Optimizing load balancing routing mechanisms with evolutionary computation

Link State routing protocols, such as Open Shortest Path First (OSPF), are widely applied to intra-domain routing in todays IP networks. They provide a good scalability without lost of simplicity. A router running OSPF distributes traf- fic uniformly over Equal-cost Multi-path (ECMP), enabling a better distribution of packets among the existent links. More recently, other load balancing strategies, that consider non even splitting of traffic, have been put forward. Such is the case of the Distributed Exponentially-weighted Flow SpliTting (DEFT), that enables traf- fic to be directed through non equal-cost multi-paths, while preserving the OSPF simplicity. As the optimal link weight computation is known to be NP-hard, intel- ligence heuristics are particularly suited to address this optimization problem. In this context, this work compares the solutions provided by Evolutionary Al- gorithms (EA) for the weight setting problem, considering both ECMP and DEFT load balancing alternatives. In addition to a single objective network congestion optimization problem, both load balancing schemes are also applied to a multi- objective optimization approach able to attain routing configurations resilient to traffic demand variations.COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e TecnologiaThis work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT -Fundação para a Ciência e Tecnologia within the ProjectScope: UID/CEC/00319/2013

Hybrid IP/SDN routing for inter-data center communications

Internet Service Providers (ISPs) and dedicated inter-Data Center Wide Area Networks have been exploring Software-Defined Networking (SDN) features to achieve a high utilization of the available resources. This work proposes a scalable hybrid IP/SDN routing model, and optimization procedures fostered by Evolutionary Computation algorithms, to achieve near optimal network resources utilization under changing traffic requirements.This work has been supported by national funds through FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019.info:eu-repo/semantics/publishedVersio

A framework for improving routing configurations using multi-objective optimization mechanisms

IP networks are nowadays well established technolo- gies being used to support a myriad of applications and services, thus assuming a crucial role in todays telecommunication sys- tems. Nevertheless, such infrastructures usually require network administrators to perform a wide set of complex planning and management tasks trying to attain adequate network configura- tions. Many of such management tasks can be mathematically for- mulated as NP-hard optimization problems, sometimes involving several objective functions. In this context, this work explores and demonstrates the potential of using computational intelligence methods as optimization engines to tackle complex network op- timization problems. In particular, Multi-objective Evolutionary Algorithms (MOEAs) are used to attain near-optimal link state routing configurations robust to distinct operational conditions. As result, network administrators will be provided with a set of alternative routing configurations representing distinct tradeoffs between the considered optimization goals. The robustness of the proposed methods is illustrated by presenting several multi-objective optimization examples able to improve the performance and resilience levels of a network infrastructure. Moreover, the devised methods are integrated in a freely available Traffic Engineering optimization framework able to be used by network administrators interested in this particular research field.This work has been supported by COMPETE: POCI-010145-FEDER-007043 and FCT Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013

The impact and consequences of deepfakes in cyberspace on the social environment

We live in an era daily inundated with information, and the economy of attention makes us far from the truth. The present study has its core to study the creation, use and sharing of videos originated by artificial intelligence that can make it appear that a person says or does something, although he has never said or done anything of the kind. This content is called deepfake. The problem is the way this content is propagated, which for the untrained eye it can be seen as authentic. Quantitative research was carried out, through an inquiry and a literature review.info:eu-repo/semantics/publishedVersio

Study of Deepfakes in Cyberspace, Impact, and Consequences on the Social Environment

We live in era in which we are inundated with information from both hemispheres, and in which the economy of attention makes us far from the truth. The present study has as its core to study the creation, use, as well as the sharing of videos originated by artificial intelligence that can make it appear that a person says or does something, although he has never said or done anything of the kind, this type of content is called deepfakes. The problem is the way in which these contents are propagated, which for the untrained eye, can be seem as authentic. To prepare the article, we opted for quantitative research, through a survey, and a literature review in books, articles and reports. It was found through the obtained data that many users are linked to the desire to verify the veracity and source of information circulating in digital networks

