The evolving nature of electric power distribution systems is motivating the development of advanced applications for utility planning, operation, management, and control. These model-based applications interact with multiple subsystems within software platforms referred to as advanced distribution management systems. Recently, the Common Information Model has been used to enable data model standardization across several entities. Model fidelity is the key to the development of leading-edge, scalable model-based algorithms and assessment of emerging technologies such as distributed energy resources for grid planning and operation. However, distribution system models are error-prone and model validation is challenging due to: (1) the need for considerable time and effort to maintain the network models, (2) update delays due to the evolving nature of the real-world systems that models are derived from, and (3) inevitable human errors in the process which are difficult to detect. In this paper, we extend a model validation application from our prior work referred to as the Model Validator to derive and validate a system admittance matrix using the Common Information Model. First, we introduce different modules for computing device-level primitive admittance matrices from Common Information Model parameters. These individual primitive admittance matrices are then used to validate corresponding entries in a full system admittance matrix. In addition, we validate the overall system admittance matrix by checking for gaps in coverage or extra elements that are not expected. The proposed approach checks the validity of each piece of network model equipment separately and identifies inconsistencies between Common Information Model parameters and the system admittance matrix at the device level. Furthermore, where devices can be specified using different combinations of Common Information Model parameters, each parameter combination is validated separately. This breakdown can help distribution engineers more efficiently apply root cause analysis for inconsistencies. The Model Validator application is implemented on top of GridAPPS-D, an open-source standards-based platform for advanced distribution management system application development. The effectiveness of the proposed application is demonstrated on six different test cases: four IEEE feeders, the Electric Power Research Institute J1 circuit, and a taxonomy feeder.

We propose an approach for improved reproducibility that includes capturing and relating provenance characteristics and performance metrics. We discuss two use cases: scientific reproducibility of results in the Energy Exascale Earth System Model (E3SM—previously ACME) and performance reproducibility in molecular dynamics workflows on HPC platforms. To capture and persist the provenance and performance data of these workflows, we have designed and developed the Chimbuko and ProvEn frameworks. Chimbuko captures provenance and enables detailed single workflow performance analysis. ProvEn is a hybrid, queryable system for storing and analyzing the provenance and performance metrics of multiple runs in workflow performance analysis campaigns. Workflow provenance and performance data output from Chimbuko can be visualized in a dynamic, multilevel visualization providing overview and zoom-in capabilities for areas of interest. Provenance and related performance data ingested into ProvEn is queryable and can be used to reproduce runs. Our provenance-based approach highlights challenges in extracting information and gaps in the information collected. It is agnostic to the type of provenance data it captures so that both the reproducibility of scientific results and that of performance can be explored with our tools.

We propose an approach for improved reproducibility that includes capturing and relating provenance characteristics and performance metrics, in a hybrid queriable system, the ProvEn server. The system capabilities are illustrated on two use cases: scientific reproducibility of results in the ACME climate simulations and performance reproducibility in molecular dynamics workflows on HPC computing platforms.

The Belle II experiment at KEK is preparing for taking first collision data in early 2018. For the success of the experiment it is essential to have information about varying conditions available to systems worldwide in a fast and efficient manner that is straightforward for both the user and maintainer. The Belle II Conditions Database was designed to make maintenance as easy as possible. To this end, a HTTP REST service was developed with industry-standard tools such as Swagger for the API interface development, Payara for the Java EE application server, and the Hazelcast in-memory data grid for support of scalable caching as well as transparent distribution of the service across multiple sites. On the client side, the online and offline software has to be able to obtain conditions data from the Belle II Conditions Database in a robust and reliable way under very different situations. As such the client side interface to the Belle II Conditions Database has been designed with a variety of access mechanisms which allow the software to be used with and without an internet connection. Different methods to access the payload information are implemented to allow for a high level of customization per site and to simplify testing of new payloads locally. Changes to the conditions data are usually handled transparently but users can actively check whether an object has changed or register callback functions to be called whenever a conditions data object is updated. In addition a command line user interface has been developed to simplify inspection and modification of the database contents.

The Distribution Common Information Model (CIM) offers a means for interoperable distribution system applications. Adoption has been slow in part because of the steep learning curve, and the mismatch between CIMu0027s object-oriented schema and widely-used relational database schemas. We evaluated alternative database technologies, and tested a triple-store database with SPARQL queries on a set of IEEE distribution test feeders. These alternative queries were much simpler to design and use, so they should help lower one of the important barriers to CIM adoption. An open-source platform has been provided for other developers to work with CIM in this way. One goal is to more rapidly develop interoperable ADMS and DERMS applications. Another use is to convert feeder models between commercial CIM-compliant products and research software like GridLAB-D and OpenDSS.

