End-to-end resource management for federated delivery of multimedia services

Recently, the Internet has become a popular platform for the delivery of multimedia content. Currently, multimedia services are either offered by Over-the-top (OTT) providers or by access ISPs over a managed IP network. As OTT providers offer their content across the best-effort Internet, they cannot offer any Quality of Service (QoS) guarantees to their users. On the other hand, users of managed multimedia services are limited to the relatively small selection of content offered by their own ISP. This article presents a framework that combines the advantages of both existing approaches, by dynamically setting up federations between the stakeholders involved in the content delivery process. Specifically, the framework provides an automated mechanism to set up end-to-end federations for QoS-aware delivery of multimedia content across the Internet. QoS contracts are automatically negotiated between the content provider, its customers, and the intermediary network domains. Additionally, a federated resource reservation algorithm is presented, which allows the framework to identify the optimal set of stakeholders and resources to include within a federation. Its goal is to minimize delivery costs for the content provider, while satisfying customer QoS requirements. Moreover, the presented framework allows intermediary storage sites to be included in these federations, supporting on-the-fly deployment of content caches along the delivery paths. The algorithm was thoroughly evaluated in order to validate our approach and assess the merits of including intermediary storage sites. The results clearly show the benefits of our method, with delivery cost reductions of up to 80 % in the evaluated scenario

An improved particle swarm algorithm for multi-objectives based optimization in MPLS/GMPLS networks

Particle swarm optimization (PSO) is a swarm-based optimization technique capable of solving different categories of optimization problems. Nevertheless, PSO has a serious exploration issue that makes it a difficult choice for multi-objectives constrained optimization problems (MCOP). At the same time, Multi-Protocol Label Switched (MPLS) and its extended version Generalized MPLS, has become an emerging network technology for modern and diverse applications. Therefore, as per MPLS and Generalized MPLS MCOP needs, it is important to find the Pareto based optimal solutions that guarantee the optimal resource utilization without compromising the quality of services (QoS) within the networks. The paper proposes a novel version of PSO, which includes a modified version of the Elitist Learning Strategy (ELS) in PSO that not only solves the existing exploration problem in PSO, but also produces optimal solutions with efficient convergence rates for different MPLS/ GMPLS network scales. The proposed approach has also been applied with two objective functions; the resource provisioning and the traffic load balancing costs. Our simulations and comparative study showed improved results of the proposed algorithm over the well-known optimization algorithms such as the “standard” PSO, Adaptive PSO, BAT, and Dolphin algorithm

A CLUSTERING-BASED SELECTIVE PROBING FRAMEWORK TO SUPPORT INTERNET QUALITY OF SERVICE ROUTING

The advent of the multimedia applications has triggered widespread interest in QoS supports. Two Internet-based QoS frameworks have been proposed: Integrated Services (IntServ) and Differentiated Services (DiffServ). IntServ supports service guarantees on a per-flow basis. The framework, however, is not scalable due to the fact that routers have to maintain a large amount of state information for each supported flow. DiffServ was proposed as an alternate solution to address the lack of scalability of the IntServ framework. DiffServ uses class-based service differentiation to achieve aggregate support for QoS requirements. This approach eliminates the need to maintain per-flow states on a hop-by-hop basis and reduces considerably the overhead routers incur in forwarding traffic.Both IntServ and DiffServ frameworks focus on packet scheduling. As such, they decouple routing from QoS provisioning. This typically results in inefficient routes, thereby limiting the ability of the network to support QoS requirements and to manage resources efficiently. The goal of this thesis is to address this shortcoming. We propose a scalable QoS routing framework to identify and select paths that are very likely to meet the QoS requirements of the underlying applications. The tenet of our approach is based on seamlessly integrating routing into the DiffServ framework to extend its ability to support QoS requirements. Scalability is achieved using selective probing and clustering to reduce signaling and routers overhead.The major contributions of this thesis are as follows: First, we propose a scalable routing architecture that supports QoS requirements. The architecture seamlessly integrates the QoS traffic requirements of the underlying applications into a DiffServ framework. Second, we propose a new delay-based clustering method, referred to as d-median. The proposed clustering method groups Internet nodes into clusters, whereby nodes in the same cluster exhibit equivalent delay characteristics. Each cluster is represented by anchor node. Anchors use selective probing to estimate QoS parameters and select appropriate paths for traffic forwarding. A thorough study to evaluate the performance of the proposed d-median clustering algorithm is conducted. The results of the study show that, for power-law graphs such as the Internet, the d-median clustering based approach outperforms the set covering method commonly proposed in the literature. The study shows that the widely used clustering methods, such as set covering or k-median, are inadequate to capture the balance between cluster sizes and the number of clusters. The results of the study also show that the proposed clustering method, applied to power-law graphs, is robust to changes in size and delay distribution of the network. Finally, the results suggest that the delay bound input parameter of the d-median scheme should be no less than 1 and no more than 4 times of the average delay per one hop of the network. This is mostly due to the weak hierarchy of the Internet resulting from its power-law structure and the prevalence of the small-world property

