Accepted NRE demos
Data Transfer Node Service in SCinet
Location: NOC 1081 (SCinet)
“Data Transfer Nodes (DTN) have been deployed in large data centers and HPC facilities as front end of large systems, which have served very well for process/compute intensive science workflows. Recently, many data intensive science workflows are emerging, which demanded different types of infrastructure to support different types of data intensive science workflow. One of solutions for supporting this trend is to provide DTN services at network exchange points. This resource provides a platform for prototyping new data intensive science workflows, enabling more reliable data transfer services, providing a monitoring and measurement point for the science workflows, and serving as test point when data transfers fail. We will deploy a DTN as shared infrastructure in the StarLight facility, which will facilitate the SC community to prototype and test the data intensive science workflows before the SC conference. We will transition the service platform to SC17 SCinet for staging and for the duration of conference.
Programmable Privacy-Preserving Network Measurement for Network Usage Analysis and Troubleshooting
Location: Booth 1653 (Center for Data Intensive Science – Open Commons Consortium)
Network measurement and monitoring are instrumental to network operations, planning and troubleshooting. However, increasing line rates (100+Gbps), changing measurement targets and metrics, privacy concerns, and policy differences across multiple R&E network domains have introduced tremendous challenges in operating such high-speed heterogeneous networks, understanding the traffic patterns, providing for resource optimization, and locating and resolving network issues. There is strong demand for a flexible, high-performance measurement instrument that can empower network operators to achieve the versatile objectives of effective network management, especially effective resource provisioning. In this demonstration, we propose AMIS: Advanced Measurement Instrument and Services to achieve programmable, flow-granularity and event-driven network measurement, sustain scalable line rates, to meet evolving measurement objectives and to derive knowledge for network advancement.
Let’s Do Full DPI of the SCinet Network and Do Some SCIENCE
Location: NOC 1081 (SCinet)
The fundamental scientific paradigm addressed in this research project is the application of greater network packet visibility and packet inspection to secure computer network systems. The greater visibility and inspection will enable detection of advanced content-based threats that exploit application vulnerabilities and designed to bypass traditional security approaches such as firewalls and antivirus scanners. Greater visibility and inspection is achieved through identification of the application protocol (e.g. http, SMTP, skype) and, in some cases, extraction and processing of the information contained in the packet payload. Analysis is then performed on the resulting DPI data to identify potentially malicious behavior. In order to obtain visibility and inspect the application protocol and contents DPI technologies have been developed.
We have developed a novel piece of technology that will enable us to create layer 7 meta data up to 7k applications/protocols at a variety of line rates. This demonstration will be to show what protocols have been dissected and how this information can be created to enable interesting use-cases like situation awareness, asset discovery, network mapping, and passive identity management.
Tracking Network Events with Write Optimized Data Structure
Location: NOC 1081 (SCinet)
The basic action of two IP addresses communicating is still a critical part of most security investigations. Typical tools log events and send them to a variety of traditional databases. These databases are optimized for querying rather than ingestion. When faced with indexing hundreds of millions of events such indices degrade in their ability to accept insertions at a rate that is unacceptable for network monitoring.
Write-optimized data structures (WODS) provides a novel approach to traditional storage structures (e.g. B-trees). WODS trade minor degradations in query performance for significant gains in insertion rates, typically on the order of 10 to 100 times faster. Our Diventi project uses a write optimized B-Tree known as a Be tree to index entries in connection logs from a common network security tool (bro). In previous tests this sustained a rate of 20,000 inserts per second, while after 300,000,000 events a traditional B-Tree degraded to 100 inserts per second.
