Energy efficient VM scheduling for big data processing in cloud computing environments

2019 ◽  
Author(s):  
SangWook Han ◽  
SeDong Min ◽  
HwaMin Lee
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Energy Efficient ◽  
Big Data Processing ◽  
Vm Scheduling ◽  
Computing Environments

Big Data Processing in Cloud Computing Environments

2017 ◽  
Vol 23 (11) ◽  
pp. 11092-11095 ◽  
Author(s):  
A Noraziah ◽  
Mohammed Adam Ibrahim Fakherldin ◽  
Khalid Adam ◽  
Mazlina Abdul Majid
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Big Data Processing ◽  
Computing Environments

PIM-WEAVER: A High Energy-efficient, General-purpose Acceleration Architecture for String Operations in Big Data Processing

2019 ◽  
Vol 21 ◽  
pp. 129-142
Author(s):  
Wenming Li ◽  
Xiaochun Ye ◽  
Da Wang ◽  
Hao Zhang ◽  
Zhimin Tang ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Energy Efficient ◽  
High Energy ◽  
General Purpose ◽  
Big Data Processing

NoSQL Databases

2014 ◽  
pp. 186-215 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Open Source ◽  
Data Storage ◽  
Big Data Processing ◽  
Nosql Databases ◽  
Data Intensive ◽  
Huge Data ◽  
Data Intensive Applications

NoSQL databases are designed to meet the huge data storage requirements of cloud computing and big data processing. NoSQL databases have lots of advanced features in addition to the conventional RDBMS features. Hence, the “NoSQL” databases are popularly known as “Not only SQL” databases. A variety of NoSQL databases having different features to deal with exponentially growing data-intensive applications are available with open source and proprietary option. This chapter discusses some of the popular NoSQL databases and their features on the light of CAP theorem.

Big Data Analytics in Cloud Computing

2019 ◽  
pp. 325-341
Author(s):  
Rajganesh Nagarajan ◽  
Ramkumar Thirunavukarasu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Data Visualization ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Big Data Processing ◽  
Investment Cost

In this chapter, the authors consider different categories of data, which are processed by the big data analytics tools. The challenges with respect to the big data processing are identified and a solution with the help of cloud computing is highlighted. Since the emergence of cloud computing is highly advocated because of its pay-per-use concept, the data processing tools can be effectively deployed within cloud computing and certainly reduce the investment cost. In addition, this chapter talks about the big data platforms, tools, and applications with data visualization concept. Finally, the applications of data analytics are discussed for future research.

Trends and Technologies in Big Data Processing

2020 ◽  
pp. 17-42
Author(s):  
Amitava Choudhury ◽  
Kalpana Rangra
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Computing Technology ◽  
Time Data ◽  
Big Data Processing ◽  
Real Time Data ◽  
Real Time Data Processing

Data type and amount in human society is growing at an amazing speed, which is caused by emerging new services such as cloud computing, internet of things, and location-based services. The era of big data has arrived. As data has been a fundamental resource, how to manage and utilize big data better has attracted much attention. Especially with the development of the internet of things, how to process a large amount of real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention to high performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. In this chapter, various big data processing techniques are discussed.

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