No abstract
Globally distributed scientific experiments usually involve massive data volumes and distributed data analysis being done by many collaborators. With complex workloads and heterogeneous resources, each user may require certain characteristics for their network paths. In this paper, we present the iNDIRA tool, which interacts with SDN northbound interfaces to enable intent-based networking. It provides reliable, simple, and technology-agnostic communication between users and networks. Focusing particularly on science applications, iNDIRA uses natural language processing to construct semantic RDF graphs to understand, interact, and create the required network services. The technical challenges addressed by iNDIRA are: (1) development of a high-level descriptive language to query for network-application requirements, (2) keyword identification and condition checking based on user profiles and topology details, (3) allow user negotiation based on the current network state, (4) provide network provisioning guidance, and finally, (5) automatically provision and monitor a layer-2 network path for use by the application. iNDIRA is implemented on the ESnet network, where it interacts with OpenNSA (aka the NSI client) and Globus data transfer tools, to build complex cross-domain network paths for heterogeneous science applications and perform secure data transfer. We describe our implementation of iNDIRA running on ESnet's production network. We present results of iNDIRA's query processing mechanism, evaluate iNDIRA's intent language evaluation with other approaches, and describe future enhancements for iNDIRA.
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
Abstract-University campuses, Supercomputer centers and R&E networks are challenged to architect, build and support IT infrastructure to deal effectively with the data deluge facing most science disciplines. Hybrid network architecture, multi-domain bandwidth reservations, performance monitoring and GLIF Open Lightpath Exchanges (GOLE) are examples of network architectures that have been proposed, championed and implemented successfully to meet the needs of science. Most recently, Science DMZ, a campus design pattern that bypasses traditional performance hotspots in typical campus network implementation, has been gaining momentum. In this paper and corresponding demonstration, we build upon the SC11 SCinet Research Sandbox demonstrator with Software-Defined networking to explore new architectural approaches. A virtual switch network abstraction is explored, that when combined with software-defined networking concepts provides the science users a simple, adaptable network framework to meet their upcoming application requirements.
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