Metodología para el diseño de microrredes aisladas usando métodos de optimización numérica

Actualmente, la mayoría de sistemas eléctricos aislados que incluyen fuentes basadas en combustibles fósiles, están siendo modificados por esquemas de generación híbridos que reducen el impacto ambiental, aprovechando los recientes avances en el área de las energías renovables. En esta tesis se propone una metodología para realizar el diseño de una microrred híbrida aislada, empleando métodos de optimización iterativos y considerando como caso de estudio el municipio de Unguía en el departamento de Chocó. El diseño busca minimizar el costo de operación de la red y las emisiones de CO2, garantizando un nivel de confiabilidad en el suministro. El diseño de la microrred considera los siguientes elementos: generadores Diesel, un banco de baterías, paneles fotovoltaicos, aerogeneradores, un sistema de almacenamiento por bombeo de agua y vehículos eléctricos. Para dar un enfoque realista se consideran variaciones horarias en la demanda eléctrica, la velocidad del viento, la temperatura ambiente y la radiación solar. Estos datos fueron reportados por entidades de medición del clima y prestadoras del servicio eléctrico en el lugar de estudio. Las variables de diseño son: el número de paneles fotovoltaicos, el número de aerogeneradores y el número de baterías para un tipo dado de cada elemento, el volumen del tanque de almacenamiento de agua, la potencia nominal de la bomba y de la turbina hidráulica. Además se calcula la potencia de carga y descarga de los sistemas de almacenamiento, la demanda no suministrada y la potencia entregada por el generador Diesel en cada hora. Se proponen dos enfoques para resolver el problema de optimización planteado: optimización basada en escenarios determinísticos y optimización basada en escenarios probabilísticos. Los resultados obtenidos con ambos métodos se comparan con los diseños calculados usando los siguientes métodos tradicionales: el software HOMER y una metodología heurística basada en algoritmos genéticos. Con los métodos propuestos se obtienen diseño con mínimo costo.Abstract: Currently, most of the isolated electrical systems which operate with fossil fuel sources are being modified by hybrid schemes that minimize environmental impact, taking advantage of recent advances in the renewable energies topic. This thesis proposes a methodology for the design of an isolated hybrid microgrid. Using iterative optimization methods and the Unguía community in Chocó department as study case, the design goal aims to minimize the cost of grid operation and CO2 emissions, ensuring a level of supply reliability. The following elements are considered in the microgrid design: Diesel generators, a battery bank, solar panels, wind turbines, a hydro pumped storage system and electric vehicles. Hourly variations are considered in the conditions of the problem such as: load demand, wind speed, environment temperature and solar radiation. These data were reported by entities that measure the climate and electric service suppliers at the study site. The resulted design establishes: the number of photovoltaic panels, the number of wind turbines and the number of batteries for a given type of each element, the volume of the water storage tank, the pump and the hydraulic turbine capacity. Moreover charge and discharge power of storage systems, the unmet load and the power delivered by the diesel generator are calculated at each time. Two methods to solve the optimization problem are proposed: Optimization based on deterministic scenarios and optimization based on probabilistic scenarios. The results obtained with both methods are compared with the designs calculated using the following traditional methods: The HOMER software and a heuristic method based on genetic algorithms. With the proposed methods a design with minimal cost is obtained.Maestrí

