Pós-Graduação em Ciência da Computação – UFPE
Defesa de Dissertação de Mestrado Nº 1.265
Aluno: Thiago Pereira de Brito Vieira
Orientador: Prof. Vinicius Cardoso Garcia
Co-orientador: Prof. Stenio Flávio de Lacerda Fernandes
Título: An Approach for Profiling Distributed Applications Through Network Traffic Analysis
Data: 05/03/2013
Hora/Local: 14:00h – Sala D224
Banca Examinadora:
- Prof. Jose Augusto Suruagy Monteiro (UFPE / CIn)
- Prof. Denio Mariz Timoteo de Sousa (IFPB / Coinfo)
- Prof. Vinicius Cardoso Garcia (UFPE / CIn)[[MORE]]
RESUMO:
Distributed systems has been adopted for building modern Internet services and cloud computing infrastructure, in order to obtain services with high performance, scalability and reliability. Cloud computing service level agreements (SLA) require low time to identify, diagnose and solve problems in its production infrastructure, in order to avoid problem impacts into quality of service provided for its clients. Thus, the detection of error causes, diagnosing and reproduction of errors are challenges that motivate efforts to the development of less intrusive mechanisms for monitoring and debugging distributed applications at runtime. Network traffic analysis is one option to the distributed systems measurement, although there are limitations on capacity to process large amounts of network traffic in short time, and on scalability to process network traffic where there is variation of resource demand.
The goal of this dissertation is to solve the processing capacity problem for measuring distributed systems through network traffic analysis, in order to evaluate distributed systems at a data center, using commodity hardware and cloud computing services, in a minimally intrusive way.
We proposed a new approach based on MapReduce, for deep inspection of distributed application traffic, in order to evaluate the behavior of distributed systems at runtime, using commodity hardware. In this dissertation we evaluated the effectiveness of MapReduce for a deep packet inspection algorithm, its processing capacity, completion time speedup, processing capacity scalability, and the behavior followed by MapReduce phases, when applied to deep packet inspection, for extracting indicators of distributed applications.
Palavras-chave: Distributed Application Measurement, Profiling, MapReduce, Network Traffic Analysis, Packet Analysis, Deep Packet Inspection