Network coding via evolutionary algorithms

Network coding (NC) is a relatively recent novel technique that generalises network operation beyond traditional store-and-forward routing, allowing intermediate nodes to combine independent data streams linearly. The rapid integration of bandwidth-hungry applications such as video conferencing and HDTV means that NC is a decisive future network technology. NC is gaining popularity since it offers significant benefits, such as throughput gain, robustness, adaptability and resilience. However, it does this at a potential complexity cost in terms of both operational complexity and set-up complexity. This is particularly true of network code construction. Most NC problems related to these complexities are classified as non deterministic polynomial hard (NP-hard) and an evolutionary approach is essential to solve them in polynomial time. This research concentrates on the multicast scenario, particularly: (a) network code construction with optimum network and coding resources; (b) optimising network coding resources; (c) optimising network security with a cost criterion (to combat the unintentionally introduced Byzantine modification security issue). The proposed solution identifies minimal configurations for the source to deliver its multicast traffic whilst allowing intermediate nodes only to perform forwarding and coding. In the method, a preliminary process first provides unevaluated individuals to a search space that it creates using two generic algorithms (augmenting path and linear disjoint path. An initial population is then formed by randomly picking individuals in the search space. Finally, the Multi-objective Genetic algorithm (MOGA) and Vector evaluated Genetic algorithm (VEGA) approaches search the population to identify minimal configurations. Genetic operators (crossover, mutation) contribute to include optimum features (e.g. lower cost, lower coding resources) into feasible minimal configurations. A fitness assignment and individual evaluation process is performed to identify the feasible minimal configurations. Simulations performed on randomly generated acyclic networks are used to quantify the performance of MOGA and VEGA

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

GMPLS-OBS interoperability and routing acalability in internet

The popularization of Internet has turned the telecom world upside down over the last two decades. Network operators, vendors and service providers are being challenged to adapt themselves to Internet requirements in a way to properly serve the huge number of demanding users (residential and business). The Internet (data-oriented network) is supported by an IP packet-switched architecture on top of a circuit-switched, optical-based architecture (voice-oriented network), which results in a complex and rather costly infrastructure to the transport of IP traffic (the dominant traffic nowadays). In such a way, a simple and IP-adapted network architecture is desired. From the transport network perspective, both Generalized Multi-Protocol Label Switching (GMPLS) and Optical Burst Switching (OBS) technologies are part of the set of solutions to progress towards an IP-over-WDM architecture, providing intelligence in the control and management of resources (i.e. GMPLS) as well as a good network resource access and usage (i.e. OBS). The GMPLS framework is the key enabler to orchestrate a unified optical network control and thus reduce network operational expenses (OPEX), while increasing operator's revenues. Simultaneously, the OBS technology is one of the well positioned switching technologies to realize the envisioned IP-over-WDM network architecture, leveraging on the statistical multiplexing of data plane resources to enable sub-wavelength in optical networks. Despite of the GMPLS principle of unified control, little effort has been put on extending it to incorporate the OBS technology and many open questions still remain. From the IP network perspective, the Internet is facing scalability issues as enormous quantities of service instances and devices must be managed. Nowadays, it is believed that the current Internet features and mechanisms cannot cope with the size and dynamics of the Future Internet. Compact Routing is one of the main breakthrough paradigms on the design of a routing system scalable with the Future Internet requirements. It intends to address the fundamental limits of current stretch-1 shortest-path routing in terms of RT scalability (aiming at sub-linear growth). Although "static" compact routing works fine, scaling logarithmically on the number of nodes even in scale-free graphs such as Internet, it does not handle dynamic graphs. Moreover, as multimedia content/services proliferate, the multicast is again under the spotlight as bandwidth efficiency and low RT sizes are desired. However, it makes the problem even worse as more routing entries should be maintained. In a nutshell, the main objective of this thesis in to contribute with fully detailed solutions dealing both with i) GMPLS-OBS control interoperability (Part I), fostering unified control over multiple switching domains and reduce redundancy in IP transport. The proposed solution overcomes every interoperability technology-specific issue as well as it offers (absolute) QoS guarantees overcoming OBS performance issues by making use of the GMPLS traffic-engineering (TE) features. Keys extensions to the GMPLS protocol standards are equally approached; and ii) new compact routing scheme for multicast scenarios, in order to overcome the Future Internet inter-domain routing system scalability problem (Part II). In such a way, the first known name-independent (i.e. topology unaware) compact multicast routing algorithm is proposed. On the other hand, the AnyTraffic Labeled concept is also introduced saving on forwarding entries by sharing a single forwarding entry to unicast and multicast traffic type. Exhaustive simulation campaigns are run in both cases in order to assess the reliability and feasible of the proposals

