An adaptive scaling mechanism for managing performance variations in network functions virtualization: A case study in an NFV-based EPC

The scaling is a fundamental task that allows addressing performance variations in Network Functions Virtualization (NFV). In the literature, several approaches propose scaling mechanisms that differ in the utilized technique (e.g., reactive, predictive and machine learning-based). The scaling in NFV must be accurate both at the time and the number of instances to be scaled, aiming at avoiding unnecessary procedures of provisioning and releasing of resources; however, achieving a high accuracy is a non-trivial task. In this paper, we propose for NFV an adaptive scaling mechanism based on Q-Learning and Gaussian Processes that are utilized by an agent to carry out an improvement strategy of a scaling policy, and therefore, to make better decisions for managing performance variations. We evaluate our mechanism by simulations, in a case study in a virtualized Evolved Packet Core, corroborating that it is more accurate than approaches based on static threshold rules and Q-Learning without a policy improvement strategy

Towards automated composition of convergent services: A survey

A convergent service is defined as a service that exploits the convergence of communication networks and at the same time takes advantage of features of the Web. Nowadays, building up a convergent service is not trivial, because although there are significant approaches that aim to automate the service composition at different levels in the Web and Telecom domains, selecting the most appropriate approach for specific case studies is complex due to the big amount of involved information and the lack of technical considerations. Thus, in this paper, we identify the relevant phases for convergent service composition and explore the existing approaches and their associated technologies for automating each phase. For each technology, the maturity and results are analysed, as well as the elements that must be considered prior to their application in real scenarios. Furthermore, we provide research directions related to the convergent service composition phases

An effective approach for network management based on situation management and mashups

The Situation Management discipline is intended to address situations happening or that might happen in dynamic systems. In this way, this discipline supports the provisioning of solutions that enable analyzing, correlating, and coordinating interactions among people, information, technologies, and actions targeted to overcome situations. Over recent years, the Situation Management has been employed in diverse domains ranging from disaster response to public health. Notwithstanding, up to now, it has not been used to deal with unexpected, dynamic, and heterogeneous situations that network administrators face in their daily work; in this thesis, these situations are referred to as network management situations. The mashup technology also allows creating solutions, named mashups, aimed to cope with situations. Mashups are composite Web applications built up by end-users through the combination of Web resources available along the Internet. These composite Web applications have been useful to manage situations in several domains ranging from telecommunication services to water floods. In particular, in the network management domain, the mashup technology has been used to accomplish specific tasks, such as botnet detection and the visualization of traffic of the border gateway protocol. In the network management domain, large research efforts have been made to automate and facilitate the management tasks. However, so far, none of these efforts has carried out network management by means of the Situation Management and the mashup technology. Thus, the goal of this thesis is to investigate the feasibility on using the Situation Management and mashups as an effective (in terms of complexity, consuming of time, traffic, and time of response) approach for network management. To achieve the raised goal, this thesis introduces an approach formed by mashments (special mashups devised for coping with network management situations), the Mashment Ecosystem, the process to develop and launch mashments, the Mashment System Architecture, and the Mashment Maker. An extensive analysis of the approach was conducted on networks based on the Software-Defined Networking paradigm and virtual nodes. The results of analysis have provided directions and evidences that corroborate the feasibility of using the Situation Management and mashups as an effective approach for network management

An effective approach for network management based on situation management and mashups

The Situation Management discipline is intended to address situations happening or that might happen in dynamic systems. In this way, this discipline supports the provisioning of solutions that enable analyzing, correlating, and coordinating interactions among people, information, technologies, and actions targeted to overcome situations. Over recent years, the Situation Management has been employed in diverse domains ranging from disaster response to public health. Notwithstanding, up to now, it has not been used to deal with unexpected, dynamic, and heterogeneous situations that network administrators face in their daily work; in this thesis, these situations are referred to as network management situations. The mashup technology also allows creating solutions, named mashups, aimed to cope with situations. Mashups are composite Web applications built up by end-users through the combination of Web resources available along the Internet. These composite Web applications have been useful to manage situations in several domains ranging from telecommunication services to water floods. In particular, in the network management domain, the mashup technology has been used to accomplish specific tasks, such as botnet detection and the visualization of traffic of the border gateway protocol. In the network management domain, large research efforts have been made to automate and facilitate the management tasks. However, so far, none of these efforts has carried out network management by means of the Situation Management and the mashup technology. Thus, the goal of this thesis is to investigate the feasibility on using the Situation Management and mashups as an effective (in terms of complexity, consuming of time, traffic, and time of response) approach for network management. To achieve the raised goal, this thesis introduces an approach formed by mashments (special mashups devised for coping with network management situations), the Mashment Ecosystem, the process to develop and launch mashments, the Mashment System Architecture, and the Mashment Maker. An extensive analysis of the approach was conducted on networks based on the Software-Defined Networking paradigm and virtual nodes. The results of analysis have provided directions and evidences that corroborate the feasibility of using the Situation Management and mashups as an effective approach for network management

Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey

Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving 5G and 6G networks. A 5G/6G network can comprise various network slices from unique or multiple tenants. Network providers need to perform intelligent and efficient resource management to offer slices that meet the quality of service and quality of experience requirements of 5G/6G use cases. Resource management is far from being a straightforward task. This task demands complex and dynamic mechanisms to control admission and allocate, schedule, and orchestrate resources. Intelligent and effective resource management needs to predict the services’ demand coming from tenants (each tenant with multiple network slice requests) and achieve autonomous behavior of slices. This paper identifies the relevant phases for resource management in network slicing and analyzes approaches using reinforcement learning (RL) and DRL algorithms for realizing each phase autonomously. We analyze the approaches according to the optimization objective, the network focus (core, radio access, edge, and end-to-end network), the space of states, the space of actions, the algorithms, the structure of deep neural networks, the exploration–exploitation method, and the use cases (or vertical applications). We also provide research directions related to RL/DRL-based network slice resource management

Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey

Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving 5G and 6G networks. A 5G/6G network can comprise various network slices from unique or multiple tenants. Network providers need to perform intelligent and efficient resource management to offer slices that meet the quality of service and quality of experience requirements of 5G/6G use cases. Resource management is far from being a straightforward task. This task demands complex and dynamic mechanisms to control admission and allocate, schedule, and orchestrate resources. Intelligent and effective resource management needs to predict the services’ demand coming from tenants (each tenant with multiple network slice requests) and achieve autonomous behavior of slices. This paper identifies the relevant phases for resource management in network slicing and analyzes approaches using reinforcement learning (RL) and DRL algorithms for realizing each phase autonomously. We analyze the approaches according to the optimization objective, the network focus (core, radio access, edge, and end-to-end network), the space of states, the space of actions, the algorithms, the structure of deep neural networks, the exploration–exploitation method, and the use cases (or vertical applications). We also provide research directions related to RL/DRL-based network slice resource management