ICALEPCS2013 - List of Authors (Suwalska, A.)

Paper

Title

Page

High-Availability Monitoring and Big Data: Using Java Clustering and Caching Technologies to Meet Complex Monitoring Scenarios

439

M. Bräger, M. Brightwell, E. Koufakis, R. Martini, A. Suwalska
CERN, Geneva, Switzerland

Monitoring and control applications face ever more demanding requirements: as both data sets and data rates continue to increase, non-functional requirements such as performance, availability and maintainability become more important. C2MON (CERN Control and Monitoring Platform) is a monitoring platform developed at CERN over the past few years. Making use of modern Java caching and clustering technologies, the platform supports multiple deployment architectures, from a simple 3-tier system to highly complex clustered solutions. In this paper we consider various monitoring scenarios and how the C2MON deployment strategy can be adapted to meet them.

Poster MOPPC140 [1.382 MB]

TUPPC029

Integration, Processing, Analysis Methodologies and Tools for Ensuring High Data Quality and Rapid Data Access in the TIM* Monitoring System

615

A. Suwalska, M. Brightwell, M. Bräger, E. Koufakis, R. Martini, P. Sollander
CERN, Geneva, Switzerland

Processing, storing and analysing large amounts of real-time data is a challenge for every monitoring system. The performance of the system strongly depends on high quality configuration data and the ability of the system to cope with data anomalies. The Technical Infrastructure Monitoring system (TIM) addresses data quality issues by enforcing a workflow of strict procedures to integrate or modify data tag configurations. TIM’s data acquisition layer architecture allows real-time analysis and rejection of irrelevant data. The discarded raw data 90,000,000 transactions/day) are stored in a database, then purged after gathering statistics. The remaining operational data (2,000,000 transactions/day) are transferred to a server running an in-memory database, ensuring its rapid processing. These data are currently stored for 30 days allowing ad hoc historical data analysis. In this paper we describe the methods and tools used to guarantee the quality of configuration data and highlight the advanced architecture that ensures optimal access to operational data as well as the tools used to perform off-line data analysis.
* Technical Infrastructure Monitoring system

Poster TUPPC029 [0.742 MB]

Paper	Title	Page
MOPPC140	High-Availability Monitoring and Big Data: Using Java Clustering and Caching Technologies to Meet Complex Monitoring Scenarios	439
	M. Bräger, M. Brightwell, E. Koufakis, R. Martini, A. Suwalska CERN, Geneva, Switzerland
	Monitoring and control applications face ever more demanding requirements: as both data sets and data rates continue to increase, non-functional requirements such as performance, availability and maintainability become more important. C2MON (CERN Control and Monitoring Platform) is a monitoring platform developed at CERN over the past few years. Making use of modern Java caching and clustering technologies, the platform supports multiple deployment architectures, from a simple 3-tier system to highly complex clustered solutions. In this paper we consider various monitoring scenarios and how the C2MON deployment strategy can be adapted to meet them.
	Poster MOPPC140 [1.382 MB]

TUPPC029	Integration, Processing, Analysis Methodologies and Tools for Ensuring High Data Quality and Rapid Data Access in the TIM* Monitoring System	615
	A. Suwalska, M. Brightwell, M. Bräger, E. Koufakis, R. Martini, P. Sollander CERN, Geneva, Switzerland
	Processing, storing and analysing large amounts of real-time data is a challenge for every monitoring system. The performance of the system strongly depends on high quality configuration data and the ability of the system to cope with data anomalies. The Technical Infrastructure Monitoring system (TIM) addresses data quality issues by enforcing a workflow of strict procedures to integrate or modify data tag configurations. TIM’s data acquisition layer architecture allows real-time analysis and rejection of irrelevant data. The discarded raw data 90,000,000 transactions/day) are stored in a database, then purged after gathering statistics. The remaining operational data (2,000,000 transactions/day) are transferred to a server running an in-memory database, ensuring its rapid processing. These data are currently stored for 30 days allowing ad hoc historical data analysis. In this paper we describe the methods and tools used to guarantee the quality of configuration data and highlight the advanced architecture that ensures optimal access to operational data as well as the tools used to perform off-line data analysis. * Technical Infrastructure Monitoring system
	Poster TUPPC029 [0.742 MB]