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Autonomic Computing (AC) is defined as “computing systems that manage themselves in accordance with high-level objectives from humans”. AC is now a well-established scientific domain, and a priority for industry. Automated detection, diagnosis, and ultimately management, of software/hardware problems define autonomic dependability. The paper reports on applying state of the art autonomic dependability methods to the Logging and Bookeeping data, with promising results on detection.
Motivated by the behavioral modeling of a grid system based on the Logging and Bookkeeping (L&B) files that are job traces recorded by the EGEE1 grid broker, we face the facts that 1)the jobs are weakly supervised. i.e., two classes (“done” and “failed”) are known according to the job executing situation. 2)the initial description of jobs is highly redundant. For each job, there are three tables containing circa 100 attributes among which strong dependence exits. 3)job traces are represented using a structured representation (Job Description Language) and there is no natural metric on this representation space 4)the complex distribution of job data set is heterogeneous on users who launched the jobs and on the instant grid load measured on weeks. To solve the difficulties, a two-step approach is proposed, Constructive Induction and...
Grid systems are complex heterogeneous systems, and their modeling constitutes a highly challenging goal. This paper is interested in modeling the jobs handled by the EGEE grid, by mining the Logging and Bookkeeping files. The goal is to discover meaningful job clusters, going beyond the coarse categories of "successfully terminated jobs" and "other jobs". The presented approach is a three-step process: i) Data slicing is used to alleviate the job heterogeneity and afford discriminant learning; ii) Constructive induction proceeds by learning discriminant hypotheses from each data slice; iii) Finally, double clustering is used on the representation built by constructive induction; the clusters are fully validated after the stability criteria proposed by Meila (2006). Lastly, the job clusters are submitted to the experts and so...
The ever increasing scale and complexity of large computational systems ask for sophisticated management tools, paving the way toward Autonomic Computing. A first step toward Autonomic Grids is presented in this paper; the interactions between the grid middleware and the stream of computational queries are modeled using statistical learning. The approach is implemented and validated in the context of the EGEE grid. The G-StrAP system, embedding the StrAP Data Streaming algorithm, provides manageable and understandable views of the computational workload based on gLite reporting services. An online monitoring module shows the instant distribution of the jobs in real-time and its dynamics, enabling anomaly detection. An offline monitoring module provides the administrator with a consolidated view of the workload, enabling the visual insp...
The rise of grid systems, made of a large number of heterogeneous resources, motivated the highly challenging field of Autonomic Computing, aimed at the self-management of such complex systems. A preliminary step, this paper is interested in modeling the jobs submitted to the grid and discovering meaningful job categories, beyond the coarse distinction between "successfully executed" and "failed" jobs. The difficulty lies in the huge size of the available observations (the Logs of the grid) and their heterogeneity, severely hindering Machine Learning algorithms at the state of the art. This difficulty is addressed through an original 3-step process: i) the data are firstly sliced into (more) homogeneous subsets, where a data slice involves jobs submitted by a single user, or during a single period of time; ii) supervised ML algorithms ...
The Affinity Propagation (AP) clustering algorithm proposed by Frey and Dueck (2007) provides an understandable, nearly optimal summary of a dataset, albeit with quadratic computational complexity. This paper, motivated by Autonomic Computing, extends AP to the data streaming framework. Firstly a hierarchical strategy is used to reduce the complexity to O(N^{1+"}); the distortion loss incurred is analyzed in relation with the dimension of the data items. Secondly, a coupling with a change detection test is used to cope with non-stationary data distribution, and rebuild the model as needed. The presented approach Strap is applied to the stream of jobs submitted to the EGEE Grid, providing an understandable description of the job flow and enabling the system administrator to spot online some sources of failures.
The Affinity Propagation (AP) clustering algorithm proposed by Frey and Dueck (2007) provides an understandable, nearly optimal summary of a dataset, albeit with quadratic computational complexity. This paper, motivated by Autonomic Computing, extends AP to the data streaming framework. Firstly a hierarchical strategy is used to reduce the complexity to O(N^{1+"}); the distortion loss incurred is analyzed in relation with the dimension of the data items. Secondly, a coupling with a change detection test is used to cope with non-stationary data distribution, and rebuild the model as needed. The presented approach Strap is applied to the stream of jobs submitted to the EGEE Grid, providing an understandable description of the job flow and enabling the system administrator to spot online some sources of failures.
La convergence entre informatique autonomique et grilles est nécessaire pour passer à l’échelle dans la prise en compte de la complexité des ressources ainsi que des traitements de calcul et de données. Un premier pas vers des grilles autonomiques consiste à modéliser les interactions complexes entre l’intergiciel de grille et les requêtes des utilisateurs, en exploitant les acquis des techniques de fouille de données et d’apprentissage. Cet article décrit G-STRAP, un environnement de supervision qui fournit en temps réel une description concise de la distribution dynamique du flot de tâches actives sur la grille. Nous proposons une analyse théorique de l’algorithme de clustering au coeur de G-STRAP, et une étude expérimentale sur des données réelles de la grille EGEE.
Grids have emerged as a promising technology to handle the data and compute intensive requirements of many application areas. Digital medical image processing is a promising application area for grids. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. The research project AGIR (Grid Analysis of Radiological Data) presented in this paper addresses this challenge through a combined approach: on one hand, leveraging the grid middleware through core grid medical services which target the requirements of medical data processing applications; on the other hand, grid-enabling a panel of applications ranging from algorithmic resea...
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