Using POMDP as Modeling Framework for Network Fault Management

For highדּpeed networks, it is important that fault management be proactive--i.e., detect, diagnose, and mitigate problems before they result in severe degradation of network performance. Proactive fault manageשּׂent depends on monitoring the network to obtain the data on which to base manager decisions. However, monitoring introduces additional overhead that may itself degrade network performance especially when the network is in a stressed state. Thus, a tradeoff must be made be﫠tween the amount of data collected and transferred on one hand, and the speed and accuracy of fault detection and diagnosis on the other hand. Such a tradeoff can be naturally formulated as a Partially Observable Markov decision process (POMDP).Since exact solution of POMDPs for a realistic number of states is computationally prohibitive, we develop a reinforcementשּׁearningﬢased fast algorithm which learns the decisionגּule in an approximate network simulator and makes it fast deployable to the real network. Simulation results are given to diagnose a switch fault in an ATM network. This approach can be applied to centralized fault management or to construct intelligent agents for distributed fault management

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

International audienceIntrospection 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. 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 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

Metodologia para desenvolvimento de sistema de correção de alarmes de telecomunicações /

Dissertação (Mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico

Modelling of reliable service based operations support system (MORSBOSS)

Philosophiae Doctor - PhDThe underlying theme of this thesis is identification, classification, detection and prediction of cellular network faults using state of the art technologies, methods and algorithms