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IntOpt: In-Band Network Telemetry Optimization for NFV Service Chain Monitoring
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (Disco, Computer Networking)ORCID iD: 0000-0002-8925-6859
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (Disco, Computer Networking)ORCID iD: 0000-0002-9446-8143
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (Disco, Computer Networking)ORCID iD: 0000-0001-7358-8675
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (Disco, Computer Networking)ORCID iD: 0000-0002-6101-4305
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2019 (English)In: 2019 IEEE International Conference on Communications (ICC) Próceedings, IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Managing and scaling virtual network function(VNF) service chains require the collection and analysis ofnetwork statistics and states in real time. Existing networkfunction virtualization (NFV) monitoring frameworks either donot have the capabilities to express the range of telemetryitems needed to perform management or do not scale tolarge traffic volumes and rates. We present IntOpt, a scalableand expressive telemetry system designed for flexible VNFservice chain network monitoring using active probing. IntOptallows to specify monitoring requirements for individual servicechain, which are mapped to telemetry item collection jobsthat fetch the required telemetry items from P4 (programmingprotocol-independent packet processors) programmable dataplaneelements. In our approach, the SDN controller creates theminimal number of monitoring flows to monitor the deployedservice chains as per their telemetry demands in the network.We propose a simulated annealing based random greedy metaheuristic(SARG) to minimize the overhead due to activeprobing and collection of telemetry items. Using P4-FPGA, webenchmark the overhead for telemetry collection and compareour simulated annealing based approach with a na¨ıve approachwhile optimally deploying telemetry collection probes. Ournumerical evaluation shows that the proposed approach canreduce the monitoring overhead by 39% and the total delays by57%. Such optimization may as well enable existing expressivemonitoring frameworks to scale for larger real-time networks.

Place, publisher, year, edition, pages
IEEE, 2019.
Series
IEEE International Conference on Communications, ISSN 1550-3607, E-ISSN 1938-1883
Keywords [en]
In-band Network Telemetry, Monitoring, P4, Service Function Chain, Software Defined Networks
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-74631DOI: 10.1109/ICC.2019.8761722ISI: 000492038804033ISBN: 978-1-5386-8089-6 (print)ISBN: 978-1-5386-8088-9 (electronic)OAI: oai:DiVA.org:kau-74631DiVA, id: diva2:1348380
Conference
IEEE ICC 2019: IEEE International Conference on Communications 2019 Shanghai, China 20-24 May
Projects
HITS, 4707
Funder
Knowledge FoundationAvailable from: 2019-09-04 Created: 2019-09-04 Last updated: 2019-12-18Bibliographically approved
In thesis
1. SDN-Enabled Resiliency, Monitoring and Control in Computer Networks
Open this publication in new window or tab >>SDN-Enabled Resiliency, Monitoring and Control in Computer Networks
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Next generation networks aim to increase network convergence by allowing a single network architecture to serve diverse traffic types ranging from high-bandwidth video streaming to low-latency industrial automation, while meeting their respective service level requirements. Such a converged network architecture puts high requirements on flexibility, interoperability, and resilience. While current networks exhibit some degree of network convergence, they may not reach the level of interoperability required for future application areas. This is particularly prevalent in networks that depend heavily on closed and proprietary equipment, such as industrial automation and small cell backhaul networks. Recently, Software Defined Networking (SDN) and Network Function Virtualization (NFV) have been proposed as solutions for increased network flexibility. By separating and logically centralizing the network control plane, SDN allows for dynamic control of the network infrastructure. NFV, on the other hand, enables flexibility and scalability through the virtualization and orchestration of network functions.

In this thesis, we investigate how SDN and NFV can be used to make next generation networks more reliable, flexible and programmable. We focus mainly on three areas: resiliency, monitoring, and control. First, we look at the usage of SDN to enable in-network resiliency in wireless access, backhaul and industrial automation networks. Next, we design and evaluate FastReact, a switch program that allows industrial automation networks to partially offload their distributed application logic to the data plane, reducing end to end latency and increasing network resiliency. Finally, we propose combining FastReact control with in-network telemetry event detection, significantly increasing the monitoring capacity by selectively discarding redundant telemetry information in the data plane.

Abstract [en]

Next generation computer networks aim to provide a single network architecture, which can support any type of service, ranging from high-bandwidth video streaming to low-latency industrial automation. Those services have a wide range of network requirements that must be supported by a single converged network, which puts high requirements on flexibility, interoperability, and resilience.

Recently, Software Defined Networking (SDN) and Network Function Virtualization (NFV) have been proposed as solutions for increased network flexibility. By separating and logically centralizing the network control plane, SDN allows for dynamic control of the network infrastructure. NFV, on the other hand, enables flexibility and scalability through the virtualization and orchestration of network functions.

In this thesis, we investigate how SDN and NFV can be used to make next generation networks more reliable, flexible and programmable. We focus mainly on three different areas: resiliency, monitoring, and control, and how they can be improved upon through using SDN. 

Place, publisher, year, edition, pages
Karlstad: Karlstads universitet, 2020. p. 39
Series
Karlstad University Studies, ISSN 1403-8099 ; 2020:2
Keywords
software defined networking, data plane programming, wireless, industrial automation, in-network telemetry, complex event processing
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-75953 (URN)978-91-7867-074-1 (ISBN)978-91-7867-075-8 (ISBN)
Public defence
2020-02-04, 1B306, Fryxellsalen, Universitetsgatan 1, KARLSTAD, 10:15 (English)
Opponent
Supervisors
Available from: 2020-01-13 Created: 2019-12-12 Last updated: 2020-02-03Bibliographically approved

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Bhamare, DevalKassler, AndreasVestin, JonathanKhoshkholghi, Mohammad AliTaheri, Javid

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