Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration.

The disaggregated architecture of advanced Radio Access Networks (RANs) with diverse X-haul latencies, in conjunction with resource-limited multi-access edge computing networks, presents significant challenges in designing a general model in placing baseband and user plane functions to accommodate versatile 5G services. This paper proposes a novel approach, NetMind+, which leverages Deep Reinforcement Learning (DRL) to determine the function placement strategies in diverse and evolving RAN topologies, aiming at minimizing power consumption. NetMind+ resolves the problem with a maze-solving strategy, enabling a Markov Decision Process with standardized action space scales across different networks. Additionally, a Graph Convolutional Network (GCN) based encoding and an incremental learning mechanism are introduced, allowing features from different and dynamic networks to be aggregated into a single DRL agent. This facilitates the generalization capability of DRL and minimizes the negative retraining impact. In an example with three sub-networks, NetMind+ demonstrates a substantial 32.76% improvement in power savings and a 41.67% increase in service stability compared to benchmarks from the existing literature. Compared to traditional methods necessitating a dedicated DRL agent for each network, NetMind+ attains comparable performance with 70% of the training cost savings. Furthermore, it demonstrates robust adaptability during network variations, accelerating training speed by 50%.

Multi-access edge computing (MEC) has been regarded as one of the essential technologies for mobile networks, by providing computing resources and services close to users, thereby, avoiding extra energy consumption and fitting the low-latency ultra-reliable requirements for emerging 5G applications. Task offloading policy plays a pivotal role in handling offloading requests and maximizing the network computing performance. Most recently developed offloading solutions are designed for instant rewards, therefore, neglecting the long-term computing resource optimization at the edge, which fail to deliver optimized network performance when a significant increase of computing requests appears. In this paper, with the objective of maximizing long-term offloading benefits on delay and energy consumption, task offloading policies are proposed to firstly avoid resource over-distribution through deep reinforcement learning (DRL) based resource reservation and server cooperation, and secondly maximize the average instant reward and the utilization of reserved resources by an optimization-based joint policy consisting of offloading decision, transmission power allocation and resource distribution. The DRL-based joint policy is evaluated in a simulated multi-server edge computing network. Compared to previous solutions, the DRL-based algorithms achieve higher and more reliable overall rewards. Of the implemented three DRL-based algorithms, fully cooperative multi-agent DRL accounts for cooperation between servers, achieving a 70.5% reduction in reward variance and a 13.4% increase in average rewards over 500 continuous operations. Resource balanced policies on long-term rewards help edge networks handle the explosive growth of 5G computing-intensive applications in the future.

Network protection is a key solution and an important component in the requirements of virtualization over elastic optical networks (EONs). In this paper, we examine the significance of network virtualization with survivability design against single-link failures and shared risk link groups (SRLG) under dedicated protection and bandwidth squeezing schemes. We study an optimization version of the routing, modulation and spectrum allocation (RMSA) problem with the goal of maximizing the revenue from the accommodated requests of the virtual optical networks (VONs) or slices. We present a Mixed Integer Linear Programming (MILP) formulation for the problem and evaluate some numerical results. We suggest a heuristic that utilizes techniques of decomposition of the problem, which can be employed to obtain a near optimal solution that has a per instance guarantee on the closeness to the optimal solution.

Survivability is an important component in the requirements in elastic optical networks (EONs) with virtualization. In this paper, we examine the significance of network survivability design against single-link failure under dedicated protection and bandwidth squeezing schemes under multiple virtual topologies. We proposed an integer linear programming (ILP) formulation and a genetic algorithm (GA) to derive some different types of protection for each virtual topology considering routing and a channel-based spectrum approach. The proposed ILP and GA provide efficient survivability results and resource savings (in terms of spectrum) for a full design of modern virtualized EONs with different kinds of mechanisms for protection.

Elastic optical network (EON) is fast becoming a key solution for designing optical network with better usage of spectrum resources or other objectives of interest to tenant and/or operators. A primary concern of EONs is to protect the network against failures of its elements, because this kind of event can provoke the loss of substantial amount of traffic. In this paper, we propose a new mixed integer linear programming (MILP) formulation for protecting the network traffic against multiple link failures. The key idea is to use bandwidth squeezing together with grooming capability to provide few extra traffic for protection and guarantee a minimum bandwidth for each source-destination node pair under multiple failure events. The proposed formulation solves the virtual topology design problem jointly with the grooming, routing, modulation and spectrum allocation (RMSA) tasks. Due to the non-deterministic polynomial time (NP-hard) nature of the proposed MILP formulation, a heuristic strategy (referred to as two-step MILP) for large networks is also proposed. The solutions and performance of the proposed MILP formulation and two-step MILP analyzed through case studies in a small network. In addition, the performance of three large networks is assessed for cases scenarios where connections are under different service-level agreement (SLA). In view of proposed formulation and two-step MILP, it is possible to identify the configurations that ensure better usage of spectrum resources with different kinds of protection against single or multiple link failures.

Spectrum-sliced elastic optical path networks (SLICE) enable flexible bandwidth provisioning, which allows efficient resource utilization and support to heterogeneous bandwidth demands. This makes SLICE a very promising networking architecture. In SLICE, finding a route and a slice of the spectrum is an important design problem, which is known as the Routing and Spectrum Assignment (RSA) problem. In this paper, we present a new heuristic, based on Yen's k-shortest path algorithm and capable of dealing with the traffic heterogeneity to find appropriate sets of paths for each source-destination node pairs to mitigate the bottleneck in the network. The results show that the proposed algorithm reduces the lightpath blocking probability and achieves significantly improved spectrum efficiency. We also analyse the maximum number of alternate shortest paths for minimising the blocking probability.

This paper proposes a linear formulation and an iterative heuristic, both with traffic grooming capability, which can maximize the number of remaining available routes and minimize the number of transceivers in Elastic Optical Networks (EON). The aim of the proposal is to preserve the open capacity for the accommodation of future unknown demands. Case studies are carried out in order to analyze the basic properties of the formulation in a small network, and the heuristic is used for moderate larger networks. The results suggest that it is feasible to preserve enough open capacity to avoid blocking of future requests in EON with scarce resources.

This work proposes to describe a teaching approach for introductory laboratory course in object-oriented programming and its respective teacher's experience. The profile of the first classes of freshmen, with different career goals, enrolled in Interdisciplinary Bachelor of Science and Technology at Federal University of ABC is presented. Educational methodology adopted in the lab using tutorials and a project-based learning approach is also discussed. Furthermore, some statistics about assessment of a student class in response to the learning activities, and its respective analysis are shown. And finally their evaluation about this educational approach is presented.