More Like this

1. Building the SLATE Platform

   https://doi.org/10.1145/3219104.3219144  

   Breen, Joe; McKee, Shawn; Riedel, Benedikt; Stidd, Jason; Truong, Luan; Vukotic, Ilija; Bryant, Lincoln; Carcassi, Gabriele; Chen, Jiahui; Gardner, Robert W.; et al (July 2018, Proceedings of the Practice and Experience on Advanced Research Computing)

   We describe progress on building the SLATE (Services Layer at the Edge) platform. The high level goal of SLATE is to facilitate creation of multi-institutional science computing systems by augmenting the canonical Science DMZ pattern with a generic, "programmable", secure and trusted underlayment platform. This platform permits hosting of advanced container-centric services needed for higher-level capabilities such as data transfer nodes, software and data caches, workflow services and science gateway components. SLATE uses best-of-breed data center virtualization and containerization components, and where available, software defined networking, to enable distributed automation of deployment and service lifecycle management tasks by domain experts. As such it will simplify creation of scalable platforms that connect research teams, institutions and resources to accelerate science while reducing operational costs and development cycle times. 

   more » « less
2. Science Gateway Development to aid Cyber and Software Automation for Neuroscience Researchers and Educators

   Calyam, P.; Nair, S.S (January 2018, 13th Gateway Computing Environments Conference (Gateways))

   Neuroscientists are increasingly relying on parallel and distributed computing resources for analysis and visualization of their neuron simulations. This requires expert knowledge of programming and cyberinfrastructure configuration, which is beyond the repertoire of most neuroscience programs. This paper presents early experiences from a one-credit graduate research training course titled ECE 8001 “Software and Cyber Automation in Neuroscience” at the University of Missouri for engendering multi-disciplinary collaborations between computational neuroscience and cyberinfrastructure students and faculty. Specifically, we discuss the course organization and exemplar outcomes involving a next-generation science gateway for training novice users on exemplar neuroscience use cases that involve using tools such as NEURON and MATLAB on local as well as Neuroscience Gateway resources. We also discuss our vision towards a course sequence curriculum for graduate/undergraduate students from biological/psychological sciences and computer science/engineering to jointly build “self- service” training modules using Jupyter Notebook platforms. Thus, our efforts show how we can create scalable and sustainable cyber and software automation for fulfilling a broad set of neuroscience research and education use cases. 

   more » « less
3. Extending Tapis Workflow Management Framework with Elastic Google Cloud Distributed System using CloudyCluster by Omnibond

   Eric Lam; Sean Cleveland; Cole Mcknight; Boyd Wilson; Richard Cardone; Maytal Dahan; Joe Stubbs; Gwen A. Jacobs (October 2022, Science Gateways 2022)

   The goal of a robust cyberinfrastructure (CI) ecosystem is to catalyse discovery and innovation. Tapis does this through offering a sustainable production-quality set of API services to support modern science and engineering research, which increasingly span geographically distributed data centers, instruments, experimental facilities, and a network of national and regional CI. Leveraging frameworks, such as Tapis, enables researchers to accomplish computational and data-intensive research in a secure, scalable, and reproducible way and allows them to focus on their research instead of the technology needed to accomplish it. This project aims to enable the integration of the Google Cloud Platform (GCP) and CloudyCluster resources into Tapis- supported science gateways to provide on-demand scaling needed by computational workflows. The new functionality uses Tapis event-driven Abaco Actors and CloudyCluster to create an elastic distributed cloud computing system on demand. This integration allows researchers and science gateways to augment cloud resources on top of existing local and national computing resources. 

   more » « less
4. Coffea-Casa: Building composable analysis facilities for the HL-LHC

   https://doi.org/10.1051/epjconf/202429507009  

   Albin, Sam; Attebury, Garhan; Bloom, Kenneth; Bockelman, Brian; Lundstedt, Carl; Shadura, Oksana; Thiltges, John (January 2024, EPJ Web of Conferences)

   De_Vita, R; Espinal, X; Laycock, P; Shadura, O (Ed.)

   The large data volumes expected from the High Luminosity LHC (HL-LHC) present challenges to existing paradigms and facilities for end-user data analysis. Modern cyberinfrastructure tools provide a diverse set of services that can be composed into a system that provides physicists with powerful tools that give them straightforward access to large computing resources, with low barriers to entry. The Coffea-Casa analysis facility (AF) provides an environment for end users enabling the execution of increasingly complex analyses such as those demonstrated by the Analysis Grand Challenge (AGC) and capturing the features that physicists will need for the HL-LHC. We describe the development progress of the Coffea-Casa facility featuring its modularity while demonstrating the ability to port and customize the facility software stack to other locations. The facility also facilitates the support of batch systems while staying Kubernetes-native. We present the evolved architecture of the facility, such as the integration of advanced data delivery services (e.g. ServiceX) and making data caching services (e.g. XCache) available to end users of the facility. We also highlight the composability of modern cyberinfrastructure tools. To enable machine learning pipelines at coffee-casa analysis facilities, a set of industry ML solutions adopted for HEP columnar analysis were integrated on top of existing facility services. These services also feature transparent access for user workflows to GPUs available at a facility via inference servers while using Kubernetes as enabling technology. 

   more » « less
5. Towards a NoOps Model for WLCG

   https://doi.org/https://doi.org/10.1051/epjconf/202024507024  

   Gardner, Robert; Bryant, Lincoln; Stephen, Judith; Vukotic, Ilija; Weaver, Christopher; Wu, Wenjing (November 2020, 24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019))

   null (Ed.)

   One of the most costly factors in providing a global computing infrastructure such as the WLCG is the human effort in deployment, integration, and operation of the distributed services supporting collaborative computing, data sharing and delivery, and analysis of extreme scale datasets. Furthermore, the time required to roll out global software updates, introduce new service components, or prototype novel systems requiring coordinated deployments across multiple facilities is often increased by communication latencies, staff availability, and in many cases expertise required for operations of bespoke services. While the WLCG (and distributed systems implemented throughout HEP) is a global service platform, it lacks the capability and flexibility of a modern platform-as-a-service including continuous integration/continuous delivery (CI/CD) methods, development-operations capabilities (DevOps, where developers assume a more direct role in the actual production infrastructure), and automation. Most importantly, tooling which reduces required training, bespoke service expertise, and the operational effort throughout the infrastructure, most notably at the resource endpoints (sites), is entirely absent in the current model. In this paper, we explore ideas and questions around potential NoOps models in this context: what is realistic given organizational policies and constraints? How should operational responsibility be organized across teams and facilities? What are the technical gaps? What are the social and cybersecurity challenges? Conversely what advantages does a NoOps model deliver for innovation and for accelerating the pace of delivery of new services needed for the HL-LHC era? We will describe initial work along these lines in the context of providing a data delivery network supporting IRIS-HEP DOMA R&D. 

   more » « less