mdtmFTP: Optimized Bulk Data Transfer on Multicore Systems
Booth 1653 (Center for Data Intensive Science – Open Commons Consortium)
Large scale, high capacity transport of data across wide area networks (WANs) is a critical requirement for many science workflows. Today, this need is being addressed with architecture and protocols designed many years ago, when single core processors were predominant. Today, multicore has become the norm of high-performance computing. Yet many science communities are still relying on approaches oriented to single core. Fermilab network research group has developed a new high-performance data transfer tool, called mdtmFTP, to maximize data transfer performance on multicore platforms. mdtmFTP has several advanced features. First, mdtmFTP adopts a pipelined I/O design. A data transfer task is carried out in a pipelined manner across multiple cores. Dedicated I/O threads are spawned to perform I/O operations in parallel. Second, mdtmFTP uses a particularly designed multicore-aware data transfer middleware (MDTM) to schedule cores for its threads, which optimize use of underlying multicore core system. Third, mdtmFTP implements a large virtual file mechanism to address the lots-of-small-files (LOSF) problem. Finally, mdtmFTP unitizes multiple optimization mechanisms – zero copy, asynchronous I/O, batch processing, and pre-allocated buffer pools – to improve performance. In this demo, we will use mdtmFTP to demonstrate optimized bulk data movement over long-distance wide area network paths. Our purpose is to show that mdtmFTP performs better than existing data transfer tools.
BigData Express: Toward Predictable, Schedulable and High-Performance Data Transfer
Booth 1653 (Center for Data Intensive Science – Open Commons Consortium)
Big Data has emerged as a driving force for scientific discoveries. Large scientific instruments (e.g., colliders, and telescopes) generate exponentially increasing volumes of data. To enable scientific discovery, science data must be collected, indexed, archived, shared, and analyzed, typically in a widely distributed, highly collaborative manner. Data transfer is now an essential function for science discoveries, particularly within big data environments. Although significant improvements have been made in the area of bulk data transfer, the currently available data transfer tools and services will not be able to successfully address the high-performance and time-constraint challenges of data transfer to support extreme-scale science applications for the following reasons: disjoint end-to-end data transfer loops, cross-interference between data transfers, and existing data transfer tools and services are oblivious to user requirements (deadline and QoS requirements). We are working on the BigData Express project (BDE) to address these problems.
The BDE research team has released several software packages: (1) BDE WebPortal. A web portal that allows users to access BigData Express data transfer services; (2) BDE scheduler. It schedules and orchestrates resources at BDE sites to support high-performance data transfer. And (3) BDE AmoebaNet. A SDN-enabled network service that provide “Application-aware” network. It allows application to program network at run-time for optimum performance. These software packages can be deployed to support three types of data transfer: real-time data transfer, deadline-bound data transfer, and best-effort data transfer.
In this demo, we use BDE software to demonstrate bulk data movement over wide area networks. Our goal is to demonstrate that BDE can successfully address the high-performance and time-constraint challenges of data transfer to support extreme-scale science applications.
Deep Network Visibility Using Multidimensional Data Analysis
Location: NOC 1081 (SCinet)
“Reservoir Labs proposes to demonstrate a usable and scalable network security workflow based on ENSIGN [1], a high-performance data analytics tool involving tensor decompositions. The enhanced workflow provided by ENSIGN assists in identifying attackers who craft their actions to subvert signature-based detection methods and automates much of the labor intensive forensic process of connecting isolated incidents into a coherent attack profile. This approach complements traditional workflows that focus on highlighting individual suspicious activities. ENSIGN uses advanced tensor decomposition algorithms to decompose rich network data with multiple metadata attributes into components that capture network patterns spanning the entire multidimensional data space. This enables easier identification of anomalies and simpler analysis of large complex patterns.
Reservoir Labs proposes to apply ENSIGN over the network security logs available through the SCinet network stack to provide deep visibility into network behaviors/trends including but not limited to unauthorized traffic patterns, temporal patterns such as beaconing, security attacks, and patterns of authorized and unauthorized services.
