Software-Defined Networking for Scalable Cloud-based Services to Improve System Performance of Hadoop-based Big Data Applications

2016 ◽  
Vol 8 (2) ◽  
pp. 1-22 ◽  
Author(s):  
Desta Haileselassie Hagos
Keyword(s):  
Big Data ◽  
System Performance ◽  
Dynamic Network ◽  
Software Defined Networking ◽  
Network Control ◽  
Virtual Networks ◽  
Big Data Applications ◽  
New Research ◽  
Cloud Infrastructures ◽  
The Impact

The rapid growth of Cloud Computing has brought with it major new challenges in the automated manageability, dynamic network reconfiguration, provisioning, scalability and flexibility of virtual networks. OpenFlow-enabled Software-Defined Networking (SDN) alleviates these key challenges through the abstraction of lower level functionality that removes the complexities of the underlying hardware by separating the data and control planes. SDN has an efficient, dynamic, automated network management, higher availability and application provisioning through programmable interfaces which are very critical for flexible and scalable cloud-based services. In this study, the author explores broadly useful open technologies and methodologies for applying an OpenFlow-enabled SDN to scalable cloud-based services and a variety of diverse applications. The approach in this paper introduces new research challenges in the design and implementation of advanced techniques for bringing an SDN-enabled components and big data applications into a cloud environment in a dynamic setting. Some of these challenges become pressing concerns to cloud providers when managing virtual networks and data centers, while others complicate the development and deployment of cloud-hosted applications from the perspective of developers and end users. However, the growing demand for manageable, scalable and flexible clouds necessitates that effective solutions to these challenges be found. Hence, through real-world research validation use cases, this paper aims at exploring useful mechanisms for the role and potential of an OpenFlow-enabled SDN and its direct benefit for scalable cloud-based services. Finally, it demonstrates the impact of an OpenFlow-enabled SDN that fully embraces the opportunities and challenges of cloud infrastructures to improve the system performance of Hadoop-based big data applications by utilizing the network control capabilities of an OpenFlow to solve network congestion.

Software-Defined Networking for Scalable Cloud-Based Services to Improve System Performance of Hadoop-Based Big Data Applications

Web Services ◽  
2019 ◽  
pp. 1460-1484
Author(s):  
Desta Haileselassie Hagos
Keyword(s):  
Big Data ◽  
System Performance ◽  
Dynamic Network ◽  
Software Defined Networking ◽  
Network Control ◽  
Virtual Networks ◽  
Big Data Applications ◽  
New Research ◽  
Cloud Infrastructures ◽  
And Control

The rapid growth of Cloud Computing has brought with it major new challenges in the automated manageability, dynamic network reconfiguration, provisioning, scalability and flexibility of virtual networks. OpenFlow-enabled Software-Defined Networking (SDN) alleviates these key challenges through the abstraction of lower level functionality that removes the complexities of the underlying hardware by separating the data and control planes. SDN has an efficient, dynamic, automated network management, higher availability and application provisioning through programmable interfaces which are very critical for flexible and scalable cloud-based services. In this study, the author explores broadly useful open technologies and methodologies for applying an OpenFlow-enabled SDN to scalable cloud-based services and a variety of diverse applications. The approach in this paper introduces new research challenges in the design and implementation of advanced techniques for bringing an SDN-enabled components and big data applications into a cloud environment in a dynamic setting. Some of these challenges become pressing concerns to cloud providers when managing virtual networks and data centers, while others complicate the development and deployment of cloud-hosted applications from the perspective of developers and end users. However, the growing demand for manageable, scalable and flexible clouds necessitates that effective solutions to these challenges be found. Hence, through real-world research validation use cases, this paper aims at exploring useful mechanisms for the role and potential of an OpenFlow-enabled SDN and its direct benefit for scalable cloud-based services. Finally, it demonstrates the impact of an OpenFlow-enabled SDN that fully embraces the opportunities and challenges of cloud infrastructures to improve the system performance of Hadoop-based big data applications by utilizing the network control capabilities of an OpenFlow to solve network congestion.

