Modelling of Amperometric Biosensor Used for Synergistic Substrates Determination

In this paper the operation of an amperometric biosensor producing a chemically amplified signal is modelled numerically. The chemical amplification is achieved by using synergistic substrates. The model is based on non-stationary reaction-diffusion equations. The model involves three layers (compartments): a layer of enzyme solution entrapped on the electrode surface, a dialysis membrane covering the enzyme layer and an outer diffusion layer which is modelled by the Nernst approach. The equation system is solved numerically by using the finite difference technique. The biosensor response and sensitivity are investigated by altering the model parameters influencing the enzyme kinetics as well as the mass transport by diffusion. The biosensor action was analyzed with a special emphasis to the effect of the chemical amplification. The simulation results qualitatively explain and confirm the experimentally observed effect of the synergistic substrates conversion on the biosensor response

Modelling of Amperometric Biosensor Used for Synergistic Substrates Determination

In this paper the operation of an amperometric biosensor producing a chemically amplified signal is modelled numerically. The chemical amplification is achieved by using synergistic substrates. The model is based on non-stationary reaction-diffusion equations. The model involves three layers (compartments): a layer of enzyme solution entrapped on the electrode surface, a dialysis membrane covering the enzyme layer and an outer diffusion layer which is modelled by the Nernst approach. The equation system is solved numerically by using the finite difference technique. The biosensor response and sensitivity are investigated by altering the model parameters influencing the enzyme kinetics as well as the mass transport by diffusion. The biosensor action was analyzed with a special emphasis to the effect of the chemical amplification. The simulation results qualitatively explain and confirm the experimentally observed effect of the synergistic substrates conversion on the biosensor response

A scalable online monitoring system based on Elasticsearch for distributed data acquisition in CMS

The part of the CMS data acquisition (DAQ) system responsible for data readout and event building is a complex network of interdependent distributed applications. To ensure successful data taking, these programs have to be constantly monitored in order to facilitate the timeliness of necessary corrections in case of any deviation from specified behaviour. A large number of diverse monitoring data samples are periodically collected from multiple sources across the network. Monitoring data are kept in memory for online operations and optionally stored on disk for post-mortem analysis. We present a generic, reusable solution based on an open source NoSQL database, Elasticsearch, which is fully compatible and non-intrusive with respect to the existing system. The motivation is to benefit from an off-the-shelf software to facilitate the development, maintenance and support efforts. Elasticsearch provides failover and data redundancy capabilities as well as a programming language independent JSON-over-HTTP interface. The possibility of horizontal scaling matches the requirements of a DAQ monitoring system. The data load from all sources is balanced by redistribution over an Elasticsearch cluster that can be hosted on a computer cloud. In order to achieve the necessary robustness and to validate the scalability of the approach the above monitoring solution currently runs in parallel with an existing in-house developed DAQ monitoring system

The CMS Timing Control and Distribution System

The Compact Muon Solenoid (CMS) experiment operating at the CERN (European Laboratory for Nuclear Physics) Large Hadron Collider (LHC) is in the process of upgrading several of its detector systems. Adding more individual detector components brings the need to test and commission those components separately from existing ones so as not to compromise physics data-taking. The CMS Trigger, Timing and Control (TTC) system had reached its limits in terms of the number of separate elements (partitions) that could be supported. A new Timing and Control Distribution System (TCDS) has been designed, built and commissioned in order to overcome this limit. It also brings additional functionality to facilitate parallel commissioning of new detector elements. We describe the new TCDS system and its components and show results from the first operational experience with the TCDS system in CMS

