. This chapter discusses about cloud system model, real-time MapReduce framework, Cloud based MapReduce framework examples, quality attributes of MapReduce scheduling and various MapReduce scheduling algorithm based on quality attributes.

Cloud-Computing: eHealth aus der Wolke

2018 ◽  
Vol 23 (11) ◽  
pp. 38-41
Author(s):  
Sebastian Krolop ◽  
Florian Benthin ◽  
Constanze Knahl
Keyword(s):  
Cloud Computing ◽  
Point Of Care

Cloud-Computing gewinnt auch in Kliniken zunehmend an Bedeutung. Über das Internet bereitgestellte Lösungen verändern nicht nur Verwaltung und Logistik – im klinischen Bereich geht es zum Beispiel um die Nutzung elektronischer Patientenakten am Point-of-Care.

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