2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)

2020 ◽  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Social Computing ◽  
Distributed Processing ◽  
Parallel Distributed Processing

Handling Critical Issues of Big Data on Cloud

2016 ◽  
pp. 100-131 ◽  
Author(s):  
Madhavi Vaidya
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Distributed Processing ◽  
Processing System ◽  
Large Data ◽  
Data Sets ◽  
Complex Data ◽  
Data Scaling ◽  
Critical Issues ◽  
Hardware Platforms

Big Data is driving radical changes in traditional data analysis platforms. To perform any kind of analysis on such voluminous and complex data, scaling up the hardware platforms becomes impending. With the entire buzz surrounding Big Data; it is being collected at an unprecedented scale. Big Data has potential to revolutionize much more than just research. Loading large data-sets is often a challenge. Another shift of this Big Data processing is the move towards cloud computing. As many communities begin to rely on cloud based data management, large shared data goes up extensively. Analysis of such large data on distributed processing system or cloud is a bit difficult task to handle. The aim of this chapter is to provide a better understanding of the design challenges of cloud computing and analytics of big data on it. The challenge is related to how a large extent of data is being harnessed, and the opportunity is related to how effectively it is used for analyzing the information from it.

Handling Critical Issues of Big Data on Cloud

Web Services ◽  
2019 ◽  
pp. 1717-1748
Author(s):  
Madhavi Vaidya
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Distributed Processing ◽  
Processing System ◽  
Large Data ◽  
Data Sets ◽  
Complex Data ◽  
Data Scaling ◽  
Critical Issues ◽  
Hardware Platforms

Big Data is driving radical changes in traditional data analysis platforms. To perform any kind of analysis on such voluminous and complex data, scaling up the hardware platforms becomes impending. With the entire buzz surrounding Big Data; it is being collected at an unprecedented scale. Big Data has potential to revolutionize much more than just research. Loading large data-sets is often a challenge. Another shift of this Big Data processing is the move towards cloud computing. As many communities begin to rely on cloud based data management, large shared data goes up extensively. Analysis of such large data on distributed processing system or cloud is a bit difficult task to handle. The aim of this chapter is to provide a better understanding of the design challenges of cloud computing and analytics of big data on it. The challenge is related to how a large extent of data is being harnessed, and the opportunity is related to how effectively it is used for analyzing the information from it.

RESEARCH OF DISTRIBUTED VIRTUAL SOFTWARE-DEFINED INFRASTRUCTURE PERFORMANCE FOR BIG DATA PROCESSING TASKS

2021 ◽  
Vol 77 ◽  
pp. 58-67
Author(s):  
Y. A. Ushakov ◽  
Keyword(s):  
Experimental Study ◽  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Data Analytics ◽  
Distributed Processing ◽  
Big Data Analytics ◽  
Work Efficiency ◽  
Infrastructure Performance ◽  
Rapid Deployment

Container-based virtual software-defined virtual infrastructures have become an integral part of the underlying cloud computing engine and are used in a variety of distributed, scalable, resilient systems. But big data analytics tasks are mostly handled by traditional distributed clusters that require initial deployment, careful upgrades, and skilled maintenance. The aim of the work is to study the effectiveness of using software- defined virtual infrastructures based on containers and methods for their rapid deployment according to cloud principles for the implementation of automation platforms for distributed processing of big data. The schemes of Hadoop and Spark architecture deployment based on Docker Swarm and Kubernetes clusters are given. The development of criteria for evaluating the performance of distributed computing for processing big data was also carried out and an experimental study of the work efficiency was carried out.

scholarly journals Performance analysis of large-scale parallel-distributed processing with backup tasks for cloud computing

2014 ◽  
Vol 10 (1) ◽  
pp. 113-129 ◽  
Author(s):  
Tsuguhito Hirai ◽  
◽  
Hiroyuki Masuyama ◽  
Shoji Kasahara ◽  
Yutaka Takahashi ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Performance Analysis ◽  
Large Scale ◽  
Distributed Processing ◽  
Parallel Distributed Processing

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

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