Grid-Connected Renewable Energy Sources

The use of renewable energy sources (RESs) is a need of global society. This editorial, and its associated Special Issue “Grid-Connected Renewable Energy Sources”, offers a compilation of some of the recent advances in the analysis of current power systems that are composed after the high penetration of distributed generation (DG) with different RESs. The focus is on both new control configurations and on novel methodologies for the optimal placement and sizing of DG. The eleven accepted papers certainly provide a good contribution to control deployments and methodologies for the allocation and sizing of DG

Innovative hybrid MOEA/AD variants for solving multi-objective combinatorial optimization problems

Orientador : Aurora Trinidad Ramirez PozoCoorientador : Roberto SantanaTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 16/12/2016Inclui referências : f. 103-116Resumo: Muitos problemas do mundo real podem ser representados como um problema de otimização combinatória. Muitas vezes, estes problemas são caracterizados pelo grande número de variáveis e pela presença de múltiplos objetivos a serem otimizados ao mesmo tempo. Muitas vezes estes problemas são difíceis de serem resolvidos de forma ótima. Suas resoluções tem sido considerada um desafio nas últimas décadas. Os algoritimos metaheurísticos visam encontrar uma aproximação aceitável do ótimo em um tempo computacional razoável. Os algoritmos metaheurísticos continuam sendo um foco de pesquisa científica, recebendo uma atenção crescente pela comunidade. Uma das têndencias neste cenário é a arbordagem híbrida, na qual diferentes métodos e conceitos são combinados objetivando propor metaheurísticas mais eficientes. Nesta tese, nós propomos algoritmos metaheurísticos híbridos para a solução de problemas combinatoriais multiobjetivo. Os principais ingredientes das nossas propostas são: (i) o algoritmo evolutivo multiobjetivo baseado em decomposição (MOEA/D framework), (ii) a otimização por colônias de formigas e (iii) e os algoritmos de estimação de distribuição. Em nossos frameworks, além dos operadores genéticos tradicionais, podemos instanciar diferentes modelos como mecanismo de reprodução dos algoritmos. Além disso, nós introduzimos alguns componentes nos frameworks objetivando balancear a convergência e a diversidade durante a busca. Nossos esforços foram direcionados para a resolução de problemas considerados difíceis na literatura. São eles: a programação quadrática binária sem restrições multiobjetivo, o problema de programação flow-shop permutacional multiobjetivo, e também os problemas caracterizados como deceptivos. Por meio de estudos experimentais, mostramos que as abordagens propostas são capazes de superar os resultados do estado-da-arte em grande parte dos casos considerados. Mostramos que as diretrizes do MOEA/D hibridizadas com outras metaheurísticas é uma estratégia promissora para a solução de problemas combinatoriais multiobjetivo. Palavras-chave: metaheuristicas, otimização multiobjetivo, problemas combinatoriais, MOEA/D, otimização por colônia de formigas, algoritmos de estimação de distribuição, programação quadrática binária sem restrições multiobjetivo, problema de programação flow-shop permutacional multiobjetivo, abordagens híbridas.Abstract: Several real-world problems can be stated as a combinatorial optimization problem. Very often, they are characterized by the large number of variables and the presence of multiple conflicting objectives to be optimized at the same time. These kind of problems are, usually, hard to be solved optimally, and their solutions have been considered a challenge for a long time. Metaheuristic algorithms aim at finding an acceptable approximation to the optimal solution in a reasonable computational time. The research on metaheuristics remains an attractive area and receives growing attention. One of the trends in this scenario are the hybrid approaches, in which different methods and concepts are combined aiming to propose more efficient approaches. In this thesis, we have proposed hybrid metaheuristic algorithms for solving multi-objective combinatorial optimization problems. Our proposals are based on (i) the multi-objective evolutionary algorithm based on decomposition (MOEA/D framework), (ii) the bio-inspired metaheuristic ant colony optimization, and (iii) the probabilistic models from the estimation of distribution algorithms. Our algorithms are considered MOEA/D variants. In our MOEA/D variants, besides the traditional genetic operators, we can instantiate different models as the variation step (reproduction). Moreover, we include some design modifications into the frameworks to control the convergence and the diversity during their search (evolution). We have addressed some important problems from the literature, e.g., the multi-objective unconstrained binary quadratic programming, the multiobjective permutation flowshop scheduling problem, and the problems characterized by deception. As a result, we show that our proposed frameworks are able to solve these problems efficiently by outperforming the state-of-the-art approaches in most of the cases considered. We show that the MOEA/D guidelines hybridized to other metaheuristic components and concepts is a powerful strategy for solving multi-objective combinatorial optimization problems. Keywords: meta-heuristics, multi-objective optimization, combinatorial problems, MOEA/D, ant colony optimization, estimation of distribution algorithms, unconstrained binary quadratic programming, permutation flowshop scheduling problem, hybrid approaches

