Towards Smart City Innovation under the Perspective of Software-Defined Networking, Artificial Intelligence and Big Data

Towards Smart City Innovation under the Perspective of Software-Defined Networking, Artificial Intelligence and Big Data: Smart city projects address many of the current problems afflicting high populated areas and cities and, as such, are a target for government, institutions and private organizations that plan to explore its foreseen advantages. In technical terms, smart city projects present a complex set of requirements including a large number users with highly different and heterogeneous requirements. In this scenario, this paper proposes and analyses the impact and perspectives on adopting software-defined networking and artificial intelligence as innovative approaches for smart city project development and deployment. Big data is also considered as an inherent element of most smart city project that must be tackled. A framework  layered view is proposed with a discussion about software-defined networking and machine learning impacts on innovation followed by a use case that demonstrates the potential benefits of cognitive learning for smart cities. It is argued that the complexity of smart city projects do require new innovative approaches that potentially result in more efficient and intelligent systems.

A Prevalência do Modelo de Critérios Construídos com Stackholders para a Avaliação do Alinhamento Estratégico entre os Processos de Negócio e de Tecnologia da Informação

A Prevalência do Modelo de Critérios Construídos com Stackholders para a Avaliação do Alinhamento Estratégico entre os Processos de Negócio e de Tecnologia da Informação: O estudo analisou o modelo de alinhamento estratégico entre os processos de negócios e os processos de tecnologia de informação na EBAL – Empresa Baiana de Alimentos à luz do modelo de alinhamento estratégico de Henderson e Venkatraman (1993). Para mensuração do nível de alinhamento da TI com o Negócio, considerou os modelos de Galas e Ponte (2005), Luftman, Papp e Brier (2002), além do modelo construído, que leva em consideração a perspectiva dos gestores da EBAL, promovendo a confrontação entre estes modelos. A estratégia documental foi adotada para análise do modelo de alinhamento, enquanto que a mensuração do seu nível foi operacionalizada com o uso de questionários ponderados, todos validados através de triangulação com grupo focal. O estudo demonstrou que o modelo delimitado apresentou um bom nível de alinhamento (3.21 e 3.60, respectivamente, numa escala de 5 pontos) para as métricas sugeridas pela literatura. Contudo, na métrica construída pelo estudo, o nível de alinhamento teve uma qualificação apenas regular (2.51 pontos), sugerindo a prevalência de modelos com ponderação interna para avaliação do fenômeno.

Cognitive Management of Bandwidth Allocation Models with Case-Based Reasoning - Evidences Towards Dynamic BAM Reconfiguration - ILLUSTRATED TECHNICAL PAPER

Cognitive Management of Bandwidth Allocation Models with Case-Based Reasoning - Evidences Towards Dynamic BAM Reconfiguration - ILLUSTRATED TECHNICAL PAPER

OpenAIRE - Publication: RePAF Project - Dynamic and Co...

OpenAIRE - Publication: RePAF Project - Dynamic and Co...: {PAGE_DESCRIPTION}

Innovation in Future Internet Scenario with Network Programmability and Smart Systems

Innovation in Future Internet Scenario with Network Programmability and Smart Systems: This 'presentation paper' discusses the innovation scenario with Software-defined Networking (SDN)/ OpenFlow for smart systems. A 'presentation paper' format is used with slides and bibliographic references to subsidize and enrich contents lecture. This talk was presented at Campus Party - BA - Brazil 2017.

Ambiente de Gerência Autônoma de Redes para a Qualidade de Serviço

Ambiente de Gerência Autônoma de Redes para a Qualidade de Serviço: The need to keep assured quality of service levels in complex networks is a challenging process in network management area. In such a scenario, it becomes important to identify and specify a network management structure focusing on autonomous network administration that, by definition, will reduce human intervention in the process and will promote automatic network operations and management. The Autonomous Network Management Framework (AGA), proposed in this paper, supports autonomous management. Its implementation articulates network management planes and uses genetic algorithms in order to identify heuristics to provide solutions to network utilization, routing and packet scheduling focusing on autonomous management.

Avaliação Experimental da Utilização da Estratégia de Redes Ativas para o Suporte da Qualidade de Serviço no Contexto de Aplicações Multimídia

Avaliação Experimental da Utilização da Estratégia de Redes Ativas para o Suporte da Qualidade de Serviço no Contexto de Aplicações Multimídia: The active networking paradigm corresponds to an initiative including projects, developments and implementations searching for a new communication concept/ paradigm allowing fast, safe and efficient communications. The basic target is to provide, among other features and services, the management and configuration of new and complex network services and equipment (backbones), supporting dynamically the quality of service (QoS) needs of the new multimedia applications, so often pointed as the trend in the web world. This paper evaluates experimentally and objectively the usability of a specific active networking implementation architecture (ANTS) focusing on the dynamic configuration of backbone routers for supporting QoS in a multimedia applications scenario. The paper describes the evaluated architecture, proposes two implementation scenarios and evaluates experimentally their use for dynamic routers configuration.

Considerations about a Methodological Approach for Multi-service and Multi-priority Traffic Parameters Optimization

Considerations about a Methodological Approach for Multi-service and Multi-priority Traffic Parameters Optimization: This paper proposes a simple methodological approach to represent quality of service parameters in multi-service and multi-priority networks and discusses the identification of methods and dependencies to handle network parameters targeting an optimization method. Both the approach to represent network parameters and the optimization methods are part of a more general framework intended to optimize resource allocation in DiffServ domains.

A Publish/Subscribe QoS-aware Framework for Massive IoT Traffic Orchestration

A Publish/Subscribe QoS-aware Framework for Massive IoT Traffic Orchestration: Internet of Things (IoT) application deployment requires the allocation of resources such as virtual machines, storage, and network elements that must be deployed over distinct infrastructures such as cloud computing, Cloud of Things (CoT), datacenters and backbone networks. For massive IoT data acquisition, a gateway-based data aggregation approach is commonly used featuring sensor/ actuator seamless access and providing cache/ buffering and preprocessing functionality. In this perspective, gateways acting as producers need to allocate network resources to send IoT data to consumers. In this paper, it is proposed a Publish/Subscribe (PubSub) quality of service (QoS) aware framework (PSIoT-Orch) that orchestrates IoT traffic and allocates network resources between aggregates and consumers for massive IoT traffic. PSIoT-Orch schedules IoT data flows based on its configured QoS requirements. Additionally,  the framework allocates network resources (LSP/ bandwidth) over a controlled backbone network with limited and constrained resources between IoT data users and consumers. Network resources are allocated using a Bandwidth Allocation Model (BAM) to achieve efficient network resource allocation for scheduled IoT data streams. PSIoT-Orch adopts an ICN (Information-Centric Network) PubSub architecture approach to handle IoT data transfers requests among framework components. The proposed framework aims at gathering the inherent advantages of an ICN-centric approach using a PubSub message scheme while allocating resources efficiently keeping QoS awareness and handling restricted network resources (bandwidth) for massive IoT traffic.