scholarly journals Performance Analysis of NoSQL and Relational Databases with CouchDB and MySQL for Application’s Data Storage

2020 ◽  
Vol 10 (23) ◽  
pp. 8524
Author(s):  
Cornelia A. Győrödi ◽  
Diana V. Dumşe-Burescu ◽  
Doina R. Zmaranda ◽  
Robert Ş. Győrödi ◽  
Gianina A. Gabor ◽  
...  
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Relational Databases ◽  
Database Systems ◽  
Application Performance ◽  
Big Data Applications ◽  
Database Technology ◽  
Important Challenge ◽  
Big Data Application ◽  
The Impact

In the current context of emerging several types of database systems (relational and non-relational), choosing the type and database system for storing large amounts of data in today’s big data applications has become an important challenge. In this paper, we aimed to provide a comparative evaluation of two popular open-source database management systems (DBMSs): MySQL as a relational DBMS and, more recently, as a non-relational DBMS, and CouchDB as a non-relational DBMS. This comparison was based on performance evaluation of CRUD (CREATE, READ, UPDATE, DELETE) operations for different amounts of data to show how these two databases could be modeled and used in an application and highlight the differences in the response time and complexity. The main objective of the paper was to make a comparative analysis of the impact that each specific DBMS has on application performance when carrying out CRUD requests. To perform the analysis and to ensure the consistency of tests, two similar applications were developed in Java, one using MySQL and the other one using CouchDB database; these applications were further used to evaluate the time responses for each database technology on the same CRUD operations on the database. Finally, a comprehensive discussion based on the results of the analysis was performed that centered on the results obtained and several conclusions were revealed. Advantages and drawbacks for each DBMS are outlined to support a decision for choosing a specific type of DBMS that could be used in a big data application.

scholarly journals Performance Evaluation of Hadoop in Cloud for Big Data

2018 ◽  
Vol 7 (4.15) ◽  
pp. 16
Author(s):  
Mohammed Fakherldin ◽  
Ibrahim Aaker Targio Hashem ◽  
Abdullah Alzuabi ◽  
Faiz Alotaibi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Virtual Machines ◽  
File Systems ◽  
Research Direction ◽  
Distributed File Systems ◽  
Hadoop Mapreduce ◽  
Recent Trends ◽  
New Research ◽  
The Impact

Recent trends in big data have shown that the amount of data continues to increase at an exponential rate. This trend has inspired many researchers over the past few years to explore new research direction of studies related to multiple areas in big data. Hadoop is one of the most popular platforms for big data, thus, Hadoop MapReduce is used to store data in Hadoop distributed file systems. While, cloud computing is considered an excellent candidate for storing and processing the big data. However, processing big data across multiple nodes is a challenging task. The problem is even more complex using virtualized clusters in a cloud computing to execute a large number of tasks. This paper provides a review and analysis of the impact of using physical versus cloud cluster in the processing a large amount of data. This analysis has an impact on the processing in terms of execution time and cost of using each one of them. The result indicates that the use of cloud virtual machines helped better utilize the resources of the host computer. 

scholarly journals Prospects of Big Data Analytics in Africa Healthcare System

2018 ◽  
Vol 10 (6) ◽  
pp. 114
Author(s):  
Akindele Akinnagbe ◽  
K.Dharini Amitha Peiris ◽  
Oluyemi Akinloye
Keyword(s):  
Big Data ◽  
Positive Impact ◽  
Research Question ◽  
Health Sector ◽  
Big Data Analytics ◽  
Healthcare Systems ◽  
Ethical Challenges ◽  
Big Data Applications ◽  
The World ◽  
The Impact