Highly Distributed Science DMZs
Booth 1653 (Center for Data Intensive Science – Open Commons Consortium)
These demonstrations will showcase the utility of using several specialized software stacks to scale science DMZs across multiple highly distributed sites. Software Defined International WANs (SD-WANs) and International Software Defined Exchange Interoperability This demonstration will show how traditional exchange services, architecture, and technologies are being radically transformed by the virtualization of resources at all levels, enabling much more flexible, dynamic, and programmable communication capabilities.
IIRNC Software Defined Exchange (SDX) Services Integrated with
100 GBPS Data Transfer Nodes (DTNS) for Petascale Science
Location: Booth 1653 (Center for Data Intensive Science – Open Commons Consortium)
This demonstration illustrates high performance transport services for petascale science over WANs through SDN enabled DTN’s, which are being designed to optimize capabilities for supporting large scale, high capacity, high performance, reliable and sustained individual data streams.
Jupyter For Integrating Science Workflows And Network Orchestration
Location: Booth 1653 (Center for Data Intensive Science – Open Commons Consortium)
This demonstration will showcase the utility of using Jupyter for integrating scientific workflows with network orchestration techniques required for data intensive science.
OCC’S Environmental Data Commons At 100 GBPS
Location: Booth 1653 (Center for Data Intensive Science – Open Commons Consortium)
“We will demonstrate how 100 Gbps networks can be used to interoperate storage and compute resources located in two geographically distributed data centers so that a data commons can span multiple data centers and how two data commons connected by a 100 Gbps network can peer with each other.
Applying P4 To Supporting Data Intensive Science Workflows On Large Scale Networks
Location: Booth 1653 (Center for Data Intensive Science – Open Commons Consortium)
These demonstrations will show how P4 can be used to support large scale data intensive science workflows on high capacity high performance WANs and LANs.
International Wan High Performance Data Transfer Services Integrated With 100 Gbps Data Transfer Nodes For Petascale Science (PETATRANS)
Location: Booth 1653 (Center for Data Intensive Science – Open Commons Consortium)
The PetaTrans 100 Gbps Data Transfer Node (DTN) research project is directed at improving large scale WAN services for high performance, long duration, large capacity single data flows. iCAIR is designing, developing, and experimenting with multiple designs and configurations for 100 Gbps Data Transfer Nodes (DTNs) over 100 Gbps Wide Area Networks (WANs), especially trans-oceanic WANs, PetaTrans – high performance transport for petascale science, including demonstrations at SC17.
Large Scale Optimized Wan Data Transport For Geophysical Sciences
Location: Booth 1653 (Center for Data Intensive Science – Open Commons Consortium)
This demonstration will showcase a capability for optimal transfer of large scale geophysical data files across WANs. This capability is enabled by an innovated method for integrating data selection and transport using orchestrated workflows and network transport.
University of Texas at Dallas – Intel – Aspera with SC2017 Options
Location: Booth 995 (University of Texas at Dallas)
The University of Texas at Dallas (UTD) is planning to demonstrate a high-speed Optical Software Defined Network (SDN) at Supercomputing 2017 in Denver. To demonstrate the full performance of the optical SDN we need to push relatively large amounts of data over the network and to showcase low latency networking for a variety of special experiments. Recent involvement with the PRP Pacific Research Platform and the “in development” NRP National Research Platform first meeting, led to the realization that Data Transfer Node technology will be required for participation in the fast developing platform.
An Adaptive Network Testbed based on SDN-IP
Location: Booth 973 (NICT)
In this demonstration, we will show the conceptual design of SDN-IP testbed.
Next Generation SDN Architectures and Applications with Gigabit/sec to Terabit/sec Flows and Real-Time Analytics for Data Intensive Sciences
Location: Booth 663 (California Institute of Technology / CACR)
This submission is a brief summary of the innovative on-floor and wide area network configuration, topology, and SDN methods using multiple controllers and state of the art data transfer methods, optical network and server technologies, as well as many of the demonstrations being supported, which are described more fully in separate NRE submissions associated with the Caltech booth.
