7,678 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
Self-adaptive online virtual network migration in network virtualization environments
This is the peer reviewed version of the following article: Zangiabady, M, Garcia‐Robledo, A, Gorricho, J‐L, Serrat‐Fernandez, J, Rubio‐Loyola, J. Self‐adaptive online virtual network migration in network virtualization environments. Trans Emerging Tel Tech. 2019; 30:e3692. https://doi.org/10.1002/ett.3692, which has been published in final form at https://doi.org/10.1002/ett.3692. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.In Network Virtualization Environments, the capability of operators to allocate resources in the Substrate Network (SN) to support Virtual Networks (VNs) in an optimal manner is known as Virtual Network Embedding (VNE). In the same context, online VN migration is the process meant to reallocate components of a VN, or even an entire VN among elements of the SN in real time and seamlessly to end-users. Online VNE without VN migration may lead to either over- or under-utilization of the SN resources. However, VN migration is challenging due to its computational cost and the service disruption inherent to VN components reallocation. Online VN migration can reduce migration costs insofar it is triggered proactively, not reactively, at critical times, avoiding the negative effects of both under- and over-triggering. This paper presents a novel online cost-efficient mechanism that self-adaptively learns the exact moments when triggering VN migration is likely to be profitable in the long term. We propose a novel self-adaptive mechanism based on Reinforcement Learning that determines the right trigger online VN migration times, leading to the minimization of migration costs while simultaneously considering the online VNE acceptance ratio.Peer ReviewedPostprint (author's final draft
Time-varying resilient virtual network mapping for multi-location cloud data centers
Optical networks constitute a fundamental building block that has enabled the success of cloud computing. Virtualization, a cornerstone of cloud computing, today is applied in the networking field: physical network infrastructure is logically partitioned into separate virtual networks, thus providing isolation between distinct virtual network operators (VNOs). Hence, the problem of virtual network mapping has arisen: how to decide which physical resources to allocate for a particular virtual network? In a cloud context, not just network connectivity is required, but also data center (DC) resources located at multiple locations, for computation and/or storage. Given the underlying anycast routing principle, the network operator has some freedom to which specific DC to allocate these resources.
In this paper, we solve a resilient virtual network mapping problem that optimally decides on the mapping of both network and multi-location data center resources resiliently using anycast routing, considering time-varying traffic conditions. In terms of resilience, we consider the so-called VNO-resilience scheme, where resilience is provided in the virtual network layer. To minimize physical resource capacity requirements, we allow reuse of both network and DC resources. The failures we protect against include both network and DC resource failures: we hence allocate backup DC resources, and also account for synchronization between primary and backup DC.
As optimization criteria, we not only consider resource usage minimization, but also aim to limit virtual network reconfigurations from one time period to the next. We propose a scalable column generation approach to solve the dynamic resilient virtual network mapping problem, and demonstrate it in a case study on a nationwide US backbone network
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
Virtualisation and resource allocation in MECEnabled metro optical networks
The appearance of new network services and the ever-increasing network traffic and number
of connected devices will push the evolution of current communication networks towards the
Future Internet.
In the area of optical networks, wavelength routed optical networks (WRONs) are evolving
to elastic optical networks (EONs) in which, thanks to the use of OFDM or Nyquist WDM,
it is possible to create super-channels with custom-size bandwidth. The basic element in
these networks is the lightpath, i.e., all-optical circuits between two network nodes. The
establishment of lightpaths requires the selection of the route that they will follow and the
portion of the spectrum to be used in order to carry the requested traffic from the source to
the destination node. That problem is known as the routing and spectrum assignment (RSA)
problem, and new algorithms must be proposed to address this design problem.
Some early studies on elastic optical networks studied gridless scenarios, in which a slice
of spectrum of variable size is assigned to a request. However, the most common approach to
the spectrum allocation is to divide the spectrum into slots of fixed width and allocate multiple,
consecutive spectrum slots to each lightpath, depending on the requested bandwidth. Moreover,
EONs also allow the proposal of more flexible routing and spectrum assignment techniques,
like the split-spectrum approach in which the request is divided into multiple "sub-lightpaths".
In this thesis, four RSA algorithms are proposed combining two different levels of
flexibility with the well-known k-shortest paths and first fit heuristics. After comparing the
performance of those methods, a novel spectrum assignment technique, Best Gap, is proposed
to overcome the inefficiencies emerged when combining the first fit heuristic with highly
flexible networks. A simulation study is presented to demonstrate that, thanks to the use of
Best Gap, EONs can exploit the network flexibility and reduce the blocking ratio.
