AMS MONITORING INTERFACE P. Zuccon, G. Alberti INFN Sezione di Perugia Computing in High Energy Physics October 2010 – Taipei, Taiwan

CHEP – 18, 22 October 2010 Taipei, Taiwan THE ALPHA MAGNETIC SPECTROMETER HEP Particle detector in Space (installed on ISS) Magnetic spectrometer with: Silicon Tracker, TRD, TOF, RICH, ECAL Purpose: High statistic and high precision measurement of the cosmic rays fluxes up to 2TV Search for Dark Matter signatures Search for primordial Anti Matter Present Status: located at NASA KSC preparing for Space Shuttle launch on February 27, 2011 P. Zuccon, G. Alberti -- INFN Perugia, Italy 2

TRD TOF Tracker TOF RICH ECAL TRD Identify e+, e- Silicon Tracker Z, P ECAL E of e+, e-, γ RICH Z, E TOF Z, E Particles and nuclei are defined by their charge ( Z ) and energy ( E ~ P ) Z, P are measured independently by the Tracker, RICH, TOF and ECAL AMS: A TeV precision, multipurpose particle physics spectrometer in space. Magnet ±Z 3 P. Zuccon, G. Alberti -- INFN Perugia, Italy

US A FLORIDA A&M UNIV. FLORIDA STATE UNIVERSITY MIT - CAMBRIDGE NASA GODDARD SPACE FLIGHT CENTER NASA JOHNSON SPACE CENTER TEXAS A&M UNIVERSITY UNIV. OF MARYLAND - DEPT OF PHYSICS YALE UNIVERSITY - NEW HAVEN MEXIC O UNA M DENMAR K UNIV. OF AARHUS FINLAN D HELSINKI UNIV. UNIV. OF TURKU FRANC E GAM MONTPELLIER LAPP ANNECY LPSC GRENOBLE GERMAN Y RWTH-I RWTH-III MAX-PLANK INST. UNIV. OF KARLSRUHE ITAL Y ASI CARSO TRIESTE IROE FLORENCE INFN & UNIV. OF BOLOGNA INFN & UNIV. OF MILANO INFN & UNIV. OF PERUGIA INFN & UNIV. OF PISA INFN & UNIV. OF ROMA INFN & UNIV. OF SIENA NETHERLAND S ESA-ESTEC NIKHEF NLR ROMANI A ISS UNIV. OF BUCHAREST RUSSI A I.K.I. ITEP KURCHATOV INST. MOSCOW STATE UNIV. SPAI N CIEMAT - MADRID I.A.C. CANARIAS. SWITZERLAN D ETH-ZURICH UNIV. OF GENEVA CHIN A BISEE (Beijing) IEE (Beijing) IHEP (Beijing) SJTU (Shanghai) SEU (Nanjing) SYSU (Guangzhou) SDU (Jinan) KORE A EWHA KYUNGPOOK NAT.UNIV. PORTUGA L LAB. OF INSTRUM. LISBON ACAD. SINICA (Taiwan) AIDC (Taiwan) CSIST (Taiwan) NCU (Chung Li) NCKU (Tainan) NCTU (Hsinchu) NSPO (Hsinchu) TAIWA N AMS International Collaboration 16 Countries, 60 Institutes and 600 Physicists P. Zuccon, G. Alberti -- INFN Perugia, Italy o AMS collaboration, based at CERN, includes many countries in North America, Europe and Asia. o A relevant contribution comes from Taiwan and in particular includes CHEP2010 hosting institution, the Academia Sinica. 4

CHEP – 18, 22 October 2010 Taipei, Taiwan MONITORING THE AMS DETECTOR Many subsystems produce a lot of slow control data On orbit the commanding bandwidth is limited and the detector can not be queried directly The on-board main computer queries periodically all the sub- systems and send monitoring data to the ground on a dedicated data stream (House Keeping Data) Data are organized in packets (AMSBlock) containing information about the subsystem by which they are originated. Packets are saved on disk as they arrive on ground given the peculiar features of the transmission channel no ordering is guaranteed We developed a system to store and navigate through these data P. Zuccon, G. Alberti -- INFN Perugia, Italy 5