Intradomain routing optimization based on evolutionary computation

Tese de Doutoramento em InformáticaThe growing number of Internet-connected devices and the escalation of new Internet services, such as cloud services and video streaming, are some examples of factors that increase the volume and mutability of traffic in communication networks. The need to channel increasingly large volumes of traffic in network infrastructures with limited capacity, highlights the importance of Traffic Engineering mechanisms that aim to deliver an efficient use of network resources. Routing decisions play a key role as they de ne how traffic is distributed on the available paths and hence how networks resources are explored. Traditional routing protocols, such as Open Shortest Path First and Intermediate System to Intermediate System, have constraints which prevent an optimal network resources utilization. Some of these constraints are, for example, their inability to perform uneven splitting of traffic across multiple paths and their lack of a centralized control. These constraints impose additional difficulties when changes in network operating conditions need to be considered, such as significant variations intra c necessities and link failures. When facing such changes, routing configurations need to adapt to the new conditions and ensure that the network continues to operate efficiently. Communication technologies are constantly evolving. Recently, alternative routing solutions have emerged that enable new Traffic Engineering approaches. Network management problems and, in particular, the optimization of network resources utilization can be addressed using such alternative solutions. Software-De ned Networking and Segment Routing paradigms provide greater exibility in the selection of routing paths, and although they can overcome many of the constraints of traditional routing protocols, it is necessary to find configurations, both scalable and manageable in real contexts, that optimize the distribution of available resources. In this context, this research work intends to respond to the stated problems by proposing efficient mechanisms for optimizing the use of resources in networks configured with traditional Link State routing protocols, as well as in networks that implement the latest paradigms of Software De ned Networking and Segment Routing. In addition to enabling efficient resource utilization, the proposed optimization mechanisms are responsive to relevant changes in the network environment that may result from variations in traffic requirements or topology changes such as link failures. The nature of the problems, which besides not being solvable in polynomial time include more than one optimization objective in their formulation, are addressed using algorithms fostered in the field of Evolutionary Computation. Distinct traffic requirements and different network states frequently require clashing configurations. Evolutionary Algorithms possess several characteristics that are desirable to solve problems with multiple conflicting goals and make them preferable to classical optimization methods. They provide a set of compromise solutions to problems for which there is no single optimal solution. The research work winded up in an autonomous optimization framework that integrates all the proposed Traffic Engineering methods, which is made publicly available to be freely used by researchers and network administrators.O surgimento e a prolifera cão de novos serviços de Internet, como os servi cos de cloud e a transmissão de vídeo, bem como o aumento do número de dispositivos que se ligam diariamente a Internet, são alguns fatores que contribuem para um avolumar e uma mudança nos padrões do trafego em redes de comunica cão. A necessidade de canalizar grandes volumes de trafego em infraestruturas de rede com capacidade limitada, enfatiza a importância da Engenharia de Trafego que tem por objetivo proporcionar um uso e ciente dos recursos de rede. As decisões de encaminhamento desempenham um papel essencial neste contexto, pois de nem como o trafego e distribuído pelos caminhos disponíveis e, consequentemente, como os recursos de rede são utilizados. Os protocolos de encaminhamento tradicionais, como o Open Shortest Path First e o Intermediate System to Intermediate System, possuem restrições operacionais que impedem uma utilização ótima dos recursos. Algumas dessas restrições são, por exemplo, as opções limitadas de balanceamento de carga e a falta de uma gestão centralizada. Essas restrições impõem dificuldades acrescidas quando alterações nas condições de funcionamento da rede têm de ser contempladas como, por exemplo, variações significativas do volume e trafego e falhas de ligações f sicas. As configurações de encaminhamento precisam adaptar-se as novas condições operacionais e garantir que a rede continue a operar de forma e ciente. As tecnologias de comunicação evoluem. Recentemente foram propostas soluções alternativas de encaminhamento de trafego que permitem novas abordagens de Engenharia de Trafego. Os problemas de gestão de redes e, em particular, a otimização da utilização de recursos podem ser abordados com recurso a essas novas soluções. Os paradigmas de Software Defined Networking e Segment Routing proporcionam uma maior exibilidade na seleção de caminhos de trafego, e embora sejam capazes de superar muitas das restrições dos protocolos de encaminhamento tradicionais, e necessário encontrar configurações, escaláveis e geríveis em contexto real, que otimizem a distribuição de trafego nos recursos disponíveis. Neste contexto, este trabalho de investigação procura responder aos problemas enunciados, propondo mecanismos e cientes para a otimização da utilização de recursos de redes configuradas com protocolos de encaminhamento Link State tradicionais, bem como em redes que implementam os mais recentes paradigmas de Software Defined Networking e Segment Routing. Para al em de possibilitarem uma utilização e ciente dos recursos, os mecanismos de otimização propostos são responsivos a altera coes relevantes no ambiente de rede que podem resultar de variações nos requisitos de trafego ou alterações de topologia. A natureza dos problemas, que para alem de não serem resolúveis em tempo polinomial incluem na sua formulação mais do que um objetivo de otimização, são abordados recorrendo a algoritmos da área da Computação Evolucionaria. Distintos requisitos de trafego e diferentes estados de rede exigem frequentemente configurações conflituantes. Os Algoritmos Evolucionários possuem varias características que são desejáveis para resolver problemas com múltiplos objetivos e que os torna preferíveis a métodos clássicos de otimização. Eles fornecem um conjunto de soluções de compromisso em problemas para os quais não existe uma solução ótima única. O trabalho de investigação concluiu numa ferramenta de otimização que integra todos os métodos de engenharia de trafego propostos, e que e disponibilizada para ser usada livremente por investigadores e administradores de rede

