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Background: Kangaroo Mother Care (KMC) requires few specialized resources, reduces mortality, and can be implemented in low resource environments. It is now recommended for implementation around the globe. Objectives: This paper discusses KMC use with low birth weight newborns at a Tanzania hospital providing valuable outcomes assessment of KMC use in rural areas of sub-Saharan Africa. Methods: The research used retrospective records (136 Low Birth Weight (LBW) and 33 Very Low Birth Weight (VLBW) ne- onates) supplemented by observations. Data included weights (longitudinal), survival period, and cause of death if it occurred. This hospital’s KMC use and study data began when the mother-baby dyad was referred, usually birth day. Results: This KMC group demonstrated 70% survival, but 77% of deaths occur within 24 hours. After the first 24 hours, KMC survival rate was 92%. Even VLBW neonates (

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work presented here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence., Comment: To appear in the journal of Future Generation Computer Systems

The Software-defined network for End-to-end Networked Science at Exascale (SENSE) research project is building smart network services to accelerate scientific discovery in the era of 'big data' driven by Exascale, cloud computing, machine learning and AI. The project's architecture, models, and demonstrated prototype define the mechanisms needed to dynamically build end-to-end virtual guaranteed networks across administrative domains, with no manual intervention. In addition, a highly intuitive 'intent' based interface, as defined by the project, allows applications to express their high-level service requirements, and an intelligent, scalable model-based software orchestrator converts that intent into appropriate network services, configured across multiple types of devices. The significance of these capabilities is the ability for science applications to manage the network as a first-class schedulable resource akin to instruments, compute, and storage, to enable well defined and highly tuned complex workflows that require close coupling of resources spread across a vast geographic footprint such as those used in science domains like high-energy physics and basic energy sciences.

Today's scientific computing applications and workflows operate on heterogeneous and vastly distributed infrastructures. Traditional human-in-the-loop service engineering approach met its insurmountable challenge in dealing with these very complex and diverse networked systems, including conventional and software defined networks, compute, storage, clouds and instruments. Orchestration is the key to integrate and coordinate the networked multi-services and automate end-to-end workflows. In this work, we present a model driven intelligent orchestration approach to this end-to-end automation, which is built upon a semantic modeling solution that supports the full stack of service integration, orchestration, abstraction, and intent and policy representation. We also present the design of a real-world orchestrator called StackV that is able to accommodate highly complex application scenarios such as Software Defined ScienceDMZ (SD-SDMZ) and Hybrid Cloud Inter-Networking (HCIN) by implementing this approach.

For 25 years, SCinet has provided SC conference attendees and the high-performance computing (HPC) community with a one-of-a-kind research network utilizing advanced technologies to internationally interconnect, interoperate, and showcase HPC applications in conjunction with scientific and network research at SC. SCinet is a focal point for the international high-performance networking community. It is the place for network engineers, network service providers, equipment vendors, and application developers in the diverse HPC communities to collaborate in demonstrating the latest research, innovative applications, and advanced technologies.The event is made possible by the tireless efforts of over a hundred volunteers and the generous contributions of dozens of vendors and service providers. It requires a year to plan and two weeks to build, culminating in an operational network for four days, which then is dismantled, packed, and shipped in 24 hours.SCinet makes a major contribution to the advanced networking field by bringing together unique resources in the form of hardware, software, and services along with expertise to architect, build and operate a state-of-the-art network to enable data-intensive science applications.

iGrid2005's goal was to demonstrate the potential of hybrid networks showing applications that require the use of these high-performance multi-gigabit networks. In this article we describe the network infrastructure in place during the workshop, we focus on the innovative and original aspects of the set-up, and we show that the infrastructure was a clear example of lambda networking.

High-end networked applications such as distance visualization, distributed data analysis, and advanced collaborative environments have demanding quality of service (QoS) requirements. Particular challenges include concurrent flows with different QoS specifications, high-bandwidth flows, application-level monitoring and control, and end-to-end QoS across networks and other devices. We describe a QoS architecture and implementation that together help to address these challenges. The General-purpose Architecture for Reservation and Allocation (GARA) supports flow-specific QoS specification, immediate and advance reservation, and online monitoring and control of both individual resources and heterogeneous resource ensembles. Mechanisms provided by the Globus Toolkit are used to address resource discovery and security issues when resources span multiple administrative domains. Our prototype GARA implementation builds on differentiated services mechanisms to enable the coordinated management of two distinct flow types-foreground media flows and background bulk transfers-as well as the co-reservation of networks, CPUs, and storage systems. We present results obtained on a wide area differentiated services testbed that demonstrate our ability to deliver QoS for realistic flows.

Cloud infrastructures have seen increasing popularity for addressing the growing computational needs of today's scientific and engineering applications. However, resource management challenges exist in the elastic cloud environment, such as resource provisioning and task allocation, especially when data movement between multiple domains plays an important role. In this work, we study the impact of data-aware resource management and scheduling on scientific workflows in multicloud environments. We develop a workflow simulator based on a network simulation framework for fine-grained simulation for workflow computation and data movement. Using the workload traces from a production metagenomic data analysis service, we evaluate different resource scheduling mechanisms, including proposed data-aware scheduling policies under various resource and bandwidth configurations. The results of this work are expected to answer questions about how to provision computing resources for certain workloads efficiently and how to place tasks across multidomain clouds in order to reduce data movement costs for overall improved system performance.

The IETF’s recent differentiated services (DS) architecture, which specifies a scalable mechanism for treating packets differently, offers new opportunities for building end-to-end quality of service (QoS) systems. However, it also introduces new challenges. In particular, it is not clear whether TCP’s flow and congestion control mechanisms work well with the mechanisms used for end-to-end QoS. For that reason it is essential to analyze whether the existing DS mechanisms can be used with standard TCP implementations or whether it is necessary to wait for upcoming features introduced in future modified versions of TCP. The general-purpose architecture for reservation and allocation (GARA) supports flow-specific QoS specification, immediate and advance reservation, and online monitoring and control of both individual resources and heterogeneous resource ensembles. Using GARA, we evaluated actual DS mechanisms provided by Cisco routers. We present the results of this evaluation and discuss their impact on the performance of popular TCP implementations.

A GMPLS (Generalized Multi-Protocol Label Switching)-based link restoration scheme with IP-routing stability for data link failure between routers through a PXC (Photonic Cross-Connect) network was proposed and successfully demonstrated in a US nationwide photonic network for the first time.

The Lambda-on-demand functionality with link aggregation to accommodate the dynamic bandwidth demands of an ultra-high-resolution visualization application was realized in over a US nationwide photonic network for the first time.