Resource Allocation in SDN/NFV-Enabled Core Networks

For next generation core networks, it is anticipated to integrate communication, storage and computing resources into one unified, programmable and flexible infrastructure. Software-defined networking (SDN) and network function virtualization (NFV) become two enablers. SDN decouples the network control and forwarding functions, which facilitates network management and enables network programmability. NFV allows the network functions to be virtualized and placed on high capacity servers located anywhere in the network, not only on dedicated devices in current networks. Driven by SDN and NFV platforms, the future network architecture is expected to feature centralized network management, virtualized function chaining, reduced capital and operational costs, and enhanced service quality. The combination of SDN and NFV provides a potential technical route to promote the future communication networks. It is imperative to efficiently manage, allocate and optimize the heterogeneous resources, including computing, storage, and communication resources, to the customized services to achieve better quality-of-service (QoS) provisioning. This thesis makes some in-depth researches on efficient resource allocation for SDN/NFV-enabled core networks in multiple aspects and dimensionality. Typically, the resource allocation task is implemented in three aspects. Given the traffic metrics, QoS requirements, and resource constraints of the substrate network, we first need to compose a virtual network function (VNF) chain to form a virtual network (VN) topology. Then, virtual resources allocated to each VNF or virtual link need to be optimized in order to minimize the provisioning cost while satisfying the QoS requirements. Next, we need to embed the virtual network (i.e., VNF chain) onto the substrate network, in which we need to assign the physical resources in an economical way to meet the resource demands of VNFs and links. This involves determining the locations of NFV nodes to host the VNFs and the routing from source to destination. Finally, we need to schedule the VNFs for multiple services to minimize the service completion time and maximize the network performance. In this thesis, we study resource allocation in SDN/NFV-enabled core networks from the aforementioned three aspects. First, we jointly study how to design the topology of a VN and embed the resultant VN onto a substrate network with the objective of minimizing the embedding cost while satisfying the QoS requirements. In VN topology design, optimizing the resource requirement for each virtual node and link is necessary. Without topology optimization, the resources assigned to the virtual network may be insufficient or redundant, leading to degraded service quality or increased embedding cost. The joint problem is formulated as a Mixed Integer Nonlinear Programming (MINLP), where queueing theory is utilized as the methodology to analyze the network delay and help to define the optimal set of physical resource requirements at network elements. Two algorithms are proposed to obtain the optimal/near-optimal solutions of the MINLP model. Second, we address the multi-SFC embedding problem by a game theoretical approach, considering the heterogeneity of NFV nodes, the effect of processing-resource sharing among various VNFs, and the capacity constraints of NFV nodes. In the proposed resource constrained multi-SFC embedding game (RC-MSEG), each SFC is treated as a player whose objective is to minimize the overall latency experienced by the supported service flow, while satisfying the capacity constraints of all its NFV nodes. Due to processing-resource sharing, additional delay is incurred and integrated into the overall latency for each SFC. The capacity constraints of NFV nodes are considered by adding a penalty term into the cost function of each player, and are guaranteed by a prioritized admission control mechanism. We first prove that the proposed game RC-MSEG is an exact potential game admitting at least one pure Nash Equilibrium (NE) and has the finite improvement property (FIP). Then, we design two iterative algorithms, namely, the best response (BR) algorithm with fast convergence and the spatial adaptive play (SAP) algorithm with great potential to obtain the best NE of the proposed game. Third, the VNF scheduling problem is investigated to minimize the makespan (i.e., overall completion time) of all services, while satisfying their different end-to-end (E2E) delay requirements. The problem is formulated as a mixed integer linear program (MILP) which is NP-hard with exponentially increasing computational complexity as the network size expands. To solve the MILP with high efficiency and accuracy, the original problem is reformulated as a Markov decision process (MDP) problem with variable action set. Then, a reinforcement learning (RL) algorithm is developed to learn the best scheduling policy by continuously interacting with the network environment. The proposed learning algorithm determines the variable action set at each decision-making state and accommodates different execution time of the actions. The reward function in the proposed algorithm is carefully designed to realize delay-aware VNF scheduling. To sum up, it is of great importance to integrate SDN and NFV in the same network to accelerate the evolution toward software-enabled network services. We have studied VN topology design, multi-VNF chain embedding, and delay-aware VNF scheduling to achieve efficient resource allocation in different dimensions. The proposed approaches pave the way for exploiting network slicing to improve resource utilization and facilitate QoS-guaranteed service provisioning in SDN/NFV-enabled networks

Lagrangian-based methods for single and multi-layer multicommodity capacitated network design

