Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.798478
Title: Accurate and resource-efficient monitoring for future networks
Author: Tangari, Gioacchino
ISNI:       0000 0004 8507 5529
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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Abstract:
Monitoring functionality is a key component of any network management system. It is essential for profiling network resource usage, detecting attacks, and capturing the performance of a multitude of services using the network. Traditional monitoring solutions operate on long timescales producing periodic reports, which are mostly used for manual and infrequent network management tasks. However, these practices have been recently questioned by the advent of Software Defined Networking (SDN). By empowering management applications with the right tools to perform automatic, frequent, and fine-grained network reconfigurations, SDN has made these applications more dependent than before on the accuracy and timeliness of monitoring reports. As a result, monitoring systems are required to collect considerable amounts of heterogeneous measurement data, process them in real-time, and expose the resulting knowledge in short timescales to network decision-making processes. Satisfying these requirements is extremely challenging given today's larger network scales, massive and dynamic traffic volumes, and the stringent constraints on time availability and hardware resources. This PhD thesis tackles this important challenge by investigating how an accurate and resource-efficient monitoring function can be realised in the context of future, software-defined networks. Novel monitoring methodologies, designs, and frameworks are provided in this thesis, which scale with increasing network sizes and automatically adjust to changes in the operating conditions. These achieve the goal of efficient measurement collection and reporting, lightweight measurement- data processing, and timely monitoring knowledge delivery.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.798478  DOI: Not available
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