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Event monitoring of parallel computations

100%
Gruzlikov A., Kolesov N., Tolmacheva M.
International Journal of Applied Mathematics and Computer Science
|
2015
|
tom 25
|
nr 2
311-321
EN
The paper considers the monitoring of parallel computations for detection of abnormal events. It is assumed that computations are organized according to an event model, and monitoring is based on specific test sequences.
2
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Data-driven models for fault detection using kernel PCA: A water distribution system case study

88%
Nowicki A., Grochowski M., Duzinkiewicz K.
International Journal of Applied Mathematics and Computer Science
|
2012
|
tom 22
|
nr 4
939-949
EN
Kernel Principal Component Analysis (KPCA), an example of machine learning, can be considered a non-linear extension of the PCA method. While various applications of KPCA are known, this paper explores the possibility to use it for building a data-driven model of a non-linear system-the water distribution system of the Chojnice town (Poland). This model is utilised for fault detection with the emphasis on water leakage detection. A systematic description of the system's framework is followed by evaluation of its performance. Simulations prove that the presented approach is both flexible and efficient.
3
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Bringing introspection into BlobSeer: Towards a self-adaptive distributed data management system

88%
Carpen-Amarie A., Costan A., Cai J., Antoniu G., Bougé L.
International Journal of Applied Mathematics and Computer Science
|
2011
|
tom 21
|
nr 2
229-242
EN
Introspection is the prerequisite of autonomic behavior, the first step towards performance improvement and resource usage optimization for large-scale distributed systems. In grid environments, the task of observing the application behavior is assigned to monitoring systems. However, most of them are designed to provide general resource information and do not consider specific information for higher-level services. More precisely, in the context of data-intensive applications, a specific introspection layer is required to collect data about the usage of storage resources, data access patterns, etc. This paper discusses the requirements for an introspection layer in a data management system for large-scale distributed infrastructures. We focus on the case of BlobSeer, a large-scale distributed system for storing massive data. The paper explains why and how to enhance BlobSeer with introspective capabilities and proposes a three-layered architecture relying on the MonALISA monitoring framework. We illustrate the autonomic behavior of BlobSeer with a self-configuration component aiming to provide storage elasticity by dynamically scaling the number of data providers. Then we propose a preliminary approach for enabling self-protection for the BlobSeer system, through a malicious client detection component. The introspective architecture has been evaluated on the Grid'5000 testbed, with experiments that prove the feasibility of generating relevant information related to the state and behavior of the system.
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