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.

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.

Are They Ready for the Big Thing?

2021 ◽  
pp. 200-210
Author(s):  
Matthias Lederer ◽  
Juluis Lederer
Keyword(s):  
Big Data ◽  
Data Management ◽  
Process Management ◽  
Business Processes ◽  
Future Trend ◽  
Data Driven ◽  
Big Data Applications ◽  
All Solutions ◽  
Management Approaches ◽  
And Control

Data-driven business processes management (BPM) is regarded as a central future trend because automation often makes huge amounts of data (big data) available for the optimisation and control of workflows. Software manufacturers also use this trend and call their solutions big data applications, even if some features are reminiscent of traditional data management approaches. This chapter derives from the basic definitions of big data including 13 central requirements that a big data BPM solution must meet in order to be described as such. One hundred twenty-one process management solutions are evaluated on the basis of these to determine whether they are real big data applications. As a result, less than 5% of all solutions analysed meet all requirements.

scholarly journals Optimizing Traffic Engineering in Software Defined Networking

2018 ◽  
Author(s):  
Mouhammd Alkasassbeh ◽  
Ghazi Al-Naymat ◽  
Mohammad Alauthman ◽  
Esra Ednat
Keyword(s):  
Vertical Integration ◽  
Traffic Engineering ◽  
Minimum Cost ◽  
Software Defined Networking ◽  
Network Control ◽  
Network Configuration ◽  
Control Logic ◽  
Network Delay ◽  
Complex Process ◽  
And Control

The digital society is an outcome of the Internet which has nearly made everything connected and accessible no matter where or when. Nevertheless, despite the fact that conventional IP networks are complicated and very hard to manage, they are still widely adopted. The already established policies make the network configuration/reconfiguration a complex process that reacts to errors, load, and modifications. The prevailing networks are vertically integrated which makes things more and more complicated: Data planes and control are strapped together. Software-defined networking is a model that is meant to solve this issue by splitting the vertical integration and detaching the network’s control logic from the implicit routers and switches; this could be achieved by reinforcing centralization of network control and making the network programmable. In this work, we worked to implement MPLS networks with SDN, to enhance the traffic engineering over the network, and to minimize the network delay and latency, with minimum cost using three of the different SDN networks. The experiment results showed the advantage of the proposed approach for reducing the network delay, comparing with previous studies. Where the average of network delay in our approach reaches to 3.01 milliseconds.

scholarly journals Big Data COVID-19 Systematic Literature Review: Pandemic Crisis

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3125
Author(s):  
Laraib Aslam Haafza ◽  
Mazhar Javed Awan ◽  
Adnan Abid ◽  
Awais Yasin ◽  
Haitham Nobanee ◽  
...  
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Inclusion Criteria ◽  
Big Data Applications ◽  
Data Statistics ◽  
Review Study ◽  
The Many ◽  
Meta Analyses ◽  
And Control

The COVID-19 pandemic has frightened people worldwide, and coronavirus has become the most commonly used phrase in recent years. Therefore, there is a need for a systematic literature review (SLR) related to Big Data applications in the COVID-19 pandemic crisis. The objective is to highlight recent technological advancements. Many studies emphasize the area of the COVID-19 pandemic crisis. Our study categorizes the many applications used to manage and control the pandemic. There is a very limited SLR prospective of COVID-19 with Big Data. Our SLR study picked five databases: Science direct, IEEE Xplore, Springer, ACM, and MDPI. Before the screening, following the recommendation, Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) were reported for 893 studies from 2019, 2020 and until September 2021. After screening, 60 studies met the inclusion criteria through COVID-19 data statistics, and Big Data analysis was used as the search string. Our research’s findings successfully dealt with COVID-19 healthcare with risk diagnosis, estimation or prevention, decision making, and drug Big Data applications problems. We believe that this review study will motivate the research community to perform expandable and transparent research against the pandemic crisis of COVID-19.

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 Intelligent Management and Efficient Operation of Big Data

2015 ◽  
pp. 105-129 ◽  
Author(s):  
José Moura ◽  
Fernando Batista ◽  
Elsa Cardoso ◽  
Luís Nunes
Keyword(s):  
Big Data ◽  
Large Data ◽  
Data Sources ◽  
Data Traffic ◽  
Efficient Operation ◽  
Big Data Applications ◽  
Intelligent Management ◽  
High Level ◽  
Cloud Infrastructures

This chapter details how Big Data can be used and implemented in networking and computing infrastructures. Specifically, it addresses three main aspects: the timely extraction of relevant knowledge from heterogeneous, and very often unstructured large data sources; the enhancement on the performance of processing and networking (cloud) infrastructures that are the most important foundational pillars of Big Data applications or services; and novel ways to efficiently manage network infrastructures with high-level composed policies for supporting the transmission of large amounts of data with distinct requisites (video vs. non-video). A case study involving an intelligent management solution to route data traffic with diverse requirements in a wide area Internet Exchange Point is presented, discussed in the context of Big Data, and evaluated.

A heuristic approach to the multicriteria design of IaaS cloud infrastructures for Big Data applications

2018 ◽  
Vol 35 (5) ◽  
pp. e12259 ◽  
Author(s):  
María Arostegi ◽  
Ana Torre-Bastida ◽  
Miren Nekane Bilbao ◽  
Javier Del Ser
Keyword(s):  
Big Data ◽  
Heuristic Approach ◽  
Big Data Applications ◽  
Cloud Infrastructures

scholarly journals Intelligent Management and Efficient Operation of Big Data

Web Services ◽  
2019 ◽  
pp. 1991-2016
Author(s):  
José Moura ◽  
Fernando Batista ◽  
Elsa Cardoso ◽  
Luís Nunes
Keyword(s):  
Big Data ◽  
Large Data ◽  
Data Sources ◽  
Data Traffic ◽  
Efficient Operation ◽  
Big Data Applications ◽  
Intelligent Management ◽  
High Level ◽  
Cloud Infrastructures

This chapter details how Big Data can be used and implemented in networking and computing infrastructures. Specifically, it addresses three main aspects: the timely extraction of relevant knowledge from heterogeneous, and very often unstructured large data sources; the enhancement on the performance of processing and networking (cloud) infrastructures that are the most important foundational pillars of Big Data applications or services; and novel ways to efficiently manage network infrastructures with high-level composed policies for supporting the transmission of large amounts of data with distinct requisites (video vs. non-video). A case study involving an intelligent management solution to route data traffic with diverse requirements in a wide area Internet Exchange Point is presented, discussed in the context of Big Data, and evaluated.

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