The ATLAS Trigger/DAQ Authorlist, version 1.0

This is a reference document giving the ATLAS Trigger/DAQ author list, version 1.0 of 20 Nov 2008

The ATLAS Trigger/DAQ Authorlist, version 2.0

This is the ATLAS Trigger/DAQ Authorlist, version 2.0, 31 July 200

The ATLAS Trigger/DAQ Authorlist, version 3.0

This is the ATLAS Trigger/DAQ Authorlist, version 3.0, 11 September 200

The ATLAS Trigger/DAQ Authorlist, version 3.1

This is the ATLAS Trigger/DAQ Authorlist, version 3.1, 17 September 200

Efficient Network Monitoring for Large Data Acquisition Systems

Though constantly evolving and improving, the available network monitoring solutions have limitations when applied to the infrastructure of a high speed real-time data acquisition (DAQ) system. DAQ networks are particular computer networks where experts have to pay attention to both individual subsections as well as system wide traffic flows while monitoring the network. The ATLAS Network at the Large Hadron Collider (LHC) has more than 200 switches interconnecting 3500 hosts and totaling 8500 high speed links. The use of heterogeneous tools for monitoring various infrastructure parameters, in order to assure optimal DAQ system performance, proved to be a tedious and time consuming task for experts. To alleviate this problem we used our networking and DAQ expertise to build a flexible and scalable monitoring system providing an intuitive user interface with the same look and feel irrespective of the data provider that is used. Our system uses custom developed components for critical performance monitoring and seamlessly integrates complementary data from auxiliary tools, such as NAGIOS, information services or custom databases. A number of techniques (e.g. normalization, aggregation and data caching) were used in order to improve the user interface response time. The end result is a unified monitoring interface, for fast and uniform access to system statistics, which significantly reduced the time spent by experts for ad-hoc and post-mortem analysis

Integrated System for Performance Monitoring of ATLAS TDAQ Network

The ATLAS TDAQ Network consists of three separate networks spanning four levels of the experimental building. Over 200 edge switches and 5 multi-blade chassis routers are used to interconnect 2000 processors, adding up to more than 7000 high speed interfaces. In order to substantially speed-up ad-hoc and post mortem analysis, a scalable, yet flexible, integrated system for monitoring both network statistics and environmental conditions, processor parameters and data taking characteristics was required. For successful up-to-the-minute monitoring, information from many SNMP compliant devices, independent databases and custom APIs was gathered, stored and displayed in an optimal way. Easy navigation and compact aggregation of multiple data sources were the main requirements; characteristics not found in any of the tested products, either open-source or commercial. This paper describes how performance, scalability and display issues were addressed and what challenges the project faced during development and deployment. A full set of modules, including a fast polling SNMP engine, user interfaces using latest web technologies and caching mechanisms, has been designed and developed from scratch. Over the last year the system proved to be stable and reliable, replacing the previous performance monitoring system and extending its capabilities. Currently it is operated using a precision interval of 25 seconds (the industry standard is 30 0 seconds). Although it was developed in order to address the needs for integrated performance monitoring of the ATLAS TDAQ network, the package can be used for monitoring any network with rigid demands of precision and scalability, exceeding normal industry standards

Integrated System for Performance Monitoring of the ATLAS TDAQ Network

The ATLAS TDAQ Network consists of three separate networks spanning four levels of the experimental building. Over 200 edge switches and 5 multi-blade chassis routers are used to interconnect 2000 processors, adding up to more than 7000 high speed interfaces. In order to substantially speed-up ad-hoc and post mortem analysis, a scalable, yet flexible, integrated system for monitoring both network statistics and environmental conditions, processor parameters and data taking characteristics was required. For successful up-to-the-minute monitoring, information from many SNMP compliant devices, independent databases and custom APIs was gathered, stored and displayed in an optimal way. Easy navigation and compact aggregation of multiple data sources were the main requirements; characteristics not found in any of the tested products, either open-source or commercial. This paper describes how performance, scalability and display issues were addressed and what challenges the project faced during development and deployment. A full set of modules, including a fast polling SNMP engine, user interfaces using latest web technologies and caching mechanisms, has been designed and developed from scratch. Over the last year the system proved to be stable and reliable, replacing the previous performance monitoring system and extending its capabilities. Currently it is operated using a precision interval of 25 seconds (the industry standard is 30 0 seconds). Although it was developed in order to address the needs for integrated performance monitoring of the ATLAS TDAQ network, the package can be used for monitoring any network with rigid demands of precision and scalability, exceeding normal industry standards

Monitoring individual traffic flows within the ATLAS TDAQ network

The ATLAS data acquisition system consists of four different networks interconnecting up to 2000 processors using up to 200 edge switches and five multi-blade chassis devices. The architecture of the system has been described in [1] and its operational model in [2]. Classical, SNMP-based, network monitoring provides statistics on aggregate traffic, but for performance monitoring and troubleshooting purposes there was an imperative need to identify and quantify single traffic flows. sFlow [3] is an industry standard based on statistical sampling which attempts to provide a solution to this. Due to the size of the ATLAS network, the collection and analysis of the sFlow data from all devices generates a data handling problem of its own. This paper describes how this problem is addressed by making it possible to collect and store data either centrally or distributed according to need. The methods used to present the results in a relevant fashion for system analysts are discussed and we explore the possibilities and limitations of this diagnostic tool, giving an example of its use in solving system problems that arise during the ATLAS data taking

Network Resiliency Implementation in the ATLAS TDAQ System

The ATLAS TDAQ (Trigger and Data Acquisition) system performs the real-time selection of events produced by the detector. For this purpose approximately 2000 computers are deployed and interconnected through various high speed networks, whose architecture has already been described. This article focuses on the implementation and validation of network connectivity resiliency (previously presented at a conceptual level). Redundancy and eventually load balancing are achieved through the synergy of various protocols: 802.3ad link aggregation, OSPF (Open Shortest Path First), VRRP (Virtual Router Redundancy Protocol), MST (Multiple Spanning Trees). An innovative method for cost-effective redundant connectivity of high-throughput high-availability servers is presented. Furthermore, real-life examples showing how redundancy works, and more importantly how it might fail despite careful planning are presented

Muon Detection Based on a Hadronic Calorimeter

The TileCal hadronic calorimeter provides a muon signal which can be used to assist in muon tagging at the ATLAS level-one trigger. Originally, the muon signal was conceived to be combined with the RPC trigger in order to reduce unforeseen high trigger rates due to cavern background. Nevertheless, the combined trigger cannot significantly deteriorate the muon detection performance at the barrel region. This paper presents preliminary studies concerning the impact in muon identification at the ATLAS level-one trigger, through the use of Monte Carlo simulations with single muons with 40 GeV/c momentum. Further, different trigger scenarios were proposed, together with an approach for matching both TileCal and RPC geometries