Network Control and Multi-domain Interconnection Programming for Next-Generation Data-Intensive Sciences
Location: Booth 1653 (Center for Data Intensive Science – Open Commons Consortium)
The next generation of globally distributed science programs face unprecedented challenges in the design and implementation of their networking infrastructures to achieve efficient, flexible, secure, global data transfers for scientific workflows. These networking infrastructures must be realized with highly easy-to-program control planes, such as using the emerging software-defined networking (SDN) techniques, to allow extensibility, composability, and reactivity, to handle ever evolving requirements, such as integrating a wide range of functionalities including resource allocation, measurements, security, policy enforcement, fault tolerance, scaling, among others. At the same time, as such science programs tend to span multiple autonomous organizations, they must respect the privacy and policies of individual organizations. This demo shows novel control-plane programming primitives and abstractions toward realizing highly programmable control and interconnection of such data intensive science networks.
SENSE: SDN for End-to-end Networked Science at the Exascale
Location: Booth 663 (California Institute of Technology / CACR)
Distributed application workflows with big-data requirements depends on predictable network behavior to work efficiently. The SENSE project vision is to enable National Labs and Universities to request and provision end-to-end intelligent network services for their application workflows, leveraging SDN capabilities . Our approach is to design network abstractions and an operating framework to allow host, Science DMZ / LAN, and WAN auto-configuration across domains, based on infrastructure policy constraints designed to meet end-to-end service requirements.
Demonstrations of 400Gbps Disk-to-Disk: WAN File Transfers using iWARP and NVMe Disk
Location: Booth 1653 (Center for Data Intensive Science – Open Commons Consortium)
NASA requires the processing and exchange of ever increasing vast amounts of scientific data, so NASA networks must scale up to ever increasing speeds, with 100 Gigabit per second (Gbps) networks being the current challenge. However it is not sufficient to simply have 100 Gbps network pipes, since normal data transfer rates would not even fill a 1 Gbps pipe. The NASA Goddard High End Computer Networking (HECN) team will demonstrate systems and techniques to achieve near 400G line-rate disk-to-disk data transfers between a high performance NVMe Server at SC17 to or from a pair of high performance NVMe servers across two national wide area 2x100G network paths, by utilizing iWARP to transfer the data between the servers’ NVMe drives.
Dynamic Distributed Data Processing
Location: Booth 1653 (Center for Data Intensive Science – Open Commons Consortium)
This demonstration will show dynamic arrangement of high performance, widely distributed processing of large volumes of data across a set of compute and network resources organized in response to changing application demands. A dynamic, distributed processing pipeline will be demonstrated from SC17 to the Naval Research Laboratory in Washington, DC, and back to SC17. A software-controlled network will be assembled using a number of switches and three SCinet 100Gbs connections from DC to Denver. We will show dynamic deployment of complex production quality (uncompressed), live, 4K video processing workflows, with rapid redeployment to provide for different needs and leverage available nationally distributed resources – relevant to emerging defense and intelligence distributed data processing challenges. Our remote I/O strategy including 100G RDMA extension of the gstreamer framework allows data processing as the data stream arrives rather than waiting for bulk transfers.
Collaborating organizations: Naval Research Laboratory, University of Missouri, International Center for Advanced Internet Research, Northwestern University (iCAIR), Open Commons Consortium (OCC), Laboratory for Advanced Computing University of Chicago (LAC), Defense Research and Engineering Network (DREN), Energy Science Network (ESnet), Mid-Atlantic Crossroads (MAX), StarLight International/National Communications Exchange Facility Consortium, Metropolitan Research and Education Network (MREN), SCinet, and the Large Scale Networking Coordinating Group of the National Information Technology Research and Development (NITRD) program (Big Data Testbed).
