A Case Study on Effective Technique of Distributed Data Storage for Big Data Processing in the Wireless Internet Environment

2015 ◽  
Vol 86 (1) ◽  
pp. 239-253 ◽  
Author(s):  
Seong-Taek Park ◽  
Yeong-Real Kim ◽  
Seon-Phil Jeong ◽  
Chang-Ick Hong ◽  
Tae-Gu Kang
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Storage ◽  
Distributed Data ◽  
Wireless Internet ◽  
Effective Technique ◽  
Big Data Processing ◽  
Distributed Data Storage ◽  
Internet Environment

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.

“Saksham Model” Performance Improvisation Using Node Capability Evaluation in Apache Hadoop

2020 ◽  
pp. 206-230
Author(s):  
Ankit Shah ◽  
Mamta C. Padole
Keyword(s):  
Big Data ◽  
Distributed Computing ◽  
Data Processing ◽  
Data Storage ◽  
Model Performance ◽  
Big Data Processing ◽  
Apache Hadoop ◽  
Processing Capability ◽  
Proposed Model ◽  
Capability Evaluation

Big Data processing and analysis requires tremendous processing capability. Distributed computing brings many commodity systems under the common platform to answer the need for Big Data processing and analysis. Apache Hadoop is the most suitable set of tools for Big Data storage, processing, and analysis. But Hadoop found to be inefficient when it comes to heterogeneous set computers which have different processing capabilities. In this research, we propose the Saksham model which optimizes the processing time by efficient use of node processing capability and file management. The proposed model shows the performance improvement for Big Data processing. To achieve better performance, Saksham model uses two vital aspects of heterogeneous distributed computing: Effective block rearrangement policy and use of node processing capability. The results demonstrate that the proposed model successfully achieves better job execution time and improves data locality.

Design of Big Data Processing System Architecture Based on Hadoop under the Cloud Computing

2014 ◽  
Vol 556-562 ◽  
pp. 6302-6306 ◽  
Author(s):  
Chun Mei Duan
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Data Storage ◽  
System Architecture ◽  
Processing System ◽  
System Stability ◽  
Security Model ◽  
Data Processing System ◽  
Big Data Processing

In allusion to limitations of traditional data processing technology in big data processing, big data processing system architecture based on hadoop is designed, using the characteristics of quantification, unstructured and dynamic of cloud computing.It uses HDFS be responsible for big data storage, and uses MapReduce be responsible for big data calculation and uses Hbase as unstructured data storage database, at the same time a system of storage and cloud computing security model are designed, in order to implement efficient storage, management, and retrieval of data,thus it can save construction cost, and guarantee system stability, reliability and security.

scholarly journals Employing Vertical Elasticity for Efficient Big Data Processing in Container-Based Cloud Environments

2021 ◽  
Vol 11 (13) ◽  
pp. 6200
Author(s):  
Jin-young Choi ◽  
Minkyoung Cho ◽  
Jik-Soo Kim
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Data Storage ◽  
Resource Utilization ◽  
System Throughput ◽  
Big Data Processing ◽  
Cloud Environments ◽  
Utilization Scheme ◽  
Adaptive Resource

Recently, “Big Data” platform technologies have become crucial for distributed processing of diverse unstructured or semi-structured data as the amount of data generated increases rapidly. In order to effectively manage these Big Data, Cloud Computing has been playing an important role by providing scalable data storage and computing resources for competitive and economical Big Data processing. Accordingly, server virtualization technologies that are the cornerstone of Cloud Computing have attracted a lot of research interests. However, conventional hypervisor-based virtualization can cause performance degradation problems due to its heavily loaded guest operating systems and rigid resource allocations. On the other hand, container-based virtualization technology can provide the same level of service faster with a lightweight capacity by effectively eliminating the guest OS layers. In addition, container-based virtualization enables efficient cloud resource management by dynamically adjusting the allocated computing resources (e.g., CPU and memory) during the runtime through “Vertical Elasticity”. In this paper, we present our practice and experience of employing an adaptive resource utilization scheme for Big Data workloads in container-based cloud environments by leveraging the vertical elasticity of Docker, a representative container-based virtualization technique. We perform extensive experiments running several Big Data workloads on representative Big Data platforms: Apache Hadoop and Spark. During the workload executions, our adaptive resource utilization scheme periodically monitors the resource usage patterns of running containers and dynamically adjusts allocated computing resources that could result in substantial improvements in the overall system throughput.

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