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SDN implementations and protocols
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO)ORCID iD: 0000-0001-7734-1653
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO)ORCID iD: 0000-0001-9866-8209
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO)ORCID iD: 0000-0001-9194-010X
2018 (English)In: Big Data and Software Defined Networks / [ed] Javid Taheri, IET Digital Library, 2018, 1, p. 27-48Chapter in book (Refereed)
Abstract [en]

This chapter begins by explaining the main SDN concepts with the focus on a SDN controller. It presents the most important aspects to consider when we desire to go from traditional network to a SDN networks. We present an in-depth analysis of the most commonly used and modern SDN controllers and analyse the main features, capabilities and requirements of one of the presented controllers. OpenFlow is the standard leading in the market allowing the management of the forwarding plane devices such as routers or switches. While there are other standards with the same aim, OpenFlow has secured a position in the market and has been expanded rapidly. Therefore, an analysis is presented on a different OpenFlow compatible device for the implementation of an SDN network. This study encompasses both software and hardware solutions along with the scope of implementation or use of these devices. This chapter ends up presenting a description of OpenFlow protocol alternatives, a more detailed description of OpenFlow and its components and other wellknown southbound protocols involved for the management and configuration of the devices.

Place, publisher, year, edition, pages
IET Digital Library, 2018, 1. p. 27-48
Keywords [en]
software defined networking; protocols
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-67209DOI: 10.1049/PBPC015E_ch2ISBN: 978-1-78561-304-3 (print)ISBN: 978-1-78561-305-0 (electronic)OAI: oai:DiVA.org:kau-67209DiVA, id: diva2:1201994
Available from: 2018-04-27 Created: 2018-04-27 Last updated: 2021-04-19Bibliographically approved
In thesis
1. Traffic Management in Software-Defined Data Center Networks
Open this publication in new window or tab >>Traffic Management in Software-Defined Data Center Networks
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Traffic management in data centers is paramount to improving network and application performance, thereby improving the quality of service by reducing network congestion, packet loss, and latency. However, the deployment and configuration of traffic management techniques are challenging due to diverse data-center traffic characteristics, large data center topologies, and the interplay of different protocols at the routing, transport, and link layer. Software-Defined Networking (SDN) emerges as a new paradigm towards a centralized network configuration and traffic management by decoupling the control plane from forwarding devices. Despite its holistic view of the network, data centers are commonly interconnected by traditional networks that use standard routing protocols. It is therefore essential to achieve interoperability with legacy systems, end-to-end traffic management, and to avoid the cumbersome, time-consuming, and error-prone configuration process of data-center edge network devices.

In this thesis, we aim to improve traffic management and its configuration for software-defined data center networks. To achieve this objective, we provide novel approaches that enhance the control plane as well as leverage novel concepts of data plane programmability.

At the control plane, we first propose different mechanisms that enable the fast restoration of network connectivity after a virtual machine migration. Second, we suggest a network management automation framework that extends layer 2 connectivity to the tenants' services hosted across geo-distributed data centers. Moreover, we provide high-level policy-based mechanisms that make network configuration and traffic management simpler for data-center operators. At the data plane, we develop MP-HULA that load-balances multipath connections across least-congested paths. MP-HULA leverages advanced data plane mechanisms to rank multiple paths according to congestion metrics and uses that information for fine-grained load-balancing decisions considering transport layer information. To improve flowlet-based load-balancing decisions, we propose FlowDyn, which efficiently estimates round-trip time using programmable telemetry data. Finally, we present pCoflow, an in-network support mechanism that uses advanced programmable scheduling primitives to effectively avoid reordering for data-parallel applications even when there are flow priority changes due to global coflow scheduling updates.

Abstract [en]

Traffic management in data centers is paramount to improving network and application performance, thereby improving the quality of service by reducing network congestion, packet loss, and latency. However, the deployment and configuration of traffic management techniques are challenging due to diverse data center traffic characteristics, large data center topologies, and the interplay of different protocols at the routing, transport, and link layer. Software-Defined Networking (SDN) emerges as a new paradigm towards a centralized network configuration and traffic management by decoupling the control plane from forwarding devices.

In this thesis, we aim to improve traffic management and its configuration for software-defined data center networks by providing novel approaches that enhance the control plane as well as leveraging novel concepts of data plane programmability. At the control plane, we provide solutions such as high-level based policy framework, automation of layer 2 services, and fast restoration of network connectivity for virtual machine migration, that make network configuration and traffic management simpler for data-center operators. At the data plane, we propose approaches to improve load balancing in data centers and an in-network support mechanism to avoid reordering for data-parallel applications even when there are flow priority changes due to global coflow scheduling updates.

 

Place, publisher, year, edition, pages
Karlstad: Karlstads universitet, 2021. p. 69
Series
Karlstad University Studies, ISSN 1403-8099 ; 2021:15
Keywords
SDN, data center, load-balancing, traffic management, EVPN, big data, programmable data plane
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-83671 (URN)978-91-7867-210-3 (ISBN)978-91-7867-220-2 (ISBN)
Public defence
2021-06-03, Zoom, 13:00 (English)
Opponent
Supervisors
Note

Article 8 part of doctoral thesis as manuscript, now published.

Available from: 2021-05-12 Created: 2021-04-16 Last updated: 2021-09-27Bibliographically approved

Open Access in DiVA

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Other links

Publisher's full texthttp://digital-library.theiet.org/content/books/10.1049/pbpc015e_ch2;jsessionid=4af710hogc0kq.x-iet-live-01

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Hernandez Benet, CristianAlizadeh Noghani, KyoomarsTaheri, Javid

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