5,171 research outputs found
On QoS-assured degraded provisioning in service-differentiated multi-layer elastic optical networks
The emergence of new network applications is driving network operators to not
only fulfill dynamic bandwidth requirements, but offer various grades of
service. Degraded provisioning provides an effective solution to flexibly
allocate resources in various dimensions to reduce blocking for differentiated
demands when network congestion occurs. In this work, we investigate the novel
problem of online degraded provisioning in service-differentiated multi-layer
networks with optical elasticity. Quality of Service (QoS) is assured by
service-holding-time prolongation and immediate access as soon as the service
arrives without set-up delay. We decompose the problem into degraded routing
and degraded resource allocation stages, and design polynomial-time algorithms
with the enhanced multi-layer architecture to increase the network flexibility
in temporal and spectral dimensions. Illustrative results verify that we can
achieve significant reduction of network service failures, especially for
requests with higher priorities. The results also indicate that degradation in
optical layer can increase the network capacity, while the degradation in
electric layer provides flexible time-bandwidth exchange.Comment: accepted by IEEE GLOBECOM 201
Energy management in communication networks: a journey through modelling and optimization glasses
The widespread proliferation of Internet and wireless applications has
produced a significant increase of ICT energy footprint. As a response, in the
last five years, significant efforts have been undertaken to include
energy-awareness into network management. Several green networking frameworks
have been proposed by carefully managing the network routing and the power
state of network devices.
Even though approaches proposed differ based on network technologies and
sleep modes of nodes and interfaces, they all aim at tailoring the active
network resources to the varying traffic needs in order to minimize energy
consumption. From a modeling point of view, this has several commonalities with
classical network design and routing problems, even if with different
objectives and in a dynamic context.
With most researchers focused on addressing the complex and crucial
technological aspects of green networking schemes, there has been so far little
attention on understanding the modeling similarities and differences of
proposed solutions. This paper fills the gap surveying the literature with
optimization modeling glasses, following a tutorial approach that guides
through the different components of the models with a unified symbolism. A
detailed classification of the previous work based on the modeling issues
included is also proposed
Practical issues for the implementation of survivability and recovery techniques in optical networks
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
- …