Calibers: A Bandwidth Calendaring Paradigm for Science Workflows
Location: NOC 1081 (SCinet)
Science workflows require large data transfer between distributed instrument facilities, storage, and computing resources. To ensure that these resources are maximally utilized, R&E networks interconnecting these resources must ensure there is no bottleneck. However, running the network at high utilization results in congestion that results in poor end-to-end TCP throughput performance and/or fairness. This in turn leads to un-predictability in the transfer time leading to poor utilization of distributed resources.
Calibers aims to advance state-of-the-art in traffic engineering by leveraging SDN-based network architecture and flow pacing algorithms to provide predictable data transfers performance and higher network utilization. Calibers highlights how by intelligently and dynamically shaping flows, we can maximize the number of flows that achieve deadline while improving network resource utilization.
Calibers is able to (1) demonstrate workflow intent and network controller interaction to continually assess bandwidth allocation per flow, and (2) implement an optimization algorithm that actively manages calendaring of flow deadlines. These allow actions to perform dynamic rate shaping at the edge, allowing better network utilization.
Dynamic CEPH Provisioning for Authentication Federations
Booth 471 (Michigan State University / University of Michigan)
Scientific collaboration on large datasets can be a challenging problem which diverts time from core research goals. A major motivation for the OSiRIS project is reducing the barriers for access to and sharing of research data storage, and a core part of achieving that goal is leveraging authentication federations that many institutions already participate in. We will demonstrate that it is possible to link these federated credentials to the Ceph storage platform and make the enrollment and provisioning process for new users relatively easy – so easy that anyone walking by our booth can get started, move data into our system, and work directly with that data from their own systems
ATLAS Machine Learning on the Pacific Research Platform
Location: Booth 663 (California Institute of Technology / CACR)
The basic theme of the demo is to show the ability to perform ATLAS machine learning based simulation and analysis on a distributed platform comprised of shared GPU machines connected by high speed networks that allow transparent delivery of data through a distributed Ceph file system. The Pacific Research Platform provides this infrastructure. The lessons learned will inform the development of models for the next generation of ATLAS tools targeted for the High Luminosity Upgrade of the LHC.
HEPCloud Distributed Caching Demo
Location: Booth 663 (California Institute of Technology / CACR)
LHC production jobs targeted in this exercise consist of a sequence of processes, with all but one of them doing either no read or read from worker node. Only one process reads data both locally and remotely using XrootD . The remote read has the characteristic that the data is not read fully sequentially once, but with random skip through, and potentially many times over by all concurrent jobs. The baseline setup we want to consider is when the remotely accessed dataset is on the storage of one of remote computer center and data is brought on demand next to the compute.
Improved Monitoring and Performance in the Network Data Plane for the LHC Grid
Location: Booth 663 (California Institute of Technology / CACR)
In complex networks, such as the USCMS Tier1 facility at Fermilab and the Caltech Tier2 cluster, which support large-scale collaborative science projects, such as the Large Hadron Collider’s (LHC) Compact Muon Solenoid (CMS) experiment, detecting connectivity and performance problems is a very challenging task.
One primary objective of this investigation is to design techniques, develop monitoring systems at application level and also at the switch level which builds basic infrastructure to detect, troubleshoot and resolve pairwise connectivity and performance problems. The other main objective is improving the network performance between LHC sites.
High Throughput Flows Between North and South Hemispheres Using Kytos, an Innovative SDN Platform
Location: Booth 1491 (Sao Paulo State University)
Data-intensive, globally distributed science programs still face problems related to the need of handling huge dataset transfers between endpoints widely distributed on the globe. Orchestration of network resources to efficiently use the network infrastructure, as well as diagnosing network problems, are still big challenges that need to be addressed. Most administrators are still stuck with traditional network tools for daily administrative and debugging tasks and tethered to proprietary vendor solutions. SDN is a promising technology that aims to open interfaces of proprietary networking devices to improve orchestration and to enable innovation. In this demo we will exercise and stress multiple 100 Gbps WAN links between the United States and South America using Kytos, a new, innovative open source SDN platform. This demo will also exercise the OpenWave project, an experimental 100G alien wave that comprises a total span of approximately 10,000km.