Modern electric power distribution systems are data rich and include growing numbers of distributed energy resources and distribution automation. To take advantage of distribution automation and manage growing penetrations of distributed energy resources, distribution utilities need applications for planning and operations that use all available data and may incorporate distributed approaches to operate and control. The industry would benefit from distribution management applications based on a common platform that makes systems of each type interchangeable. This paper describes an approach to enabling cost-effective development and deployment of advanced applications for distribution system planning and operations based on development of an open-source, standards-based platform for application development called GridAPPS-D, which leverages data abstractions for application development based on standards, such as the distribution system common information model.

The Belle II experiment at KEK is preparing for first collisions in 2017. Processing the large amounts of data that will be produced will require conditions data to be readily available to systems worldwide in a fast and efficient manner that is straightforward for both the user and maintainer. The Belle II conditions database was designed with a straightforward goal: make it as easily maintainable as possible. To this end, HEP-specific software tools were avoided as much as possible and industry standard tools used instead. HTTP REST services were selected as the application interface, which provide a high-level interface to users through the use of standard libraries such as curl. The application interface itself is written in Java and runs in an embedded Payara-Micro Java EE application server. Scalability at the application interface is provided by use of Hazelcast, an open source In-Memory Data Grid (IMDG) providing distributed in-memory computing and supporting the creation and clustering of new application interface instances as demand increases. The IMDG provides fast and efficient access to conditions data via in-memory caching.

We propose a novel approach for efficient execution of workflows on distributed resources. The key components of this framework include: performance modeling to quantitatively predict workflow component behavior; optimization-based scheduling such as choosing an optimal subset of resources to meet demand and assignment of tasks to resources; distributed I/O optimizations such as prefetching; and provenance methods for collecting performance data. In preliminary results, these techniques improve throughput on a small Belle II workflow by 20%.

Distributed computing platforms provide a robust mechanism to perform large-scale computations by splitting the task and data among multiple locations, possibly located thousands of miles apart geographically. Although such distribution of resources can lead to benefits, it also comes with its associated problems such as rampant duplication of file transfers increasing congestion, long job completion times, unexpected site crashing, suboptimal data transfer rates, unpredictable reliability in a time range, and suboptimal usage of storage elements. In addition, each sub-system becomes a potential failure node that can trigger system wide disruptions. In this vision paper, we outline our approach to leveraging Deep Learning algorithms to discover solutions to unique problems that arise in a system with computational infrastructure that is spread over a wide area. The presented vision, motivated by a real scientific use case from Belle II experiments, is to develop multilayer neural networks to tackle forecasting, anomaly detection and optimization challenges in a complex and distributed data movement environment. Through this vision based on Deep Learning principles, we aim to achieve reduced congestion events, faster file transfer rates, and enhanced site reliability.

Challenges that make it difficult to find, share, and combine published data, such as data heterogeneity and resource discovery, have led to increased adoption of semantic data standards and data publishing technologies. To make data more accessible, interconnected and discoverable, some domains are being encouraged to publish their data as Linked Data. Consequently, this trend greatly increases the amount of data that semantic web tools are required to process, store, and interconnect. In attempting to process and manipulate large data sets, tools -- ranging from simple text editors to modern triplestores -- eventually breakdown upon reaching undefined thresholds. This paper shares our experiences in curating metadata, primarily to illustrate the challenges, and resulting limitations that data publishers and consumers have in the current technological environment. This paper also provides a Linked Data based solution to the research problem of resource discovery, and offers a systematic approach that the data publishers can take to select suitable tools to meet their data publishing needs. We present a real-world use case, the Resource Discovery for Extreme Scale Collaboration (RDESC), which features a scientific dataset(maximum size of 1.4 billion triples) used to evaluate a toolbox for data publishing in climate research. This paper also introduces a semantic data publishing software suite developed for the RDESC project.

Data provenance has been an active area of research as a means to standardize how the origin of data, process event history, and what or who was responsible for influencing results is explained. The common trend in capturing provenance is to collect all possible information, and then attempt to extract relevant information from the entire collection, which is not efficient especially at runtime. Unless augmented, existing disclosed provenance system APIs do not work well in distributed environments and compound the problem of extracting and reassembling relevant provenance to provide a seamless coherent historical understanding of distributed activities. This paper focuses on determining more standardized solutions for provenance capture conducive to the scientific research. As part of the Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows (IPPD) project, an API was developed, called Producer API (PAPI), which can disclose application targeted provenance, designed to work in distributed environments by means of unique object identification methods. The API also provides the flexibility to let the user decide what to do with the collected provenance and supports large-scale server based and standalone applications.