Automated network resilience optimization using computational intelligence methods

This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational intelligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two illustrative Traffic Engineering methods are described, allowing to attain routing configurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.This work has been partially supported by FCT - Fundação para a Ciência e Tecnologia Portugal in the scope of the project: UID/CEC/00319/2013

A comparison of multi-objective optimization algorithms for weight setting problems in traffic engineering

The source code of the optimization framework and binaries are publicly available at: http://bio.di.uminho. pt/netopt/.Traffic engineering approaches are increasingly important in network management to allow an optimized configuration and resource allocation. In link-state routing, setting appropriate weights to the links is an important and challenging optimization task. Different approaches have been put forward towards this aim, including evolutionary algorithms (EAs). This work addresses the evaluation of a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to minimize network congestion. In both tasks, the optimization considers scenarios where there is a dynamic alteration in the network, with (1) changes in the traffic demand matrices, and (2) link failures. The methods will simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach. Since this leads to a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came naturally; those are compared to a single-objective EA previously proposed by the authors. The results show a remarkable performance and scalability of NSGA-II in the proposed tasks presenting itself as the most promising option for TE.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope UIDB/00319/2020 and UIDB/04469/2020 units

Traffic engineering with three-segments routing

IEEE Segment Routing (SR) is a new fertile ground for Traffic Engineering (TE). By decomposing forwarding paths into segments, which specify a list of intermediate delivery points that a packet must visit on its way to the final destination, SR improves TE tasks and enables new solutions for the optimization of network resource utilization. This work proposes an Evolutionary Computation approach that enables Path Computation Element (PCE), or Software-defined Network (SDN) controllers, to optimize SR configurations for improved traffic distribution. Furthermore, we present a robust semi-oblivious method to address the variability of traffic requirements as well as alternative approaches to ensure a good network performance after link failures. In all cases, the optimization of network resource utilization is achieved using at the most three segments to configure each SR path. Moreover, all proposed optimization methods are made publicly available in a optimization framework developed by the authors.This work has been supported by FCT – Fundac ̧ ̃ao para aCiˆencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Robust optimization of intradomain routing using evolutionary algorithms

Open Shortest Path First (OSPF) is a widely used routing protocol that depends on weights assigned to each link to make routing decisions. If traffic demands are known, the OSPF weight setting (OSPFWS) problem can be defined to seek a set of weights that optimize network performance, typically by minimizing a congestion measure. The OSPFWS problem is NP-hard and, thus, meta-heuristics such as Evolutionary Algorithms (EAs) have been used in previous work to obtain near optimal solutions. However, the dynamic nature of this problem leads to the necessity of addressing these problems in a more robust manner that can deal with changes in the conditions of the network. Here, we present EAs for two of those tasks, defining objective functions that take into account, on the one hand, changes in the traffic demand matrices and, on the other, single link failures. Those functions use weighting schemes to provide trade-offs between the behaviour of the network in distinct conditions, thus providing robust sets of OSPF weights.The algorithms are implemented in the open-source software NetOpt framework.(undefined