Le problème de conception de réseau avec coûts fixes et capacités (MCFND) et le problème de conception de réseau multicouches (MLND) sont parmi les problèmes de conception de réseau les plus importants. Dans le problème MCFND monocouche, plusieurs produits doivent être acheminés entre des paires origine-destination différentes d’un réseau potentiel donné. Des liaisons doivent être ouvertes pour acheminer les produits, chaque liaison ayant une capacité donnée. Le problème est de trouver la conception du réseau à coût minimum de sorte que les demandes soient satisfaites et que les capacités soient respectées. Dans le problème MLND, il existe plusieurs réseaux potentiels, chacun correspondant à une couche donnée. Dans chaque couche, les demandes pour un ensemble de produits doivent être satisfaites. Pour ouvrir un lien dans une couche particulière, une chaîne de liens de support dans une autre couche doit être ouverte. Nous abordons le problème de conception de réseau multiproduits multicouches à flot unique avec coûts fixes et capacités (MSMCFND), où les produits doivent être acheminés uniquement dans l’une des couches. Les algorithmes basés sur la relaxation lagrangienne sont l’une des méthodes de résolution les plus efficaces pour résoudre les problèmes de conception de réseau. Nous présentons de nouvelles relaxations à base de noeuds, où le sous-problème résultant se décompose par noeud. Nous montrons que la décomposition lagrangienne améliore significativement les limites des relaxations traditionnelles. Les problèmes de conception du réseau ont été étudiés dans la littérature. Cependant, ces dernières années, des applications intéressantes des problèmes MLND sont apparues, qui ne sont pas couvertes dans ces études. Nous présentons un examen des problèmes de MLND et proposons une formulation générale pour le MLND. Nous proposons également une formulation générale et une méthodologie de relaxation lagrangienne efficace pour le problème MMCFND. La méthode est compétitive avec un logiciel commercial de programmation en nombres entiers, et donne généralement de meilleurs résultats.The multicommodity capacitated fixed-charge network design problem (MCFND) and the multilayer network design problem (MLND) are among the most important network design problems. In the single-layer MCFND problem, several commodities have to be routed between different origin-destination pairs of a given potential network. Appropriate capacitated links have to be opened to route the commodities. The problem is to find the minimum cost design and routing such that the demands are satisfied and the capacities are respected. In the MLND, there are several potential networks, each at a given layer. In each network, the flow requirements for a set of commodities must be satisfied. However, the selection of the links is interdependent. To open a link in a particular layer, a chain of supporting links in another layer has to be opened. We address the multilayer single flow-type multicommodity capacitated fixed-charge network design problem (MSMCFND), where commodities are routed only in one of the layers. Lagrangian-based algorithms are one of the most effective solution methods to solve network design problems. The traditional Lagrangian relaxations for the MCFND problem are the flow and knapsack relaxations, where the resulting Lagrangian subproblems decompose by commodity and by arc, respectively. We present new node-based relaxations, where the resulting subproblem decomposes by node. We show that the Lagrangian dual bound improves significantly upon the bounds of the traditional relaxations. We also propose a Lagrangian-based algorithm to obtain upper bounds. Network design problems have been the object of extensive literature reviews. However, in recent years, interesting applications of multilayer problems have appeared that are not covered in these surveys. We present a review of multilayer problems and propose a general formulation for the MLND. We also propose a general formulation and an efficient Lagrangian-based solution methodology for the MMCFND problem. The method is competitive with (and often significantly better than) a state-of-the-art mixedinteger programming solver on a large set of randomly generated instances

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

Resource allocation technique for powerline network using a modified shuffled frog-leaping algorithm

Resource allocation (RA) techniques should be made efficient and optimized in order to enhance the QoS (power & bit, capacity, scalability) of high-speed networking data applications. This research attempts to further increase the efficiency towards near-optimal performance. RA’s problem involves assignment of subcarriers, power and bit amounts for each user efficiently. Several studies conducted by the Federal Communication Commission have proven that conventional RA approaches are becoming insufficient for rapid demand in networking resulted in spectrum underutilization, low capacity and convergence, also low performance of bit error rate, delay of channel feedback, weak scalability as well as computational complexity make real-time solutions intractable. Mainly due to sophisticated, restrictive constraints, multi-objectives, unfairness, channel noise, also unrealistic when assume perfect channel state is available. The main goal of this work is to develop a conceptual framework and mathematical model for resource allocation using Shuffled Frog-Leap Algorithm (SFLA). Thus, a modified SFLA is introduced and integrated in Orthogonal Frequency Division Multiplexing (OFDM) system. Then SFLA generated random population of solutions (power, bit), the fitness of each solution is calculated and improved for each subcarrier and user. The solution is numerically validated and verified by simulation-based powerline channel. The system performance was compared to similar research works in terms of the system’s capacity, scalability, allocated rate/power, and convergence. The resources allocated are constantly optimized and the capacity obtained is constantly higher as compared to Root-finding, Linear, and Hybrid evolutionary algorithms. The proposed algorithm managed to offer fastest convergence given that the number of iterations required to get to the 0.001% error of the global optimum is 75 compared to 92 in the conventional techniques. Finally, joint allocation models for selection of optima resource values are introduced; adaptive power and bit allocators in OFDM system-based Powerline and using modified SFLA-based TLBO and PSO are propose