Disk-to-disk Data Transfer at 100G
Location: Booth 1653 (Center for Data Intensive Science – Open Commons Consortium)
Compute Canada and regional partners are deploying and managing advanced research computing (ARC) resources consisting of multi-petabytes storage systems and well over 100,000 compute cores. These resources, located across four sites in Canada, are used by Canadian researchers from any region in the country.
Multi-purpose GP-GPU Cluster for Machine Learning Fast Prototyping, Medium Scale Training and Knowledge Dissemination
Location: Booth 663 (California Institute of Technology / CACR)
During the recent decades of evolution of machine learning we have witnessed the advent of deep learning in tackling many computer vision tasks and other pattern recognition. This revival has been due to three factors : the discovery of several experimental method to train large models, large labelled datasets have been made available and fast development of dedicated computing hardware. Deep learning is “now everywhere”, in many everyday applications. There is a general trend of adoption of modern machine learning and deep learning method within the field of High Energy Physics. Some methods are possible avenue to tackle some of the data-hyper-intensive computation that will be required in the next decades. While there is a large amount of computing resource available and used by the community, these resources are not necessarily usable for fast prototyping and quick turn-around during exploration of new techniques. We propose a multi-GPU cluster architecture and specific multi-purpose usage to improve the return on investment and accelerate the science.
PRP Multi-Institution Hyper-Converged ScienceDMZ
Location: Booth 525 (SDSC)
The goal is to bring GIFEE “Google’s infrastructure for everyone else” to ScienceDMZs and HPC. The science running on computers is becoming more and more collaborative and is driving new requirements for federation and orchestration of HPC assets. Kubernetes has emerged as the container orchestration engine of choice for many cloud providers. Google, Amazon, Microsoft and many others already support Kubernetes. This “pattern” has reached its tipping point out in the wild but not in HPC and ScienceDMZs. Our testbed on the NSF funded Pacific Research Platform is well positioned to support experiments and research that stress the limits of distance and performance while maintaining security and domain isolation needed to enable multi institution collaboration.
CMS.VR
Location: Booth 663 (California Institute of Technology / CACR)
CMS.VR provides a virtual reality based interactive experience of high-energy proton-proton collisions in the CMS detector at the CERN Large Hadron Collider. Using the HTC Vive VR headset and hand controllers, CMS.VR puts the user inside the CMS collision hall at Point 5 of the LHC, with the full 3D geometry of the CMS detector. The user can display actual CMS collision events with reconstructed particle tracks, electrons, and muons rendered as tubes, and energy deposits in the hadron and electromagnetic calorimeters rendered as rectangular prisms. The user can move around inside the collision and interrogate the reconstructed physics objects to extract their detailed properties. An LSTM network that performs event classification is visualized as well.
MMCFTP’s Data Transfer Experiment Using Three 100Gbps Lines Between Japan and USA
Location: Booth 973 (NICT)
Massively Multi-Connection File Transfer Protocol (MMCFTP) [1] is a new file transfer protocol designed for big data sharing of advanced research projects and data intensive science. MMCFTP achieved 150 Gbps data transfer speed between Tokyo and Salt Lake using two 100 Gbps lines between Japan and USA in SC16. A new 100 Gbps route via Singapore will be available October this year. In SC17, we will try MMCFTP’s data transfers using three 100 Gbps lines between Japan and USA. The target speed will be 210 Gbps, up to 240 Gbps. This speed will become a new record of intercontinental class long distance network data transfer rate using a single host pair.
