The communication network context in actual systems like 5G, cloud and IoT (Internet of Things), presents an ever-increasing number users, applications services that are highly distributed with distinct heterogeneous communications requirements. Resource allocation this requires dynamic, efficient customized solutions Bandwidth Allocation Models (BAMs) alternative to support new trend. This paper proposes the BAMSDN (Bandwidth Model through Software-Defined Networking) framework dynamically...

DiffServ-aware MPLS-TE (DS-TE) allows bandwidth reservation for Traffic Classes (TCs) in MPLS-based engineered networks and, as such, improves the basic model. In DS-TE networks, per-Class quality of service guarantees are provided while being possible to achieve improved network utilization. requires use a Bandwidth Allocation Model (BAM) that establishes amount and any eventual sharing among them. This paper proposes new allocation model (AllocTC-Sharing) which higher priority traffic...

Bandwidth Allocation Models (BAMs) are used in order to define Constraints (BCs) a perclass basis for MPLS/DS-TE networks and effectively how network resources like bandwidth obtained shared by applications.The BAMs proposed (MAM -Maximum Model, RDM -Russian Dolls G-RDM -Generic AllocTC-Sharing) attempt optimize the use of on per-link with different allocation resource sharing characteristics.As such, adoption distinct and/or changes demands (network traffic profile) may result operational...

Bandwidth Allocation Models (BAMs) configure and handle resource allocation (bandwidth, LSPs, fiber, slots) in networks general (IP/MPLS/DS-TE, optical domain, other). In this paper, BAMs are considered for elastic slot targeting an improvement utilization. The paper focuses initially on proposing a BAM basic configuration parameter mapping suitable circuits. Following that, MAM, RDM ATCS applied the overall network utilization is evaluated. A set of simulation results behaviors presented as...

Bandwidth Allocation Models (BAMs) configure and handle resource allocation (bandwidth, LSPs, fiber) in networks general (IP/MPLS/DS-TE, optical domain, other). BAMs currently available for IP/MPLS/DS-TE (MAM, RDM, G-RDM AllocTC-Sharing) basically define restrictions (bandwidth) by class (traffic class, application user or other grouping criteria) allocate on demand this resource. There is a BAM policy inherent each existing model which behaves differently under distinct network state, such...

DS-TE (DiffServ-aware MPLS-TE) networks support Quality of Service (QoS) implementation for traffic classes (TCs). Bandwidth Allocation Models (BAMs) are used in order to define Constraints (BCs) a per-class basis and effectively how this resource is reserved eventually shared by applications. Existing BAM models aware enablers QoS guarantees while trying optimize the use bandwidth resources on per-link basis. This paper proposes new allocation model (AllocTC-Sharing) which has an...

Management is a complex task in today's heterogeneous and large scale networks like Cloud, IoT, vehicular MPLS networks. Likewise, researchers developers envision the use of artificial intelligence techniques to create cognitive autonomic management tools that aim better assist enhance process cycle. Bandwidth allocation models (BAMs) are resource solution for need share optimize limited resources bandwidth, fiber or optical slots flexible dynamic way. This paper proposes evaluates...

Internet of Things (IoT) application deployment requires the allocation resources such as virtual machines, storage, and network elements that must be deployed over distinct infrastructures cloud computing, Cloud (CoT), datacenters backbone networks. For massive IoT data acquisition, a gateway-based aggregation approach is commonly used featuring sensor/ actuator seamless access providing cache/ buffering preprocessing functionality. In this perspective , gateways acting producers need to...

Summary Bandwidth Allocation Models (BAMs) are resource allocation methods used for networks in general. BAMs currently applied handling resources such as bandwidth MPLS DS‐TE (LSP setup). In general, define restrictions by ‘class’ and allocate the available on demand. This is frequently necessary to manage large complex systems like routing networks. G‐BAM a new generalized BAM that, configuration, incorporates ‘behavior’ of existing (MAM, RDM, G‐RDM AllocTC‐Sharing). effect, any current...

The Bandwidth Allocation Models (MAM, RDM, G-RDM and AllocTC-Sharing) are management alternatives currently available which propose different resource (bandwidth) allocation strategies in multiservice networks.The BAM adoption by a network is typically choice configuration task executed the operations system setup static or nearly way.This paper proposes explores alternative of allowing definition on more dynamic way.In effect, one basic motivations towards fact that networks characteristics...

In an increasingly complex scenario for network management, a solution that allows configuration in more autonomous way with less intervention of the manager is expected. This paper presents evaluation similarity functions are necessary context using learning strategy finding solutions. The approach considered based on Case-Based Reasoning (CBR) and applied to where different Bandwidth Allocation Models (BAMs) behaviors used must be eventually switched looking best possible operation. this...

Artificial intelligence (AI) has been used in various areas to support system optimization and find solutions where the complexity makes it challenging use algorithmic heuristics. Case-based Reasoning (CBR) is an AI technique intensively exploited domains like management, medicine, design, construction, retail smart grid. CBR a for problem-solving captures new knowledge by using past experiences. One of main deployment challenges target modeling process. This paper presents straightforward...

Modelos de alocação banda (BAM) oferecem um mecanismo eficiente e prático para a dinâmica flexível recursos numa rede classes aplicações. Este artigo apresenta BAMSDN, uma ferramenta que, através BAMs, aloca o recurso "banda" dinamicamente MPLS do paradigma SDN protocolo OpenFlow. A solução modular baixo custo que permite controlador gerenciar, forma dinâmica, LSPs (Label Switched Paths) suas respectivas larguras nas portas saída switches OpenFlow, sem necessidade desenvolvimento inteligência...

The communication network context in actual systems like 5G, cloud and IoT (Internet of Things), presents an ever-increasing number users, applications, services that are highly distributed with distinct heterogeneous communications requirements. Resource allocation this requires dynamic, efficient, customized solutions Bandwidth Allocation Models (BAMs) alternative to support new trend. This paper proposes the BAMSDN (Bandwidth Model through Software-Defined Networking) framework...

The Bandwidth Allocation Models (BAMs) are management possibilities to bandwidth allocation in multiservice networks. This paper evaluates and explores the alternative of allowing BAM definition configuration on a more dynamic way. In effect, one basic motivations towards is fact that networks characteristics (traffic load) may change considerably daily network operation and, as such, some dynamics should be introduced order improve overall performance. A proof concept presented evaluating...

<p>Reinforcement learning (RL) is currently used in various real-life applications. RL-based solutions have the potential to generically address problems, including ones that are difficult solve with heuristics and meta-heuristics and, addition, set of problems issues where some intelligent or cognitive approach required. However, reinforcement agents require a not straightforward design important issues. RL agent include target problem modeling, state-space explosion, training...

<p>Reinforcement learning (RL) is currently used in various real-life applications. RL-based solutions have the potential to generically address problems, including ones that are difficult solve with heuristics and meta-heuristics and, addition, set of problems issues where some intelligent or cognitive approach required. However, reinforcement agents require a not straightforward design important issues. RL agent include target problem modeling, state-space explosion, training...

Reinforcement learning (RL) is currently used in various real-life applications. RL-based solutions have the potential to generically address problems, including ones that are difficult solve with heuristics and meta-heuristics and, addition, set of problems issues where someintelligent or cognitive approach required. However, reinforcement agents require a not straightforward design important issues. RL agent include target problem modeling, state-space explosion, training process,...