Edge Computing emerges as a solution that overcomes some obstacles of traditional central data centers to support the performance-critical Internet of Things applications. However, a challenge therein is the resource allocation for heterogeneous applications at a network edge composed of distributed and resource-restricted nodes. In this paper, we investigate how to place replicas of applications, and distribute requests among these replicas to optimize multiple objectives. We propose a genetic algorithm based on Pareto fronts as a problem-solving meta-heuristic to prioritize latency-sensitive applications and optimize conflicted objectives. Evaluation results show that our proposal outperforms other benchmark algorithms in terms of response deadline violation, as well as terms of other important and sometimes conflicting objectives, such as cost and availability.

This paper proposes a mechanism for uplink packet scheduler in LTE network in the context of Machine-to-Machine (M2M) communication. The proposed approach uses the current and past information of the system to satisfy the Quality of Service (QoS) requirements, to ensure fairness in resource allocation and to control the congestion caused by M2M devices. We carried out some network simulations by using a NS-3 simulator so as to show the effectiveness of the proposed approach. The results indicate that our solution can reduce the impact of M2M communication on Human-to-Human (H2H) communication and avoid the problem of starvation, when compared to related approaches.

The Long Term Evolution (LTE) standard plays an important role in the development of Machine-to-Machine (M2M) communication. However, the M2M communication has several different characteristics regarding to Human-to-Human (H2H) communication. Therefore, the shortage of radio resources, satisfaction of the Quality of Service (QoS) requirements and the reduction of the H2H traffic performance are important issues to be addressed when introducing the M2M communication in the network. In this article, we present a scheduler that may dynamically adjust to the level of congestion of the network based on the current traffic information of each device. The main goals of our approach are (i) satisfy the QoS requirements (ii) ensure fair allocation of resources and (iii) control the impact of H2H traffic performance. The simulation results demonstrated that the proposed scheduling has good performance according to the three objectives aforementioned.

Network slicing is a vital component of the 5G system to support diverse network scenarios, creating virtual networks (slices) by mapping virtual network requests to real networks. The mapping is an arduous computing process, mathematically studied and known as the Virtual Network Embedding (VNE) problem, and its complexity is NP-Hard. The mapping process is oriented to respect the QoS demands from the virtual network requests and the available resources in the physical-substrate infrastructure. Meta-heuristic approaches are a suitable way to solve the VNE problems because of their capacity to escape from the local optimum and adapt the solution search to complex networks; these abilities are essential in 5G networks scenarios. This article presents a systematic review of meta-heuristics organized by application, development and problem-solving approaches to VNE. It also provides the standard parameters to model the infrastructure and virtual network requests to simulate network slicing as a service. Finally, our work proposes some future research based on the discovered gaps.