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SDN-Enabled Resiliency, Monitoring and Control in Computer Networks
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013).ORCID-id: 0000-0001-7358-8675
2020 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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. 

Ort, förlag, år, upplaga, sidor
Karlstad: Karlstads universitet, 2020. , s. 39
Serie
Karlstad University Studies, ISSN 1403-8099 ; 2020:2
Nyckelord [en]
software defined networking, data plane programming, wireless, industrial automation, in-network telemetry, complex event processing
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
URN: urn:nbn:se:kau:diva-75953ISBN: 978-91-7867-074-1 (tryckt)ISBN: 978-91-7867-075-8 (digital)OAI: oai:DiVA.org:kau-75953DiVA, id: diva2:1377732
Disputation
2020-02-04, 1B306, Fryxellsalen, Universitetsgatan 1, KARLSTAD, 10:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2020-01-13 Skapad: 2019-12-12 Senast uppdaterad: 2020-02-03Bibliografiskt granskad
Delarbeten
1. IntOpt: In-Band Network Telemetry Optimization for NFV Service Chain Monitoring
Öppna denna publikation i ny flik eller fönster >>IntOpt: In-Band Network Telemetry Optimization for NFV Service Chain Monitoring
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2019 (Engelska)Ingår i: 2019 IEEE International Conference on Communications (ICC) Próceedings, IEEE, 2019Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
IEEE, 2019
Serie
IEEE International Conference on Communications, ISSN 1550-3607, E-ISSN 1938-1883
Nyckelord
In-band Network Telemetry, Monitoring, P4, Service Function Chain, Software Defined Networks
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-74631 (URN)10.1109/ICC.2019.8761722 (DOI)000492038804033 ()978-1-5386-8089-6 (ISBN)978-1-5386-8088-9 (ISBN)
Konferens
IEEE ICC 2019: IEEE International Conference on Communications 2019 Shanghai, China 20-24 May
Projekt
HITS, 4707
Forskningsfinansiär
KK-stiftelsen
Tillgänglig från: 2019-09-04 Skapad: 2019-09-04 Senast uppdaterad: 2019-12-18Bibliografiskt granskad
2. QoS Enabled WiFi MAC Layer Processing as an Example of a NFV Service
Öppna denna publikation i ny flik eller fönster >>QoS Enabled WiFi MAC Layer Processing as an Example of a NFV Service
2015 (Engelska)Ingår i: Network Softwarization (NetSoft), 2015 1st IEEE Conference on, IEEE, 2015, s. 1-9Konferensbidrag, Publicerat paper (Refereegranskat)
Ort, förlag, år, upplaga, sidor
IEEE, 2015
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-38735 (URN)10.1109/NETSOFT.2015.7116164 (DOI)000380562800045 ()
Konferens
1st IEEE Conference on Network Softwarization (NetSoft), 13-17 April 2015, London
Tillgänglig från: 2015-11-27 Skapad: 2015-11-27 Senast uppdaterad: 2019-12-12Bibliografiskt granskad
3. Low frequency assist for mmWave backhaul - the case for SDN resiliency mechanisms
Öppna denna publikation i ny flik eller fönster >>Low frequency assist for mmWave backhaul - the case for SDN resiliency mechanisms
2017 (Engelska)Ingår i: Communications Workshops (ICC Workshops), 2017 IEEE International Conference on, IEEE, 2017Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

In 5G, network densification is a major concern for operators. When a massive amount of small cells are deployed, the backhaul capacity is crucial and researchers are exploring the use of high frequency bands such as 28, 60 or even 140 GHz because of the large portion of spectrum that is available. Unfortunately, such mmWave links frequently change their capacity due to blocking and weather phenomena which makes it challenging to design stable multihop backhaul networks using those frequency bands. In this paper, we investigate the use of Software Defined Networking (SDN) for the operation and control of wireless backhaul networks. We explore different ways how SDN resiliency mechanisms such as FastFailover Groups can be used to mitigate disruptive connectivity in the multihop operation due to mmWave links frequently failing. We also demonstrate a clear benefit for using low frequency assist mode, where the small cell has an additional stable LTE uplink to the eNB that is used should the mmWave backhaul links fail. Our experiments using a network emulator show that such SDN based local repair mechanisms can significantly reduce the packet loss rate inside the mmWave backhaul mesh, which can be further reduced with an LTE assisted Failover.

