ICALEPCS2017 - List of Authors (Smareglia, R.)

Paper	Title	Page
TUPHA030	Using AI in the Fault Management Predictive Model of the SKA TM Services: A Preliminary Study	435
	M. Canzari, M. Di Carlo, M. Dolci INAF - OA Teramo, Teramo, Italy R. Smareglia INAF-OAT, Trieste, Italy
	SKA (Square Kilometer Array) is a project aimed to build a very large radio-telescope, composed by thousands of antennae and related support systems. The overall orchestration is performed by the Telescope Manager (TM), a suite of software applications. In order to ensure the proper and uninterrupted operation of TM, a local monitoring and control system is developed, called TM Services. Fault Management (FM) is one of these services, and is composed by processes and infrastructure associated with detecting, diagnosing and fixing faults, and finally returning to normal operations. The aim of the study, introducing artificial intelligence algorithms during the detection phase, is to build a predictive model, based on the history and statistics of the system, in order to perform trend analysis and failure prediction. Based on monitoring data and health status detected by the software system monitor and on log files gathered by the ELK (Elasticsearch, Logstash, and Kibana) server, the predictive model ensures that the system is operating within its normal operating parameters and takes corrective actions in case of failure.
	Poster TUPHA030 [2.851 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2017-TUPHA030
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPHA207	Tm Services: An Architecture for Monitoring and Controlling the Square Kilometre Array (SKA) Telescope Manager (Tm)	943
	M. Di Carlo, M. Canzari, M. Dolci INAF - OA Teramo, Teramo, Italy D. Barbosa, J.P. Barraca, J.B. Morgado GRIT, Aveiro, Portugal R. Smareglia INAF-OAT, Trieste, Italy
	The SKA project is an international effort (10 member and 10 associated countries with the involvement of 100 companies and research institutions) to build the world's largest radio telescope. The SKA Telescope Manager (TM) is the core package of the SKA Telescope aimed at scheduling observations, controlling their execution, monitoring the telescope and so on. To do that, TM directly interfaces with the Local Monitoring and Control systems (LMCs) of the other SKA Elements (e.g. Dishes), exchanging commands and data with them by using the TANGO controls framework. TM in turn needs to be monitored and controlled, in order its continuous and proper operation is ensured. This higher responsibility together with others like collecting and displaying logging data to operators, performing lifecycle management of TM applications, directly deal - when possible - with management of TM faults (which also includes a direct handling of TM status and performance data) and interfacing with the virtualization platform compose the TM Services (SER) package that is discussed and presented in the present paper.
	Poster TUPHA207 [6.137 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2017-TUPHA207
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Using AI in the Fault Management Predictive Model of the SKA TM Services: A Preliminary Study

435

M. Canzari, M. Di Carlo, M. Dolci
INAF - OA Teramo, Teramo, Italy
R. Smareglia
INAF-OAT, Trieste, Italy

SKA (Square Kilometer Array) is a project aimed to build a very large radio-telescope, composed by thousands of antennae and related support systems. The overall orchestration is performed by the Telescope Manager (TM), a suite of software applications. In order to ensure the proper and uninterrupted operation of TM, a local monitoring and control system is developed, called TM Services. Fault Management (FM) is one of these services, and is composed by processes and infrastructure associated with detecting, diagnosing and fixing faults, and finally returning to normal operations. The aim of the study, introducing artificial intelligence algorithms during the detection phase, is to build a predictive model, based on the history and statistics of the system, in order to perform trend analysis and failure prediction. Based on monitoring data and health status detected by the software system monitor and on log files gathered by the ELK (Elasticsearch, Logstash, and Kibana) server, the predictive model ensures that the system is operating within its normal operating parameters and takes corrective actions in case of failure.

Poster TUPHA030 [2.851 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2017-TUPHA030

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPHA207

Tm Services: An Architecture for Monitoring and Controlling the Square Kilometre Array (SKA) Telescope Manager (Tm)

943

M. Di Carlo, M. Canzari, M. Dolci
INAF - OA Teramo, Teramo, Italy
D. Barbosa, J.P. Barraca, J.B. Morgado
GRIT, Aveiro, Portugal
R. Smareglia
INAF-OAT, Trieste, Italy

The SKA project is an international effort (10 member and 10 associated countries with the involvement of 100 companies and research institutions) to build the world's largest radio telescope. The SKA Telescope Manager (TM) is the core package of the SKA Telescope aimed at scheduling observations, controlling their execution, monitoring the telescope and so on. To do that, TM directly interfaces with the Local Monitoring and Control systems (LMCs) of the other SKA Elements (e.g. Dishes), exchanging commands and data with them by using the TANGO controls framework. TM in turn needs to be monitored and controlled, in order its continuous and proper operation is ensured. This higher responsibility together with others like collecting and displaying logging data to operators, performing lifecycle management of TM applications, directly deal - when possible - with management of TM faults (which also includes a direct handling of TM status and performance data) and interfacing with the virtualization platform compose the TM Services (SER) package that is discussed and presented in the present paper.

Poster TUPHA207 [6.137 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2017-TUPHA207

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)