5,880 research outputs found
Disaster-Resilient Control Plane Design and Mapping in Software-Defined Networks
Communication networks, such as core optical networks, heavily depend on
their physical infrastructure, and hence they are vulnerable to man-made
disasters, such as Electromagnetic Pulse (EMP) or Weapons of Mass Destruction
(WMD) attacks, as well as to natural disasters. Large-scale disasters may cause
huge data loss and connectivity disruption in these networks. As our dependence
on network services increases, the need for novel survivability methods to
mitigate the effects of disasters on communication networks becomes a major
concern. Software-Defined Networking (SDN), by centralizing control logic and
separating it from physical equipment, facilitates network programmability and
opens up new ways to design disaster-resilient networks. On the other hand, to
fully exploit the potential of SDN, along with data-plane survivability, we
also need to design the control plane to be resilient enough to survive network
failures caused by disasters. Several distributed SDN controller architectures
have been proposed to mitigate the risks of overload and failure, but they are
optimized for limited faults without addressing the extent of large-scale
disaster failures. For disaster resiliency of the control plane, we propose to
design it as a virtual network, which can be solved using Virtual Network
Mapping techniques. We select appropriate mapping of the controllers over the
physical network such that the connectivity among the controllers
(controller-to-controller) and between the switches to the controllers
(switch-to-controllers) is not compromised by physical infrastructure failures
caused by disasters. We formally model this disaster-aware control-plane design
and mapping problem, and demonstrate a significant reduction in the disruption
of controller-to-controller and switch-to-controller communication channels
using our approach.Comment: 6 page
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
Leveraging Semantic Web Technologies for Managing Resources in a Multi-Domain Infrastructure-as-a-Service Environment
This paper reports on experience with using semantically-enabled network
resource models to construct an operational multi-domain networked
infrastructure-as-a-service (NIaaS) testbed called ExoGENI, recently funded
through NSF's GENI project. A defining property of NIaaS is the deep
integration of network provisioning functions alongside the more common storage
and computation provisioning functions. Resource provider topologies and user
requests can be described using network resource models with common base
classes for fundamental cyber-resources (links, nodes, interfaces) specialized
via virtualization and adaptations between networking layers to specific
technologies.
This problem space gives rise to a number of application areas where semantic
web technologies become highly useful - common information models and resource
class hierarchies simplify resource descriptions from multiple providers,
pathfinding and topology embedding algorithms rely on query abstractions as
building blocks.
The paper describes how the semantic resource description models enable
ExoGENI to autonomously instantiate on-demand virtual topologies of virtual
machines provisioned from cloud providers and are linked by on-demand virtual
connections acquired from multiple autonomous network providers to serve a
variety of applications ranging from distributed system experiments to
high-performance computing
Optical Network Virtualisation using Multi-technology Monitoring and SDN-enabled Optical Transceiver
We introduce the real-time multi-technology transport layer monitoring to
facilitate the coordinated virtualisation of optical and Ethernet networks
supported by optical virtualise-able transceivers (V-BVT). A monitoring and
network resource configuration scheme is proposed to include the hardware
monitoring in both Ethernet and Optical layers. The scheme depicts the data and
control interactions among multiple network layers under the software defined
network (SDN) background, as well as the application that analyses the
monitored data obtained from the database. We also present a re-configuration
algorithm to adaptively modify the composition of virtual optical networks
based on two criteria. The proposed monitoring scheme is experimentally
demonstrated with OpenFlow (OF) extensions for a holistic (re-)configuration
across both layers in Ethernet switches and V-BVTs
Multicast Aware Virtual Network Embedding in Software Defined Networks
The Software Defined Networking (SDN) provides not only a higher level abstraction of lower level functionalities, but also flexibility to create new multicast framework. SDN decouples the low level network elements (forwarding/data plane) from the control/management layer (control plane), where a centralized controller can access and modify the configuration of each distributed network element. The centralized framework allows to develop more network functionalities that can not be easily achieved in the traditional network architecture.
Similarly, Network Function Virtualization (NFV) enables the decoupling of network services from the underlying hardware infrastructure to allow the same Substrate (Physical) Network (SN) shared by multiple Virtual Network (VN) requests. With the network virtualization, the process of mapping virtual nodes and links onto a shared SN while satisfying the computing and bandwidth constraints is referred to as Virtual Network Embedding (VNE), an NP-Hard problem. The VNE problem has drawn a lot of attention from the research community.
In this dissertation, we motivate the importance of characterizing the mode of communication in VN requests, and we focus our attention on the problem of embedding VNs with one-to-many (multicast) communication mode. Throughout the dissertation, we highlight the unique properties of multicast VNs and explore how to efficiently map a given Virtual Multicast Tree/Network (VMT) request onto a substrate IP Network or Elastic Optical Networks (EONs). The major objective of this dissertation is to study how to efficiently embed (i) a given virtual request in IP or optical networks in the form of a multicast tree while minimizing the resource usage and avoiding the redundant multicast tranmission, (ii) a given virtual request in optical networks while minimizing the resource usage and satisfying the fanout limitation on the multicast transmission. Another important contribution of this dissertation is how to efficiently map Service Function Chain (SFC) based virtual multicast request without prior constructed SFC while minimizing the resource usage and satisfying the SFC on the multicast transmission
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