The Future of Distributed Computing Systems in ATLAS: Boldly Venturing Beyond Grids

Since 2010 the Production and Distributed Analysis system (PanDA) for the ATLAS experiment at the Large Hadron Colliderhas seen big changes to accommodate new types of distributed computing resources: clouds, HPCs, volunteer computers and other external resources. While PanDA was originally designed for fairly homogeneous resources available through the Worldwide LHC Computing Grid, the new resources are heterogeneous, at diverse scales and with diverse interfaces. Up to a fifth of the resources available to ATLAS are of such new types and require special techniques for integration into PanDA. In this talk, we present the nature and scale of these resources. We provide an overview of the various challenges faced, spanning infrastructure, software distribution, workload requirements, scaling requirements, workflow management, data management, network provisioning, and associated software and computing facilities. We describe the strategies for integrating these heterogeneous resources into ATLAS, and the new software components being developed in PanDA to efficiently use them. Plans for software and computing evolution to meet the needs of LHC operations and upgrade in the long term future will be discussed.

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Data-Aware Distributed Computing

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Data Intensive Distributed Computing ◽

10.4018/978-1-61520-971-2.ch001 ◽

2012 ◽

pp. 1-27 ◽

Cited By ~ 1

Author(s):

Esma Yildirim ◽

Mehmet Balman ◽

Tevfik Kosar

Keyword(s):

Distributed Computing ◽

Workflow Management ◽

Data Access ◽

New Paradigm ◽

Distributed Computing Systems ◽

Computing Systems ◽

Data Movement ◽

End To End ◽

Commercial Applications ◽

Main Components

With the continuous increase in the data requirements of scientific and commercial applications, access to remote and distributed data has become a major bottleneck for end-to-end application performance. Traditional distributed computing systems closely couple data access and computation, and generally, data access is considered a side effect of computation. The limitations of traditional distributed computing systems and CPU-oriented scheduling and workflow management tools in managing complex data handling have motivated a newly emerging era: data-aware distributed computing. In this chapter, the authors elaborate on how the most crucial distributed computing components, such as scheduling, workflow management, and end-to-end throughput optimization, can become “data-aware.” In this new computing paradigm, called data-aware distributed computing, data placement activities are represented as full-featured jobs in the end-to-end workflow, and they are queued, managed, scheduled, and optimized via a specialized data-aware scheduler. As part of this new paradigm, the authors present a set of tools for mitigating the data bottleneck in distributed computing systems, which consists of three main components: a data-aware scheduler, which provides capabilities such as planning, scheduling, resource reservation, job execution, and error recovery for data movement tasks; integration of these capabilities to the other layers in distributed computing, such as workflow planning; and further optimization of data movement tasks via dynamic tuning of underlying protocol transfer parameters.

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Preparing Distributed Computing Operations for the HL-LHC Era With Operational Intelligence

Frontiers in Big Data ◽

10.3389/fdata.2021.753409 ◽

2022 ◽

Vol 4 ◽

Author(s):

Alessandro Di Girolamo ◽

Federica Legger ◽

Panos Paparrigopoulos ◽

Jaroslava Schovancová ◽

Thomas Beermann ◽

...

Keyword(s):

Distributed Computing ◽

Use Cases ◽

Distributed Computing Systems ◽

Computing Systems ◽

Workload Management ◽

Level Of Automation ◽

Intelligence Services ◽

Operational Intelligence ◽

Computing Grid ◽

Scientific Results

As a joint effort from various communities involved in the Worldwide LHC Computing Grid, the Operational Intelligence project aims at increasing the level of automation in computing operations and reducing human interventions. The distributed computing systems currently deployed by the LHC experiments have proven to be mature and capable of meeting the experimental goals, by allowing timely delivery of scientific results. However, a substantial number of interventions from software developers, shifters, and operational teams is needed to efficiently manage such heterogenous infrastructures. Under the scope of the Operational Intelligence project, experts from several areas have gathered to propose and work on “smart” solutions. Machine learning, data mining, log analysis, and anomaly detection are only some of the tools we have evaluated for our use cases. In this community study contribution, we report on the development of a suite of operational intelligence services to cover various use cases: workload management, data management, and site operations.

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