CHEP – 18, 22 October 2010 Taipei, Taiwan H OUSE K EEPING D ATA Raw House Keeping data: Stored on disk as files containing AMSBlocks Each AMSblock is made of: Header: contains the subsystem ID (Node Number), the Command ID (datatype) and the timestamp Payload: the actual answer to the command A single AMSBlock may carry multiple information The typical base info is a n-tuple: (Node, datatype, subtype, time stamp, value) P. Zuccon, G. Alberti -- INFN Perugia, Italy A lot of small size chunks of well defined structure. The best way to store and easily access House Keeping data is a: DataBase A lot of small size chunks of well defined structure. The best way to store and easily access House Keeping data is a: DataBase 6

CHEP – 18, 22 October 2010 Taipei, Taiwan E XAMPLE OF THE DB S TRUCTURE Node type table Node Typestring P. Zuccon, G. Alberti -- INFN Perugia, Italy Data Type table Node TypeReference Data typeInteger DescriptionString Unit of measurement String max_alarmdouble min_alarmdouble max_warndouble min_warndouble Node Number table Node Typereference Node Numberinteger Node Namestring Slow Control data Node Numberreference Data Typereference Timestampinteger valueinteger Correction coefficients Node Numberreference Data Typereference interceptdouble Slopedouble Quadraticdouble 7

CHEP – 18, 22 October 2010 Taipei, Taiwan ACCESS TO HOUSE KEEPING DATA Typical uses of House Keeping data: – Monitoring: multiple (quasi)on-line access to monitor the various subsystems – Analysis: long term studies of various parameters – Correlation: random access to correlate with scientific data P. Zuccon, G. Alberti -- INFN Perugia, Italy Data Base Monitoring Workstation Batch Program Data Export Data Export Data Base Fill Data Base Fill 8

CHEP – 18, 22 October 2010 Taipei, Taiwan ACCESS TO HOUSE KEEPING DATA Typical uses of House Keeping data: – Monitoring: multiple (quasi)on-line access to monitor the various subsystems – Analysis: long term studies of various parameters – Correlation: random access to correlate with scientific data P. Zuccon, G. Alberti -- INFN Perugia, Italy Data Base Web Browser Batch Program Data Export Data Export Web Application Data Base Fill Data Base Fill 9

CHEP – 18, 22 October 2010 Taipei, Taiwan A CCESS AND V ISUALISATION The Browser and hyperlinks seem a very effective way to Navigate through the slow control info – Multiple access – No need to run a specific program – Proven scalability – Large set of examples and features A good solution seem WEB Application An evaluation of the available tools on the (free) Market showed us two tools – RRDTool to produce the plots – web2py as the main framework P. Zuccon, G. Alberti -- INFN Perugia, Italy 10

CHEP – 18, 22 October 2010 Taipei, Taiwan RRD TOOL RRDtool is the OpenSource industry standard, high performance data logging and graphing system for time series data. Can be used to write custom monitoring shell scripts or create whole applications using its Perl, Python, Ruby, TCL or PHP bindings. P. Zuccon, G. Alberti -- INFN Perugia, Italy 11

CHEP – 18, 22 October 2010 Taipei, Taiwan W EB 2 PY Web2py: is a framework designed to write web applications using the python language It is based on the: Model Controller View scheme. Model: This layer provide a description of the DB structure (tables, relations, …) the way the actual underling DB can be chosen, or changed, transparently (MySQL, PostgresSQL, Oracle, …) Controllers : This correspond to a set of functions called when a web page is requested. They extract the data from the database, do some operation and provide the output data to the views. Views: These are the actual web pages. Here the output data from the controllers are formatted and prepared for the browser rendering. P. Zuccon, G. Alberti -- INFN Perugia, Italy 12

