Catalin Cirstoiu

University of Bucharest, Bucureşti, Bucureşti, Romania

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Publications (15)3.93 Total impact

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    ABSTRACT: Grid computing has gained an increasing importance in the last years, especially in the academic environments, offering the possibility to rapidly solve complex scientific problems. The monitoring of the Grid jobs has a vital importance for analyzing the system's performance, for providing the users an appropriate feed-back, and for obtaining historical data which may be used for performance prediction. Several monitoring systems have been developed, with different strategies to collect and store the information. We shall present here a solution based on MonALISA, a distributed service for monitoring, control and global optimization of complex systems, and LISA, a component application of MonALISA which can help in optimizing other applications by means of monitoring services. The advantages of this system are, among others, flexibility, dynamic configuration, high communication performance.
    06/2011;
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    ABSTRACT: This paper discusses the latest generation of the MONARC (MOdels of Networked Analysis at Regional Centers) simulation framework, as a design and modeling tool for large scale distributed systems applied to HEP experiments. The simulation of Grid architectures has a vital importance in the future deployment of Grid systems for providing the users an appropriate feed-back. We present here an example of simulating complex data processing systems and the way the framework is used to optimize the overall Grid architecture and/or the policies that govern the Grid's use.
    06/2011;
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    ABSTRACT: The MonALISA (Monitoring Agents in a Large Integrated Services Architecture) framework provides a set of distributed services for monitoring, control, management and global optimization for large scale distributed systems. It is based on an ensemble of autonomous, multi-threaded, agent-based subsystems which are registered as dynamic services. They can be automatically discovered and used by other services or clients. The distributed agents can collaborate and cooperate in performing a wide range of management, control and global optimization tasks using real time monitoring information.Program summaryProgram title: MonALISACatalogue identifier: AEEZ_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEEZ_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Caltech License – free for all non-commercial activitiesNo. of lines in distributed program, including test data, etc.: 147 802No. of bytes in distributed program, including test data, etc.: 2 5913 689Distribution format: tar.gzProgramming language: Java, additional APIs available in Java, C, C++, Perl and pythonComputer: Computing Clusters, Network Devices, Storage Systems, Large scale data intensive applicationsOperating system: The MonALISA service is mainly used in Linux, the MonALISA client runs on all major platforms (Windows, Linux, Solaris, MacOS).Has the code been vectorized or parallelized?: It is a multithreaded application. It will efficiently use all the available processors.RAM: for the MonALISA service the minimum required memory is 64 MB; if the JVM is started allocating more memory this will be used for internal caching. The MonALISA client requires typically 256–512 MB of memory.Classification: 6.5External routines: Requires Java: JRE or JDK to run. These external packages are used (they are included in the distribution): JINI, JFreeChart, PostgreSQL (optional).Nature of problem: To monitor and control distributed computing clusters and grids, the network infrastructure, the storage systems, and the applications used on such facilities. The monitoring information gathered is used for developing the required higher level services, the components that provide decision support and some degree of automated decisions and for maintaining and optimizing workflow in large scale distributed systems.Solution method: The MonALISA framework is designed as an ensemble of autonomous self-describing agent-based subsystems which are registered as dynamic services. These services are able to collaborate and cooperate in performing a wide range of distributed information-gathering and processing tasks.Running time: MonALISA services are designed to run continuously to collect monitoring data and to trigger alarms or to take automatic actions in case it is necessary.References:[1]http://monalisa.caltech.edu.
    Computer Physics Communications 12/2009; · 2.41 Impact Factor
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    ABSTRACT: MonALISA developers describe how it works, the key design principles behind it, and the biggest technical challenges in building it.
    Queue 09/2009; 7(6):40.
  • ACM Queue. 01/2009; 7:40.