On the other hand, operators must face profound architectural changes to increase the
adaptability and flexibility of networks and ease their management. Thanks to the use of
network function virtualisation (NFV), the necessary network functions that must be applied
to offer a service can be deployed as virtual appliances hosted by commodity servers, which
can be located in data centres, network nodes or even end-user premises. The appearance of
new computation and networking paradigms, like multi-access edge computing (MEC), may
facilitate the adaptation of communication networks to the new demands. Furthermore, the
use of MEC technology will enable the possibility of installing those virtual network functions
(VNFs) not only at data centres (DCs) and central offices (COs), traditional hosts of VFNs, but
also at the edge nodes of the network. Since data processing is performed closer to the enduser,
the latency associated to each service connection request can be reduced. MEC nodes
will be usually connected between them and with the DCs and COs by optical networks.
In such a scenario, deploying a network service requires completing two phases: the
VNF-placement, i.e., deciding the number and location of VNFs, and the VNF-chaining,
i.e., connecting the VNFs that the traffic associated to a service must transverse in order to
establish the connection. In the chaining process, not only the existence of VNFs with available
processing capacity, but the availability of network resources must be taken into account to
avoid the rejection of the connection request. Taking into consideration that the backhaul of
this scenario will be usually based on WRONs or EONs, it is necessary to design the virtual
topology (i.e., the set of lightpaths established in the networks) in order to transport the tra c
from one node to another. The process of designing the virtual topology includes deciding the
number of connections or lightpaths, allocating them a route and spectral resources, and finally
grooming the traffic into the created lightpaths.
Lastly, a failure in the equipment of a node in an NFV environment can cause the
disruption of the SCs traversing the node. This can cause the loss of huge amounts of data
and affect thousands of end-users. In consequence, it is key to provide the network with faultmanagement
techniques able to guarantee the resilience of the established connections when a
node fails.
For the mentioned reasons, it is necessary to design orchestration algorithms which solve
the VNF-placement, chaining and network resource allocation problems in 5G networks
with optical backhaul. Moreover, some versions of those algorithms must also implements
protection techniques to guarantee the resilience system in case of failure.
This thesis makes contribution in that line. Firstly, a genetic algorithm is proposed to solve
the VNF-placement and VNF-chaining problems in a 5G network with optical backhaul based
on star topology: GASM (genetic algorithm for effective service mapping). Then, we propose
a modification of that algorithm in order to be applied to dynamic scenarios in which the
reconfiguration of the planning is allowed. Furthermore, we enhanced the modified algorithm
to include a learning step, with the objective of improving the performance of the algorithm.
In this thesis, we also propose an algorithm to solve not only the VNF-placement and
VNF-chaining problems but also the design of the virtual topology, considering that a WRON
is deployed as the backhaul network connecting MEC nodes and CO. Moreover, a version
including individual VNF protection against node failure has been also proposed and the
effect of using shared/dedicated and end-to-end SC/individual VNF protection schemes are
also analysed.
Finally, a new algorithm that solves the VNF-placement and chaining problems and
the virtual topology design implementing a new chaining technique is also proposed.
Its corresponding versions implementing individual VNF protection are also presented.
