84,835 research outputs found
Segment Routing: a Comprehensive Survey of Research Activities, Standardization Efforts and Implementation Results
Fixed and mobile telecom operators, enterprise network operators and cloud
providers strive to face the challenging demands coming from the evolution of
IP networks (e.g. huge bandwidth requirements, integration of billions of
devices and millions of services in the cloud). Proposed in the early 2010s,
Segment Routing (SR) architecture helps face these challenging demands, and it
is currently being adopted and deployed. SR architecture is based on the
concept of source routing and has interesting scalability properties, as it
dramatically reduces the amount of state information to be configured in the
core nodes to support complex services. SR architecture was first implemented
with the MPLS dataplane and then, quite recently, with the IPv6 dataplane
(SRv6). IPv6 SR architecture (SRv6) has been extended from the simple steering
of packets across nodes to a general network programming approach, making it
very suitable for use cases such as Service Function Chaining and Network
Function Virtualization. In this paper we present a tutorial and a
comprehensive survey on SR technology, analyzing standardization efforts,
patents, research activities and implementation results. We start with an
introduction on the motivations for Segment Routing and an overview of its
evolution and standardization. Then, we provide a tutorial on Segment Routing
technology, with a focus on the novel SRv6 solution. We discuss the
standardization efforts and the patents providing details on the most important
documents and mentioning other ongoing activities. We then thoroughly analyze
research activities according to a taxonomy. We have identified 8 main
categories during our analysis of the current state of play: Monitoring,
Traffic Engineering, Failure Recovery, Centrally Controlled Architectures, Path
Encoding, Network Programming, Performance Evaluation and Miscellaneous...Comment: SUBMITTED TO IEEE COMMUNICATIONS SURVEYS & TUTORIAL
A Survey on the Contributions of Software-Defined Networking to Traffic Engineering
Since the appearance of OpenFlow back in 2008, software-defined networking (SDN) has gained momentum. Although there are some discrepancies between the standards developing organizations working with SDN about what SDN is and how it is defined, they all outline traffic engineering (TE) as a key application. One of the most common objectives of TE is the congestion minimization, where techniques such as traffic splitting among multiple paths or advanced reservation systems are used. In such a scenario, this manuscript surveys the role of a comprehensive list of SDN protocols in TE solutions, in order to assess how these protocols can benefit TE. The SDN protocols have been categorized using the SDN architecture proposed by the open networking foundation, which differentiates among data-controller plane interfaces, application-controller plane interfaces, and management interfaces, in order to state how the interface type in which they operate influences TE. In addition, the impact of the SDN protocols on TE has been evaluated by comparing them with the path computation element (PCE)-based architecture. The PCE-based architecture has been selected to measure the impact of SDN on TE because it is the most novel TE architecture until the date, and because it already defines a set of metrics to measure the performance of TE solutions. We conclude that using the three types of interfaces simultaneously will result in more powerful and enhanced TE solutions, since they benefit TE in complementary ways.European Commission through the Horizon 2020 Research and Innovation Programme (GN4) under Grant 691567
Spanish Ministry of Economy and Competitiveness under the Secure Deployment of Services Over SDN and NFV-based Networks Project S&NSEC under Grant TEC2013-47960-C4-3-
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
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