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

Performance modeling of big data applications in the cloud centers

2017 ◽  
Vol 73 (5) ◽  
pp. 2258-2283 ◽  
Author(s):  
Chao Shen ◽  
Weiqin Tong ◽  
Jenq-Neng Hwang ◽  
Qiang Gao
Keyword(s):  
Big Data ◽  
Performance Modeling ◽  
Big Data Applications

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 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.

Modeling and Evaluating the Effects of Big Data Storage Resource Allocation in Global Scale Cloud Architectures

2016 ◽  
Vol 12 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Enrico Barbierato ◽  
Marco Gribaudo ◽  
Mauro Iacono
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Storage ◽  
Global Scale ◽  
Big Data Applications ◽  
Storage Resource ◽  
Management Techniques ◽  
Cloud Elasticity ◽  
Cloud Infrastructures ◽  
Cloud Applications

The availability of powerful, worldwide span computing facilities offering application scalability by means of cloud infrastructures perfectly matches the needs for resources that characterize Big Data applications. Elasticity of resources in the cloud enables application providers to achieve results in terms of complexity, performance and availability that were considered beyond affordability, by means of proper resource management techniques and a savvy design of the underlying architecture and of communication facilities. This paper presents an evaluation technique for the combined effects of cloud elasticity and Big Data oriented data management layer on global scale cloud applications, by modeling the behavior of both typical in memory and in storage data management.

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