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    ABSTRACT: ALICE (A Large Ion Collider Experiment) is a general-purpose, heavy-ion detector at the CERN LHC which focuses on QCD, the strong-interaction sector of the Standard Model. It is designed to address the physics of strongly interacting matter and the quark-gluon plasma at extreme values of energy density and temperature in nucleus-nucleus collisions. Besides running with Pb ions, the physics programme includes collisions with lighter ions, lower energy running and dedicated proton-nucleus runs. ALICE will also take data with proton beams at the top LHC energy to collect reference data for the heavy-ion programme and to address several QCD topics for which ALICE is complementary to the other LHC detectors. The ALICE detector has been built by a collaboration including currently over 1000 physicists and engineers from 105 Institutes in 30 countries. Its overall dimensions are 16 × 16 × 26 m3 with a total weight of approximately 10 000 t. The experiment consists of 18 different detector systems each with its own specific technology choice and design constraints, driven both by the physics requirements and the experimental conditions expected at LHC. The most stringent design constraint is to cope with the extreme particle multiplicity anticipated in central Pb-Pb collisions. The different subsystems were optimized to provide high-momentum resolution as well as excellent Particle Identification (PID) over a broad range in momentum, up to the highest multiplicities predicted for LHC. This will allow for comprehensive studies of hadrons, electrons, muons, and photons produced in the collision of heavy nuclei. Most detector systems are scheduled to be installed and ready for data taking by mid-2008 when the LHC is scheduled to start operation, with the exception of parts of the Photon Spectrometer (PHOS), Transition Radiation Detector (TRD) and Electro Magnetic Calorimeter (EMCal). These detectors will be completed for the high-luminosity ion run expected in 2010. This paper describes in detail the detector components as installed for the first data taking in the summer of 2008.
    Journal of Instrumentation 08/2008; 3(08):S08002. · 1.53 Impact Factor
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    ABSTRACT: ALICE (A Large Ion Collider Experiment) is a general-purpose, heavy-ion detector at the CERN LHC which focuses on QCD, the strong-interaction sector of the Standard Model. It is designed to address the physics of strongly interacting matter and the quark-gluon plasma at extreme values of energy density and temperature in nucleus-nucleus collisions. Besides running with Pb ions, the physics programme includes collisions with lighter ions, lower energy running and dedicated proton-nucleus runs. ALICE will also take data with proton beams at the top LHC energy to collect reference data for the heavy-ion programme and to address several QCD topics for which ALICE is complementary to the other LHC detectors. The ALICE detector has been built by a collaboration including currently over 1000 physicists and engineers from 105 Institutes in 30 countries. Its overall dimensions are 16 × 16 × 26 m3 with a total weight of approximately 10 000 t. The experiment consists of 18 different detector systems each with its own specific technology choice and design constraints, driven both by the physics requirements and the experimental conditions expected at LHC. The most stringent design constraint is to cope with the extreme particle multiplicity anticipated in central Pb-Pb collisions. The different subsystems were optimized to provide high-momentum resolution as well as excellent Particle Identification (PID) over a broad range in momentum, up to the highest multiplicities predicted for LHC. This will allow for comprehensive studies of hadrons, electrons, muons, and photons produced in the collision of heavy nuclei. Most detector systems are scheduled to be installed and ready for data taking by mid-2008 when the LHC is scheduled to start operation, with the exception of parts of the Photon Spectrometer (PHOS), Transition Radiation Detector (TRD) and Electro Magnetic Calorimeter (EMCal). These detectors will be completed for the high-luminosity ion run expected in 2010. This paper describes in detail the detector components as installed for the first data taking in the summer of 2008.
    Journal of Instrumentation. 07/2008; 3:8002.
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    ABSTRACT: In this paper we present the Experiment Dashboard monitoring system, which is currently in use by four Large Hadron Collider (LHC)[1] experiments. The goal of the Experiment Dashboard is to monitor the activities of the LHC experiments on the distributed infrastructure, providing monitoring data from the virtual organization (VO) and user perspectives. The LHC experiments are using various Grid infrastructures (LCG[2]/EGEE[3], OSG[4], NDGF[5]) with correspondingly various middleware flavors and job submission methods. Providing a uniform and complete view of various activities like job processing, data movement and publishing, access to distributed databases regardless of the underlying Grid flavor is the challenging task. In this paper we will describe the Experiment Dashboard concept, its framework and main monitoring applications.
    Journal of Physics Conference Series 07/2008; 119(6).
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    ABSTRACT: Starting from mid-2008, the ALICE detector at CERN LHC will collect data at a rate of 4PB per year. ALICE will use exclusively distributed Grid resources to store, process and analyse this data. The top-level management of the Grid resources is done through the AliEn (ALICE Environment) system, which is in continuous development since year 2000. AliEn presents several original solutions, which have shown their viability in a number of large exercises of increasing complexity called Data Challenges. This paper describes the AliEn architecture: Job Management, Data Management and UI. The current status of AliEn will be illustrated, as well as the performance of the system during the data challenges. The paper also describes the future AliEn development roadmap.
    Journal of Physics Conference Series 07/2008; 119(6):2012-.