Opportunistic usage of the CMS online cluster using a cloud overlay

After two years of maintenance and upgrade, the Large Hadron Collider (LHC), the largest and most powerful particle accelerator in the world, has started its second three year run. Around 1500 computers make up the CMS (Compact Muon Solenoid) Online cluster. This cluster is used for Data Acquisition of the CMS experiment at CERN, selecting and sending to storage around 20 TBytes of data per day that are then analysed by the Worldwide LHC Computing Grid (WLCG) infrastructure that links hundreds of data centres worldwide. 3000 CMS physicists can access and process data, and are always seeking more computing power and data. The backbone of the CMS Online cluster is composed of 16000 cores which provide as much computing power as all CMS WLCG Tier1 sites (352K HEP-SPEC-06 score in the CMS cluster versus 300K across CMS Tier1 sites). The computing power available in the CMS cluster can significantly speed up the processing of data, so an effort has been made to allocate the resources of the CMS Online cluster to the grid when it isn't used to its full capacity for data acquisition. This occurs during the maintenance periods when the LHC is non-operational, which corresponded to 117 days in 2015. During 2016, the aim is to increase the availability of the CMS Online cluster for data processing by making the cluster accessible during the time between two physics collisions while the LHC and beams are being prepared. This is usually the case for a few hours every day, which would vastly increase the computing power available for data processing. Work has already been undertaken to provide this functionality, as an OpenStack cloud layer has been deployed as a minimal overlay that leaves the primary role of the cluster untouched. This overlay also abstracts the different hardware and networks that the cluster is composed of. The operation of the cloud (starting and stopping the virtual machines) is another challenge that has been overcome as the cluster has only a few hours spare during the aforementioned beam preparation. By improving the virtual image deployment and integrating the OpenStack services with the core services of the Data Acquisition on the CMS Online cluster it is now possible to start a thousand virtual machines within 10 minutes and to turn them off within seconds. This document will explain the architectural choices that were made to reach a fully redundant and scalable cloud, with a minimal impact on the running cluster configuration while giving a maximal segregation between the services. It will also present how to cold start 1000 virtual machines 25 times faster, using tools commonly utilised in all data centres

Opportunistic usage of the CMS online cluster using a cloud overlay

After two years of maintenance and upgrade, the Large Hadron Collider (LHC), the largest and most powerful particle accelerator in the world, has started its second three year run. Around 1500 computers make up the CMS (Compact Muon Solenoid) Online cluster. This cluster is used for Data Acquisition of the CMS experiment at CERN, selecting and sending to storage around 20 TBytes of data per day that are then analysed by the Worldwide LHC Computing Grid (WLCG) infrastructure that links hundreds of data centres worldwide. 3000 CMS physicists can access and process data, and are always seeking more computing power and data. The backbone of the CMS Online cluster is composed of 16000 cores which provide as much computing power as all CMS WLCG Tier1 sites (352K HEP-SPEC-06 score in the CMS cluster versus 300K across CMS Tier1 sites). The computing power available in the CMS cluster can significantly speed up the processing of data, so an effort has been made to allocate the resources of the CMS Online cluster to the grid when it isn’t used to its full capacity for data acquisition. This occurs during the maintenance periods when the LHC is non-operational, which corresponded to 117 days in 2015. During 2016, the aim is to increase the availability of the CMS Online cluster for data processing by making the cluster accessible during the time between two physics collisions while the LHC and beams are being prepared. This is usually the case for a few hours every day, which would vastly increase the computing power available for data processing. Work has already been undertaken to provide this functionality, as an OpenStack cloud layer has been deployed as a minimal overlay that leaves the primary role of the cluster untouched. This overlay also abstracts the different hardware and networks that the cluster is composed of. The operation of the cloud (starting and stopping the virtual machines) is another challenge that has been overcome as the cluster has only a few hours spare during the aforementioned beam preparation. By improving the virtual image deployment and integrating the OpenStack services with the core services of the Data Acquisition on the CMS Online cluster it is now possible to start a thousand virtual machines within 10 minutes and to turn them off within seconds. This document will explain the architectural choices that were made to reach a fully redundant and scalable cloud, with a minimal impact on the running cluster configuration while giving a maximal segregation between the services. It will also present how to cold start 1000 virtual machines 25 times faster, using tools commonly utilised in all data centres

The Phase-2 Upgrade of the CMS Data Acquisition

The High Luminosity LHC (HL-LHC) will start operating in 2027 after the third Long Shutdown (LS3), and is designed to provide an ultimate instantaneous luminosity of 7:5 × 1034 cm−2 s−1, at the price of extreme pileup of up to 200 interactions per crossing. The number of overlapping interactions in HL-LHC collisions, their density, and the resulting intense radiation environment, warrant an almost complete upgrade of the CMS detector. The upgraded CMS detector will be read out by approximately fifty thousand highspeed front-end optical links at an unprecedented data rate of up to 80 Tb/s, for an average expected total event size of approximately 8 − 10 MB. Following the present established design, the CMS trigger and data acquisition system will continue to feature two trigger levels, with only one synchronous hardware-based Level-1 Trigger (L1), consisting of custom electronic boards and operating on dedicated data streams, and a second level, the High Level Trigger (HLT), using software algorithms running asynchronously on standard processors and making use of the full detector data to select events for offline storage and analysis. The upgraded CMS data acquisition system will collect data fragments for Level-1 accepted events from the detector back-end modules at a rate up to 750 kHz, aggregate fragments corresponding to individual Level- 1 accepts into events, and distribute them to the HLT processors where they will be filtered further. Events accepted by the HLT will be stored permanently at a rate of up to 7.5 kHz. This paper describes the baseline design of the DAQ and HLT systems for the Phase-2 of CMS