Big data is having a positive impact in almost every sphere of life, such as in military intelligence, space science, aviation, banking, and health. Big data is a growing force in healthcare. Even though healthcare systems in the developed world are recording some breakthroughs due to the application of big data, it is important to research the impact of big data in developing regions of the world, such as Africa. Healthcare systems in Africa are, in relative terms, behind the rest of the world. Platforms and technologies used to amass big data such as the Internet and mobile phones are already in use in Africa, thereby making big data applications to be emerging. Hence, the key research question we address is whether big data applications can improve healthcare in Africa especially during epidemics and through the public health system. In this study, a literature review is carried out, firstly to present cases of big data applications in healthcare in Africa, and secondly, to explore potential ethical challenges of such applications. This review will provide an update on the application of big data in the health sector in Africa that can be useful for future researchers and health care practitioners in Africa.

Dynamic Network Bandwidth Resizing for Big Data Applications

2017 ◽  
Author(s):  
Fabio Diniz Rossi ◽  
Guilherme Da Cunha Rodrigues ◽  
Rodrigo N. Calheiros ◽  
Marcelo Da Silva Conterato
Keyword(s):  
Big Data ◽  
Dynamic Network ◽  
Network Bandwidth ◽  
Big Data Applications

scholarly journals I/O Performance Modeling for Big Data Applications over Cloud Infrastructures

2015 ◽  
Author(s):  
Ioannis Mytilinis ◽  
Dimitrios Tsoumakos ◽  
Verena Kantere ◽  
Anastassios Nanos ◽  
Nectarios Koziris
Keyword(s):  
Big Data ◽  
Performance Modeling ◽  
Big Data Applications ◽  
Cloud Infrastructures

scholarly journals The Impact Of Lane Closures On Congestion And Reliability: A Case Study Of Toronto's Gardiner Expressway

2021 ◽  
Author(s):  
Christina Borowiec
Keyword(s):  
Big Data ◽  
Performance Indicators ◽  
System Performance ◽  
Policy Action ◽  
Methods Of Analysis ◽  
Trade Offs ◽  
Regression Framework ◽  
The Impact ◽  
Lane Closures

Usage of big data with before-after methods of analysis makes it possible to evaluate the effect of major transport investments on system performance. In employing before-after methods to investigate the impact of lane closures on congestion and travel reliability, changes and trade-offs in performance indicators are quantified and policy action effectiveness is evaluated. This is illustrated through a case study of two separate lane closure interventions on the Gardiner Expressway in Toronto, Ontario. Models using a regression framework were developed for the pre-, peri-, and post-closure test periods of the first intervention and pre- and peri-closure periods of the second intervention. Results suggest the impacts of policy actions on system performance are strong, and that congestion and travel reliability counterintuitively move in different directions. Reduced demand effects are observed, prompting discussion on how highways and congestion should be managed and whether or not municipalities should add capacity to regional assets.

scholarly journals The Impact Of Lane Closures On Congestion And Reliability: A Case Study Of Toronto's Gardiner Expressway

2021 ◽  
Author(s):  
Christina Borowiec
Keyword(s):  
Big Data ◽  
Performance Indicators ◽  
System Performance ◽  
Policy Action ◽  
Methods Of Analysis ◽  
Trade Offs ◽  
Regression Framework ◽  
The Impact ◽  
Lane Closures

Usage of big data with before-after methods of analysis makes it possible to evaluate the effect of major transport investments on system performance. In employing before-after methods to investigate the impact of lane closures on congestion and travel reliability, changes and trade-offs in performance indicators are quantified and policy action effectiveness is evaluated. This is illustrated through a case study of two separate lane closure interventions on the Gardiner Expressway in Toronto, Ontario. Models using a regression framework were developed for the pre-, peri-, and post-closure test periods of the first intervention and pre- and peri-closure periods of the second intervention. Results suggest the impacts of policy actions on system performance are strong, and that congestion and travel reliability counterintuitively move in different directions. Reduced demand effects are observed, prompting discussion on how highways and congestion should be managed and whether or not municipalities should add capacity to regional assets.

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