Learning-based Predictive Control Approach for Real-time Management of Cyber-physical Systems

Cyber-physical systems (CPSs) are composed of heterogeneous, and networked hardware and software components tightly integrated with physical elements [72]. Large-scale CPSs are composed of complex components, subject to uncertainties [89], as though their design and development is a challenging task. Achieving reliability and real-time adaptation to changing environments are some of the challenges involved in large-scale CPSs development [51]. Addressing these challenges requires deep insights into control theory and machine learning. This research presents a learning-based control approach for CPSs management, considering their requirements, specifications, and constraints. Model-based control approaches, such as model predictive control (MPC), are proven to be efficient in the management of CPSs [26]. MPC is a control technique that uses a prediction model to estimate future dynamics of the system and generate an optimal control sequence over a prediction horizon. The main benefit of MPC in CPSs management comes from its ability to take the predictions of system’s environmental conditions and disturbances into account [26]. In this dissertation, centralized and distributed MPC strategies are designed for the management of CPSs. They are implemented for the thermal management of a CPS case study, smart building. The control goals are optimizing system efficiency (lower thermal power consumption in the building), and improving users’ convenience (maintaining desired indoor thermal conditions in the building). Model-based control strategies are advantageous in the management of CPSs due to their ability to provide system robustness and stability. The performance of a model-based controller strongly depends on the accuracy of the model as a representation of the system dynamics [26]. Accurate modeling of large-scale CPSs is difficult (due to the existence of unmodeled dynamics and uncertainties in the modeling process); therefore, modelbased control approach is not practical for these systems [6]. By incorporating machine learning with model-based control strategies, we can address CPS modeling challenges while preserving the advantages of model-based control methods. In this dissertation, a learning-based modeling strategy incorporated with a model-based control approach is proposed to manage energy usage and maintain thermal, visual, and olfactory performance in buildings. Neural networks (NNs) are used to learn the building’s performance criteria, occupant-related parameters, environmental conditions, and operation costs. Control inputs are generated through the model-based predictive controller and based on the learned parameters, to achieve the desired performance. In contrast to the existing building control systems presented in the literature, the proposed management system integrates current and future information of occupants (convenience, comfort, activities), building energy trends, and environment conditions (environmental temperature, humidity, and light) into the control design. This data is synthesized and evaluated in each instance of decision-making process for managing building subsystems. Thus, the controller can learn complex dynamics and adapt to the changing environment, to achieve optimal performance while satisfying problem constraints. Furthermore, while many prior studies in the filed are focused on optimizing a single aspect of buildings (such as thermal management), and little attention is given to the simultaneous management of all building objectives, our proposed management system is developed considering all buildings’ physical models, environmental conditions, comfort specifications, and occupants’ preferences, and can be applied to various building management applications. The proposed control strategy is implemented to manage indoor conditions and energy consumption in a building, simulated in EnergyPlus software. In addition, for comparison purposes, we designed and simulated a baseline controller for the building under the same conditions