DAQExpert - An expert system to increase CMS data-taking efficiency

The efficiency of the Data Acquisition (DAQ) of the Compact Muon Solenoid (CMS) experiment for LHC Run 2 is constantly being improved. A~significant factor affecting the data taking efficiency is the experience of the DAQ operator. One of the main responsibilities of the DAQ operator is to carry out the proper recovery procedure in case of failure of data-taking. At the start of Run 2, understanding the problem and finding the right remedy could take a considerable amount of time (up to many minutes). Operators heavily relied on the support of on-call experts, also outside working hours. Wrong decisions due to time pressure sometimes lead to an additional overhead in recovery time. To increase the efficiency of CMS data-taking we developed a new expert system, the DAQExpert, which provides shifters with optimal recovery suggestions instantly when a failure occurs. DAQExpert is a~web application analyzing frequently updating monitoring data from all DAQ components and identifying problems based on expert knowledge expressed in small, independent logic-modules written in Java. Its results are presented in real-time in the control room via a web-based GUI and a sound-system in a form of short description of the current failure, and steps to recover

Performance of the CMS Event Builder

The data acquisition system (DAQ) of the CMS experiment at the CERN Large Hadron Collider (LHC) assembles events at a rate of 100 kHz. It transports event data at an aggregate throughput of ~100 GB/s to the high-level trigger (HLT) farm. The CMS DAQ system has been completely rebuilt during the first long shutdown of the LHC in 2013/14. The new DAQ architecture is based on state-of-the-art network technologies for the event building. For the data concentration, 10/40 Gb/s Ethernet technologies are used together with a reduced TCP/IP protocol implemented in FPGA for a reliable transport between custom electronics and commercial computing hardware. A 56 Gb/s Infiniband FDR CLOS network has been chosen for the event builder. We report on the performance of the event builder system and the steps taken to exploit the full potential of the network technologies.The data acquisition system (DAQ) of the CMS experiment at the CERN Large Hadron Collider assembles events at a rate of 100 kHz, transporting event data at an aggregate throughput of to the high-level trigger farm. The DAQ architecture is based on state-of-the-art network technologies for the event building. For the data concentration, 10/40 Gbit/s Ethernet technologies are used together with a reduced TCP/IP protocol implemented in FPGA for a reliable transport between custom electronics and commercial computing hardware. A 56 Gbit/s Infiniband FDR Clos network has been chosen for the event builder. This paper presents the implementation and performance of the event-building system

Operational experience with the new CMS DAQ-Expert

The data acquisition (DAQ) system of the Compact Muon Solenoid (CMS) at CERN reads out the detector at the level-1 trigger accept rate of 100 kHz, assembles events with a bandwidth of 200 GB/s, provides these events to the high level-trigger running on a farm of about 30k cores and records the accepted events. Comprising custom-built and cutting edge commercial hardware and several 1000 instances of software applications, the DAQ system is complex in itself and failures cannot be completely excluded. Moreover, problems in the readout of the detectors,in the first level trigger system or in the high level trigger may provoke anomalous behaviour of the DAQ systemwhich sometimes cannot easily be differentiated from a problem in the DAQ system itself. In order to achieve high data taking efficiency with operators from the entire collaboration and without relying too heavily on the on-call experts, an expert system, the DAQ-Expert, has been developed that can pinpoint the source of most failures and give advice to the shift crew on how to recover in the quickest way. The DAQ-Expert constantly analyzes monitoring data from the DAQ system and the high level trigger by making use of logic modules written in Java that encapsulate the expert knowledge about potential operational problems. The results of the reasoning are presented to the operator in a web-based dashboard, may trigger sound alerts in the control room and are archived for post-mortem analysis - presented in a web-based timeline browser. We present the design of the DAQ-Expert and report on the operational experience since 2017, when it was first put into production