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Fisk, I.

Publications and source records attributed to Fisk, I..

Snowmass 2021 Computational Frontier CompF4 Topical Group Report Storage and Processing Resource Access

Computing plays a significant role in all areas of high energy physics. The Snowmass 2021 CompF4 topical group's scope is facilities R&D, where we consider "facilities" as the computing hardware and software infrastructure inside the data centers plus the networking between data centers, irrespective of who owns them, and what policies are applied for using them. In other words, it includes commercial clouds, federally funded High Performance Computing (HPC) systems for all of science, and systems funded explicitly for a given experimental or theoretical program. This topical group report summarizes the findings and recommendations for the storage, processing, networking and associated software service infrastructures for future high energy physics research, based on the discussions organized through the Snowmass 2021 community study.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Evolution of the Distributed Computing Model of the CMS experiment at the LHC

The Computing Model of the CMS experiment was prepared in 2005 and described in detail in the CMS Computing Technical Design Report. With the experience of the first years of LHC data taking and with the evolution of the available technologies, the CMS Collaboration identified areas where improvements were desirable. In this work we describe the most important modifications that have been, or are being implemented in the Distributed Computing Model of CMS. The Worldwide LHC computing Grid (WLCG) project acknowledged that the whole distributed computing infrastructure is impacted by this kind of changes that are happening in most LHC experiments and decided to create several Technical Evolution Groups (TEG) aiming at assessing the situation and at developing a strategy for the future. In this work we describe the CMS view on the TEG activities as well.

Grandi, C.↗

Early experience on using glidein WMS in the cloud

Cloud computing is steadily gaining traction both in commercial and research worlds, and there seems to be significant potential to the HEP community as well. However, most of the tools used in the HEP community are tailored to the current computing model, which is based on grid computing. One such tool is glideinWMS, a pilot-based workload management system. In this paper we present both what code changes were needed to make it work in the cloud world, as well as what architectural problems we encountered and how we solved them. Benchmarks comparing grid, Magellan, and Amazon EC2 resources are also included.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Using Amazon's Elastic Compute Cloud to dynamically scale CMS computational resources

Large international scientific collaborations such as the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider have traditionally addressed their data reduction and analysis needs by building and maintaining dedicated computational infrastructure. Emerging cloud computing services such as Amazon's Elastic Compute Cloud (EC2) offer short-term CPU and storage resources with costs based on usage. These services allow experiments to purchase computing resources as needed, without significant prior planning and without long term investments in facilities and their management. We have demonstrated that services such as EC2 can successfully be integrated into the production-computing model of CMS, and find that they work very well as worker nodes. The cost-structure and transient nature of EC2 services makes them inappropriate for some CMS production services and functions. We also found that the resources are not truely 'on-demand' as limits and caps on usage are imposed. Our trial workflows allow us to make a cost comparison between EC2 resources and dedicated CMS resources at a University, and conclude that it is most cost effective to purchase dedicated resources for the 'base-line' needs of experiments such as CMS. However, if the ability to use cloud computing resources is built into an experiment's software framework before demand requires their use, cloud computing resources make sense for bursting during times when spikes in usage are required.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

CMS Distributed Computing Integration in the LHC sustained operations era

After many years of preparation the CMS computing system has reached a situation where stability in operations limits the possibility to introduce innovative features. Nevertheless it is the same need of stability and smooth operations that requires the introduction of features that were considered not strategic in the previous phases. Examples are: adequate authorization to control and prioritize the access to storage and computing resources, improved monitoring to investigate problems and identify bottlenecks on the infrastructure, increased automation to reduce the manpower needed for operations, effective process to deploy in production new releases of the software tools. We present the work of the CMS Distributed Computing Integration Activity that is responsible for providing a liaison between the CMS distributed computing infrastructure and the software providers, both internal and external to CMS. In particular we describe the introduction of new middleware features during the last 18 months as well as the requirements to Grid and Cloud software developers for the future.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