Developing Applications with Networking Capabilities via End-to-End SDN (DANCES)

Hazlewood, Victor; Benninger, Kathy; Peterson, Gregory M.; Charcalla, Jason; Sparks, Benny; Hanley, Jesse; Adams, Andrew; Learn, Bryan; Budden, Robert; Simmel, Derek; Lappa, Joseph; Yanovich, Jared

doi:10.1145/2949550.2949557

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…A NSF-funded research project launched with the name of DANCES (Developing Applications with Networking Capabilities via End-to-end SDN) [40]. DANCES uses SDN technology.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Programmable Network for Large-Scale Scientific Data Transfer Using AmoebaNet

Shah

Noh

2019

Applied Sciences

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Large scientific experimental facilities currently are generating a tremendous amount of data. In recent years, the significant growth of scientific data analysis has been observed across scientific research centers. Scientific experimental facilities are producing an unprecedented amount of data and facing new challenges to transfer the large data sets across multi continents. In particular, these days the data transfer is playing an important role in new scientific discoveries. The performance of distributed scientific environment is highly dependent on high-performance, adaptive, and robust network service infrastructures. To support large scale data transfer for extreme-scale distributed science, there is the need of high performance, scalable, end-to-end, and programmable networks that enable scientific applications to use the networks efficiently. We worked on the AmoebaNet solution to address the problems of a dynamic programmable network for bulk data transfer in extreme-scale distributed science environments. A major goal of the AmoebaNet project is to apply software-defined networking (SDN) technology to provide "Application-aware" network to facilitate bulk data transfer. We have prototyped AmoebaNet's SDN-enabled network service that allows application to dynamically program the networks at run-time for bulk data transfers. In this paper, we evaluated AmoebaNet solution with real world test cases and shown that how it efficiently and dynamically can use the networks for bulk data transfer in large-scale scientific environments. Appl. Sci. 2019, 9, 4541 2 of 17 for on-demand secure path reservation over the Wide Area Network (WAN), and these solutions also provide the automated, guaranteed bandwidth services for scientific workflows. The existing network paradigm for scientific workflows has been proven very successfully. However, it has been observed during our comprehensive study that the present network paradigm for an extreme-scale scientific data transfer job still needs some more improvements to reach its full potentials. We claimed that the current networking paradigm must address the following major problems: scalability, last mile, and dynamic programmability problems.The recent emerging concept in the network world is called Software-Defined Networking (SDN) [9][10][11]. This latest technology has been providing the new methods of configuration and management of networks. In SDN, the underlying network devices are simply considered as packets forwarding elements. It is possible to manage the control logic of the network centrally through a software program, which can effectively control the entire network behavior. In order to address such problems mentioned above, AmoebaNet [12] has been proposed.It is common to notice low efficiency in data movement when running the data transfer tools on DTN machines. Such inefficient data transmission is one of critical points in scientific data computing. Therefore, it is required to provide high-performance, predictable, and schedulable data movement...

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“…A NSF-funded research project launched with the name of DANCES (Developing Applications with Networking Capabilities via End-to-end SDN) [40]. DANCES uses SDN technology.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Programmable Network for Large-Scale Scientific Data Transfer Using AmoebaNet

Shah

Noh

2019

Applied Sciences

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“…The DANCES (Developing Applications with Networking Capabilities via End-to-end SDN) (Hazlewood et al, 2016) is a NSFfunded SDN research project. It aims to enable network bandwidth scheduling via SDN technology and enhance the overall performance of scientific applications.…”

Section: Qos-guaranteed End-to-end Network Path Effortsmentioning

confidence: 99%

AmoebaNet: An SDN-enabled network service for big data science

Shah

et al. 2018

Journal of Network and Computer Applications

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Data transfer is now an essential function for science discoveries, particularly within big data environments. To support data transfer for big data science, there is a need for high performance, scalable, end-to-end, and programmable networks that enable science applications to use the network most efficiently. The existing network paradigm that support big data science consists of three major components: terabit networks that provide high network bandwidths, Data Transfer Nodes (DTNs) and Science DMZ architecture that bypasses the performance hotspots in typical campus networks, and on-demand secure circuits/paths reservation systems, such as ESNet OSCARS and Internet2 AL2S, which provides automated, guaranteed bandwidth service in WAN. This network paradigm has proven to be very successful. However, to reach its full potentials, we claim that existing network paradigm for big data science must address three major problems: the last mile problem, the scalability problem, and the programmability problem. To address these problems, we proposed a solution called AmoebaNet. AmoebaNet applies Software Defined Networking (SDN) technology to provide "QoS-guaranteed" network services in campus or local area networks. AmoebaNet complements existing network paradigm for big data science: it allows application to program networks at run-time for optimum performance; and, in conjunction with WAN circuits/paths reservation system such as ESNet OSCARS and Internet2 AL2S; it solves the last mile problem and the scalability problem. • Programmability. This feature enables science applications to program networks at run-time to suit their needs. A powerful and rich

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Advance reservation access control using software-defined networking and tokens

Chung

Jung

Kettimuthu

et al. 2018

Future Generation Computer Systems

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Developing Applications with Networking Capabilities via End-to-End SDN (DANCES)

Abstract: The Developing Applications with Networking Capabilities via End-to-End SDN (DANCES) project [1] is a collaboration between

Cited by 7 publications

References 4 publications

A Dynamic Programmable Network for Large-Scale Scientific Data Transfer Using AmoebaNet

A Dynamic Programmable Network for Large-Scale Scientific Data Transfer Using AmoebaNet

AmoebaNet: An SDN-enabled network service for big data science

Advance reservation access control using software-defined networking and tokens

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