Ort, förlag, år, upplaga, sidor
IEEE, 2017
Serie
IEEE/CIC International Conference on Communications in China - Workshops (CIC/ICCC), ISSN 2474-9133
Nyckelord
5G mobile communication, Long Term Evolution, software defined networking, telecommunication network reliability
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-65609 (URN)10.1109/ICCW.2017.7962658 (DOI)000464321500035 ()978-1-5090-1526-9 (ISBN)978-1-5090-1525-2 (ISBN)
Konferens
IEEE International Conference on Communications Workshops (ICC Workshops), 21-25 May 2017. Paris, France
Tillgänglig från: 2018-01-15 Skapad: 2018-01-15 Senast uppdaterad: 2019-12-12Bibliografiskt granskad
4. FastReact: In-Network Control And Caching For Industrial Control Networks Using Programmable Data Planes
Öppna denna publikation i ny flik eller fönster >>FastReact: In-Network Control And Caching For Industrial Control Networks Using Programmable Data Planes
2018 (Engelska)Ingår i: 2018 IEEE 23RD INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), IEEE, 2018, s. 219-226Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Providing network reliability as well as low and predictable latency is important especially for Industrial Automation and Control Networks. However, diagnosing link status from the control plane has high latency and overhead. In addition, the communication with the industrial controller may impose additional network latency. We present FastReact - a system enabling In-Network monitoring, control and caching for Industrial Automation and Control Networks. FastReact outsources simple monitoring and control actions to evolving programmable data planes using the P4 language. As instructed by the Industrial Controller through a Northbound API, the SDN controller composes control actions using Boolean Logic which are then installed in the data plane. The data plane parses and caches sensor values and performs simple calculations on them which are connected to fast control actions that are executed locally. For resiliency, FastReact monitors liveness and response of sensors/actuators and performs a fast local link repair in the data plane if a link failure is detected. Our testbed measurement show that FastReact can reduce the sensor/actuator delay while being resilient against several failure events.

Ort, förlag, år, upplaga, sidor
IEEE, 2018
Serie
IEEE International Conference on Emerging Technologies and Factory Automation-ETFA, ISSN 1946-0740
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-70292 (URN)000449334500026 ()978-1-5386-7108-5 (ISBN)
Konferens
23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), SEP 04-07, 2018, Politecnico Torino, Torino, ITALY
Tillgänglig från: 2018-11-23 Skapad: 2018-11-23 Senast uppdaterad: 2019-12-12Bibliografiskt granskad
5. Resilient Software Defined Networking for Industrial Control Networks
Öppna denna publikation i ny flik eller fönster >>Resilient Software Defined Networking for Industrial Control Networks
2015 (Engelska)Ingår i: 2015 10th International Conference on Information, Communications and Signal Processing (ICICS), IEEE, 2015Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Software Defined Networking (SDN) is currently a hot topic in the area of Datacenter Networking or Enterprise Networks as it has the promise to radically simplify network management and operation. However, it has not been considered so far as a promising candidate for Industrial Control Networks mainly because of the deterministic performance requirements and the dedicated design of those networks to fulfil strict performance guarantees. In this paper, we propose a resilient SDN based architecture for Industrial Control Networks and show that by combining several SDN based fast failover technologies using per-link Bidirectional Forwarding Detection (BFD), preconfigured primary and backup paths and flexible packet duplication orchestrated by an SDN controller, we can reduce significantly the control latency and provide more stringent performance guarantees even under lossy and failing links.

Ort, förlag, år, upplaga, sidor
IEEE, 2015
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-38748 (URN)10.1109/ICICS.2015.7459981 (DOI)000381754800168 ()978-1-4673-7216-9 (ISBN)
Konferens
ICICS 2015 - The 10th International Conference on Information, Communications and Signal Processing, December 2-4 2015, Singapore.
Forskningsfinansiär
KK-stiftelsen, READY
Tillgänglig från: 2015-11-27 Skapad: 2015-11-27 Senast uppdaterad: 2019-12-12Bibliografiskt granskad
6. Programmable Event Detection for In-Band Network Telemetry
Öppna denna publikation i ny flik eller fönster >>Programmable Event Detection for In-Band Network Telemetry
Visa övriga...
2019 (Engelska)Konferensbidrag (Refereegranskat)
Abstract [en]

In-Band Network Telemetry (INT) is a novel framework for collecting telemetry items and switch internal state information from the data plane at line rate. With the suppor programmable data planes and programming language P4,switches parse telemetry instruction headers and determine which telemetry items to attach using custom metadata. At the network edge, telemetry information is removed and the original packets are forwarded while telemetry reports are sent to a distributed stream processor for further processing by a network monitoring platform. In order to avoid excessive load on the stream processor, telemetry items should not be sent for each individual packet but rather when certain events are triggered. In this paper, we develop a programmable INT event detection mechanism in P4 that allows customization of which events to report to the monitoring system, on a per-flow basis, from the control plane. At the stream processor, we implement a fast INT report collector using the kernel bypass technique AF XDP, which parses telemetry reports and streams them to a distributed Kafka cluster, which can apply machine learning, visualization and further monitoring tasks. In our evaluation, we use realworld traces from different data center workloads and show that our approach is highly scalable and significantly reduces the network overhead and stream processor load due to effective event pre-filtering inside the switch data plane. While the INT report collector can process around 3 Mpps telemetry reports per core, using event pre-filtering increases the capacity by 10-15x.

Nationell ämneskategori
Telekommunikation
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-75832 (URN)
Konferens
IEEE Cloud Net 4-6 november
Projekt
HITS, 4707
Forskningsfinansiär
KK-stiftelsen
Tillgänglig från: 2019-11-27 Skapad: 2019-11-27 Senast uppdaterad: 2019-12-12
7. In-Network Caching and Control for Industrial Automation Publish/Subscribe Networks
Öppna denna publikation i ny flik eller fönster >>In-Network Caching and Control for Industrial Automation Publish/Subscribe Networks
(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Nationell ämneskategori
Data- och informationsvetenskap
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-76287 (URN)
Tillgänglig från: 2020-01-13 Skapad: 2020-01-13 Senast uppdaterad: 2020-01-13Bibliografiskt granskad

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