Global Virtualization Services (GVS) for Distributed Applications and Advanced Network Services
Location: Booth 267 (GEANT)
The Global Virtualization Services allows networked applications to dynamically acquire cyber-infrastructure objects such as computational platforms, switching and forwarding elements, storage assets, and/or other custom components or instruments, along with transport circuits interconnecting these components to create customized high performance virtual networks distributed across a global footprint. This demonstration will show high performance virtualized networked environments being set up dynamically across facilities maintained in North America by Ciena Research, and facilities maintained in Europe by NORDUnet and the GEANT Network. The demonstration will show how such virtualized services can be established easily and rapidly, and can be flexibly re-configured as the distributed application requirements change, and how these virtual environments perform at real hardware levels despite their “virtual” service model.
The Global Virtualization Services
Location: Booth 267 (GEANT)
(GVS) are based upon a Generic Virtualization Model (GVM) developed in Europe under the GEANT Project and European Commission funding. The GVM is an open architecture that defines a standard lifecycle for virtual objects, their attributes, and a means of linking virtual objects to one another to create sophisticated high performance and highly dynamic networked environments. This GVS concept allows new or experimental services and applications to be deployed easily on a global scale in insulated and isolated virtual networks allowing these emerging services to co-exist safely with other applications and services and to evolve at scale and in place into new mature production services.
Corsa Managed Filtering Capability
Location: NOC 1081 (SCinet)
This SCinet NRE demonstration showcases a managed packet filtering capability that enables network security without compromising network performance. An evolution in security architecture is achieved through the separation and simplification of security functions found in today’s traditional, all-inclusive network security solutions. This function-based architecture will be used to demonstrate 10G and 100G in-line filtering with constant packet rate forwarding performance at 150 Mpps / 100G. In addition to filtering and rate-limiting the platform performs protocol validation, offers traffic statistics for every rule, and enables other security functions. This demonstration is accomplished by deploying Corsa NSE7000 devices into the main distribution framework of the SCinet architecture.
Corsa Network Hardware Virtualization
Location: NOC 1081 (SCinet)
This SCinet NRE demonstration will provide network researchers programmatic control over their own isolated, OpenFlow-based switch virtual forwarding contexts (VFC). In parallel, this demonstration will provide the network operators with the tools and
confidence needed to allow network researchers this level of programmatic forwarding control. This will be accomplished using a SCinet infrastructure built on the Corsa DP2000 product family and its virtualization features. In this demonstration the network operator will be the SCinet Network Operations Center (NOC) who will configure and monitor these VFCs at the request of the network researchers. We will demonstrate and enforce the boundaries between the SCinet NOC administrative domain and the network researchers (i.e. user’s) administrative domain through the Corsa DP2000 virtualization features.
PerfSONAR and SDNTrace Hop-by-Hop Network Troubleshooting for Flows of Interest
Location: Booth 1635 (University of Utah)
End-to-End Network troubleshooting requires the visibility on a hop-by-hop basis regardless of layer 3 and above protocol stack. End-to-End troubleshooting should also be able to look at “flows of interest” in “virtual paths”. This demo is the start of exploration of using SDNTrace, perfSONAR, and other tools to look at these “virtual paths” on a network hop-by-hop. The exploration will validate the “virtual path” by starting a client and dynamically placing the tools in the “virtual path”.
The SC conference series has traditionally been home to cutting-edge developments in high-performance networking, alongside those in high-performance computing, storage, and analytics. SCinet is soliciting proposals from research and industry participants that displayed new or innovative demonstrations in network testbeds, emerging network hardware, protocols, and advanced network intensive scientific applications.
DEMO TOPICS
Topics for this year’s Network Research Exhibition demos and experiments include (but are not limited to):
- Software-defined networking
- Novel network architecture
- Switching and routing
- Alternative data transfer protocols
- Network monitoring and management, and network control
- Network Security/encryption and resilience
- Open clouds and storage area networks
- HPC-related usage of GENI Racks
A selection of NRE demonstrations will be invited to be on a panel in a half-day SC17 Workshop titled, “Innovating the Network for Data Intensive Science” taking place on Sunday, November 12, 2017.
