Bringing Introspection into BlobSeer: Towards a Self-Adaptative Distributed Data Management System

Abstract : Introspection is the prerequisite of an autonomic behavior, the first step towards a performance improvement and a 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, about 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. Then we propose a preliminary approach for enabling self-protection for the BlobSeer system, through a malicious clients 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 the behavior of the system.
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Contributor : Alexandra Carpen-Amarie <>
Submitted on : Tuesday, November 16, 2010 - 2:09:30 PM
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Alexandra Carpen-Amarie, Jing Cai, Alexandru Costan, Gabriel Antoniu, Luc Bougé. Bringing Introspection into BlobSeer: Towards a Self-Adaptative Distributed Data Management System. [Research Report] RR-7452, INRIA. 2010, pp.22. ⟨inria-00536556⟩

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