CHEP – 18, 22 October 2010 Taipei, Taiwan W HY W EB 2 PY Free and open source full-stack framework, written and programmable in Python. Maintained by the main developer (M. Di Pierro, Associate Professor in Computer Science at DePaul University in Chicago) and by a strong community of professionals It follows the WEB 2.0 paradigm: all the operations can be done from the web interface. Web2py has been designed from scratch to be a consistent framework that performs the task done by other (AJAX, RAILS) putting together various layers from different projects With respect other choices It has a large level of scalability: It can be used with practically all the DB as back-end ( from SQL-lite to Oracle It comes with a robust multithread web server but can be run also under Apache Speaks multiple protocols HTML/XML, RSS/ATOM, RTF, PDF, JSON, AJAX, XML-RPC, CSV, REST, WIKI, Flash/AMF, and Linked Data (RDF). Secure: It prevents the most common types of vulnerabilities including Cross Site Scripting, Injection Flaws, and Malicious File Execution and provides SSL authentication P. Zuccon, G. Alberti -- INFN Perugia, Italy 13

CHEP – 18, 22 October 2010 Taipei, Taiwan E XAMPLE OF A DB DEFINITION IN WEB 2 PY 14 P. Zuccon, G. Alberti -- INFN Perugia, Italy

CHEP – 18, 22 October 2010 Taipei, Taiwan AMS MONITORING INTERFACE (AMI) FEATURES Automatic data display for the quantities defined in the DB Stored data are the raw ADC values and conversion function parameters are kept into the DB Possibility to display the warning and the alarm limit for each quantity Possibility to aggregate information on the same web page ( custom view) Database access via XML-RPC function, C/C++ library for DB access Possibility to export large dataset to csv files from the web interface P. Zuccon, G. Alberti -- INFN Perugia, Italy 15

CHEP – 18, 22 October 2010 Taipei, Taiwan SINGLE QUANTITY DISPLAY P. Zuccon, G. Alberti -- INFN Perugia, Italy 16

CHEP – 18, 22 October 2010 Taipei, Taiwan AMI VIEW EXAMPLE P. Zuccon, G. Alberti -- INFN Perugia, Italy 17

CHEP – 18, 22 October 2010 Taipei, Taiwan IMPLEMENTATION System components: AMI: the main application Scanner: C++ modular code that parse the AMSBlocks and fill the DB via XML-RPC calls to AMI Deployment 1 st phase: Since oct 2009 in production monitoring a single subsystem (Tracker Thermal Control System) with MySQL backend Deployment 2 nd phase: other four systems added in production optimization needed Oracle as back- end Deployment 3 rd phase two parallel instances: one at CERN with world accessible with oracle backend one within the AMS Control Cluster at Kennedy Space Center behind NASA firewalls and with MySQL backend P. Zuccon, G. Alberti -- INFN Perugia, Italy 18

CHEP – 18, 22 October 2010 Taipei, Taiwan OPTIMIZATIONS AND CUSTOMIZATION Data type multiplicity: as the number of datatypes grows the approach of keeping the data on a single table is not performing we implemented a table for each quantity indexed on the timestamp this required a modification of the standard way web2py handle the tables because by defaults it checks for the existence of each table at each connection Large number of users: the images of the plots are cached and regenerated only when they are loosing validity Oracle back-end debugging P. Zuccon, G. Alberti -- INFN Perugia, Italy 19

CHEP – 18, 22 October 2010 Taipei, Taiwan FUTURE PLANS Abandon RRD-Tool in favor of another plot creation package (mathplotlib, …) Implement the plot view editor and allow selected users to create their private views Implement non-plot views for discrete variables like power switches Extend the use within AMS to all the subsystems Extend to display data quality monitoring (trigger rate, event size, reconstructed tracks) (very optional) NO-SQL database in place of the data tables (timestamp, value) P. Zuccon, G. Alberti -- INFN Perugia, Italy 20

CHEP – 18, 22 October 2010 Taipei, Taiwan CONCLUSIONS We implemented a framework to store and display the AMS slow control data The choice of a web application produces many advantages Easy access and navigation trough the data Worldwide access for remote expert monitoring Easy extension with time to other subsystems The choice to use web2py provided: Easy deployment possibility to easy scale on request (apache, multiple instances) Data Base Abstraction Layer: allow to use practically any db as back-end P. Zuccon, G. Alberti -- INFN Perugia, Italy 21

AMS on ISS 22 P. Zuccon, G. Alberti -- INFN Perugia, Italy