Workflow systems are in wide use in the scientific community today, facilitating complex computational and analytical processes. Their increasing popularity is particularly visible at workflow sharing sites such as MyExperiment [1] or Galaxy [2-4]. High-performance computing (HPC) users also are looking toward workflow solutions to manage their complex preand post-processing needs. This trend likely will continue with the advent of exascale architectures, which will require extreme-scale collaborations between applications running on an exascale system and community data and knowledge repositories needed for their validation and steering [5, 6]. A new emerging use for workflows is the in-situ / streaming, often adaptive analysis of large scale simulation runs and as well as the need to analyze and interpret experimental results [10], in both cases to steer the scientific work and optimize the scientific outcome. In particular in this last case the reliably performance of the workflow is absolutely key to its usefulness.

Today's dynamic computing deployment for commercial and scientific applications is propelling us to an era where minor inefficiencies can snowball into significant performance and operational bottlenecks. Data center operations is increasingly relying on sensors based control systems for key decision insights. The increased sampling frequencies, cheaper storage costs and prolific deployment of sensors is producing massive volumes of operational data. However, there is a lag between rapid development of analytical techniques and its widespread practical deployment. We present empirical evidence of the potential carried by analytical techniques for operations management in computing and data centers. Using Machine Learning modeling techniques on data from a real instrumented cluster, we demonstrate that predictive modeling on operational sensor data can directly reduce systems operations monitoring costs and improve system reliability.

As high performance computing (HPC) infrastructures continue to grow in capability and complexity, so do the applications that they serve. HPC and distributed-area computing (DAC) (e.g. grid and cloud) users are looking increasingly toward workflow solutions to orchestrate their complex application coupling, pre- and post-processing needs. To that end, the US Department of Energy Integrated end-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows (IPPD) project is currently investigating an integrated approach to prediction and diagnosis of these extreme-scale scientific workflows. To gain insight and a more quantitative understanding of a workflow's performance our method includes not only the capture of traditional provenance information, but also the capture and integration of system environment metrics helping to give context and explanation for a workflow's execution. In this paper, we describe IPPD's provenance management solution (ProvEn) and its hybrid data store combining both of these data provenance perspectives. We discuss design and implementation details that include provenance disclosure, scalability, data integration, and a discussion on query and analysis capabilities. We also present use case examples for climate modeling and thermal modeling application domains.

Technological advancements such as the World Wide Web brought together business, engineering, and scientific communities, creating collaborative opportunities that were previously unimaginable. Provenance originates from the French term provenir, which is used to describe the curation process of artwork as art is passed from owner to owner. Data provenance, in a similar way, allows producers to pass data context to consumers by providing vocabularies and methodologies for collaborators to share the origin of anything, both digital and nondigital. Scientists found that changes of one physical input parameter can potentially have significant impact on the overall results. Identifying, understanding, and ranking sources of uncertainty is a vital part the knowledge scientists need to convey when sharing their results with the research community. The era of scientific workflows ushered in new interest by scientists seeking to tie together many disparate applications and data sources and have a record of what transpired during the workflow execution.

Mass spectrometry imaging (MSI) enables simultaneous spatially-resolved analysis of numerous ionizable molecules on a sample surface. Ambient ionization techniques are attractive because they enable imaging without sample pretreatment. The current analysis pipeline involves analyzing the data coming off the instrument using a tool called MSI Quickview and saving the results onto a storage drive before moving on to the next experiment. While this works well for single datasets, there is a demand for more scalable, flexible workflows that are re-executable across datasets, support extensive querying and ease collaboration. Here, we present a workflow that moves data analysis from a mere desktop application for single experiments to a more general capability that can be possibly extended to perform multi-modal analysis across datasets. The core components of the workflow include (1) MSI Quickview, a desktop application for the near-real time visualization and analysis of mass spectrometry data; (2) Provenance Environment (ProvEn), a provenance production and collection framework that provides components supporting the production and collection of provenance information for distributed application environments; (3) Elasticsearch, a readily-scalable, broadly-distributable, enterprise-grade search engine that is accessible through an elaborate and extensive API to power extremely fast indexing and searches that support your data discovery applications; (4) Logstash, processing of log files; and (5) Kibana, a platform to visualize, analyze and explore data from multiple sources including Elasticsearch and Logstash.