Furthermore, since the method works with any type of WDM mesh topologies, a technoeconomic
study is presented to compare the effect of using different network topologies in
both the network performance and cost.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione
DISCO: Distributed Multi-domain SDN Controllers
Modern multi-domain networks now span over datacenter networks, enterprise
networks, customer sites and mobile entities. Such networks are critical and,
thus, must be resilient, scalable and easily extensible. The emergence of
Software-Defined Networking (SDN) protocols, which enables to decouple the data
plane from the control plane and dynamically program the network, opens up new
ways to architect such networks. In this paper, we propose DISCO, an open and
extensible DIstributed SDN COntrol plane able to cope with the distributed and
heterogeneous nature of modern overlay networks and wide area networks. DISCO
controllers manage their own network domain and communicate with each others to
provide end-to-end network services. This communication is based on a unique
lightweight and highly manageable control channel used by agents to
self-adaptively share aggregated network-wide information. We implemented DISCO
on top of the Floodlight OpenFlow controller and the AMQP protocol. We
demonstrated how DISCO's control plane dynamically adapts to heterogeneous
network topologies while being resilient enough to survive to disruptions and
attacks and providing classic functionalities such as end-point migration and
network-wide traffic engineering. The experimentation results we present are
organized around three use cases: inter-domain topology disruption, end-to-end
priority service request and virtual machine migration
Management of Spectral Resources in Elastic Optical Networks
Recent developments in the area of mobile technologies, data center networks, cloud computing and social networks have triggered the growth of a wide range of network applications. The data rate of these applications also vary from a few megabits per second (Mbps) to several Gigabits per second (Gbps), thereby increasing the burden on the Inter- net. To support this growth in Internet data traffic, one foremost solution is to utilize the advancements in optical networks. With technology such as wavelength division multiplexing (WDM) networks, bandwidth upto 100 Gbps can be exploited from the optical fiber in an energy efficient manner. However, WDM networks are not efficient when the traffic demands vary frequently. Elastic Optical Networks (EONs) or Spectrum Sliced Elastic Optical Path Networks (SLICE) or Flex-Grid has been recently proposed as a long-term solution to handle the ever-increasing data traffic and the diverse demand range. EONs provide abundant bandwidth by managing the spectrum resources as fine-granular orthogonal sub-carriers that makes it suitable to accommodate varying traffic demands. However, the Routing and Spectrum Allocation (RSA) algorithm in EONs has to follow additional constraints while allocating sub-carriers to demands. These constraints increase the complexity of RSA in EONs and also, make EONs prone to the fragmentation of spectral resources, thereby decreasing the spectral efficiency. The major objective of this dissertation is to study the problem of spectrum allocation in EONs under various network conditions. With this objective, this dissertation presents the author\u27s study and research on multiple aspects of spectrum allocation in EONs: how to allocate sub-carriers to the traffic demands, how to accommodate traffic demands that varies with time, how to minimize the fragmentation of spectral resources and how to efficiently integrate the predictability of user demands for spectrum assignment. Another important contribution of this dissertation is the application of EONs as one of the substrate technologies for network virtualization
VNR Algorithm: A Greedy Approach For Virtual Networks Reconfigurations
6 pagesInternational audienceIn this paper we address the problem of virtual network reconfiguration. In our previous work on virtual network embedding strategies, we found that most virtual network rejections were caused by bottlenecked substrate links while peak resource use is equal to 18%. These observations lead us to propose a new greedy Virtual Network Reconfiguration algorithm, VNR. The main aim of our proposal is to 'tidy up' substrate network in order to minimise the number of overloaded substrate links, while also reducing the cost of reconfiguration. We compare our proposal with the related reconfiguration strategy VNA-Periodic, both of them are incorporated in the best existing embedding strategies VNE-AC and VNE-Greedy in terms of rejection rate. The results obtained show that VNR outperforms VNA-Periodic. Indeed, our research shows that the performances of VNR do not depend on the virtual network embedding strategy. Moreover, VNR minimises the rejection rate of virtual network requests by at least ' 83% while the cost of reconfiguration is lower than with VNA-Periodic
Exploring the benefit of rerouting multi-period traffic to multi-site data centers
In cloud-like scenarios, demand is served at one of multiple possible data center (DC) destinations. Usually, the exact DC that is used can be freely chosen, which leads to an anycast routing problem. Furthermore, the demand volume is expected to change over time, e.g., following a diurnal pattern. Given that virtually all application domains today rely heavily on cloud-like services, it is important that the backbone networks connecting users to the DCs are resilient against failures. In this paper, we consider the problem of resiliently routing multi-period traffic: we need to find routes to both a primary DC and a backup DC (to be used in the case of failure of the primary one, or of the network connection to it), and also account for synchronization traffic between the primary and backup DCs. We formulate this as an optimization problem and adopt column generation, using a path formulation in two sub-problems: the (restricted) master problem selects "configurations" to use for each demand in each of the time epochs it lasts, while the pricing problem (PP) constructs a new "configuration" that can lead to lower overall costs (which we express as the number of network resources, i.e., bandwidth, required to serve the demand). Here, a "configuration" is defined by the network paths followed from the demand source to each of the two selected DCs, as well as that of the synchronization traffic in between the DCs. Our decomposition allows for PPs to be solved in parallel, for which we quantitatively explore the reduction in the time required to solve the overall routing problem. The key question that we address with our model is an exploration of the potential benefits of rerouting traffic from one time epoch to the next: we compare several (re) routing strategies, allowing traffic that spans multiple time periods to i) not be rerouted in different periods, ii) only change the backup DC and routes, or iii) freely change both primary and backup DC choices and the routes toward them
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