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    ABSTRACT: Thanks to the Grid, users have access to computing resources distributed all over the world. The Grid hides the complexity and the differences of its heterogeneous components. In such a distributed system, it is clearly very important that errors are detected as soon as possible, and that the procedure to solve them is well established. We focused on two of its main elements: the workload and the data management systems. We developed an application to investigate the efficiency of the different centres. Furthermore, our system can be used to categorize the most common error messages, and control their time evolution.
    Journal of Physics Conference Series 07/2008;
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    ABSTRACT: The MonALISA (Monitoring Agents in A Large Integrated Services Architecture) framework provides a set of distributed services for monitoring, control, management and global optimization for large scale distributed systems. It is based on an ensemble of autonomous, multi-threaded, agent-based subsystems which are registered as dynamic services. They can be automatically discovered and used by other services or clients. The distributed agents can collaborate and cooperate in performing a wide range of management, control and global optimization tasks using real time monitoring information.
    01/2008;
  • Catalin Cirstoiu, Ramiro Voicu, Nicolae Tapus
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    ABSTRACT: Handling high-performance data transfers is a hot topic today, in the context of the numerous data-intensive applications which begin to be used not only in local clusters, but also in large distributed systems. For this purpose, we have developed a framework based on MONALISA that allows intelligent handling of data transfers in large environments, both over current and next-generation, hybrid high-speed networks. This paper presents its design and two policies for handling the data transfers contention to network resources. We argue for its effectiveness by presenting our experiences with the framework, using the FDT data transfer application, in the context of the ALICE Grid and the US LHCNet network.
    7th International Symposium on Parallel and Distributed Computing (ISPDC 2008), 1-5 July 2008, Krakow, Poland; 01/2008
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    ABSTRACT: The successful administration of a global Data Grid system requires collecting and storing relevant monitoring information, using it to show the status and the trends of the entire system. The collected information is further used in developing the higher-level services and components of the distributed system to provide a degree of automated operational decisions, in order to maintain and optimize the work-flow through the entire system. In this paper we present the architecture of the monitoring system, developed within the MonALISA framework, for the ALICE Grid. The system uses flexible mechanisms for collecting, aggregating, storing and presenting monitoring information both in near real-time or history charts, in global or specific views, being able to generate alerts or take automated decisions based on it.
    06/2007;
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    ABSTRACT: The MonALISA (Monitoring Agents in A Large Integrated Services Architecture) system provides a distributed service architecture which is used to collect and process monitoring information. While its initial target field of application is networks and Grid systems supporting data processing and analysis for global high energy and nuclear physics collaborations, MonALISA is broadly applicable to many fields of "data intensive" science, and to the monitoring and management of major research and education networks. MonALISA is based on a scalable Dynamic Distributed Services Architecture), and is implemented in Java using JINI and WSDL technologies. The scalability of the system derives from the use of a multi threaded engine to host a variety of loosely coupled self-describing dynamic services, the ability of each service to register itself and then to be discovered and used by any other services, or clients that require such information. The framework integrates many existing monitoring tools and procedures to collect parameters describing computational nodes, applications and network performance. Specialized mobile agents are used in the MonALISA framework to perform global optimization tasks or help and improve the operation of large distributed system by performing supervising tasks for different applications or real time parameters. MonALISA is currently running around the clock monitoring several Grids and distributed applications on around 160 sites.
    01/2004;
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    ABSTRACT: The MonALISA (Monitoring Agents in A Large Integrated Services Architecture) system provides a distributed monitoring service. MonALISA is based on a scalable Dynamic Distributed Services Architecture which is designed to meet the needs of physics collaborations for monitoring global Grid systems, and is implemented using JINI/JAVA and WSDL/SOAP technologies. The scalability of the system derives from the use of multithreaded Station Servers to host a variety of loosely coupled self-describing dynamic services, the ability of each service to register itself and then to be discovered and used by any other services, or clients that require such information, and the ability of all services and clients subscribing to a set of events (state changes) in the system to be notified automatically. The framework integrates several existing monitoring tools and procedures to collect parameters describing computational nodes, applications and network performance. It has built-in SNMP support and network-performance monitoring algorithms that enable it to monitor end-to-end network performance as well as the performance and state of site facilities in a Grid. MonALISA is currently running around the clock on the US CMS test Grid as well as an increasing number of other sites. It is also being used to monitor the performance and optimize the interconnections among the reflectors in the VRVS system.
    CoRR. 01/2003; cs.DC/0306096.