To address the challenges posed by the deployment of microservices of future end-user applications in the cloud continuum, a performance prediction model working together with a network elasticity controller will be needed. With that aim, this work introduces Layered Queuing-Graph Neural Networks (LQ-GNN), a novel Machine Learning (ML) approach to develop a generalized performance prediction model for microservicebased applications. Unlike previous works focused on individual applications, our proposal aims for a versatile model applicable to any microservice-based application, integrating the Layered Queueing Network (LQN) modeling with Graph Neural Networks (GNN). LQ-GNN allows to efficiently estimate the response time of applications under different resource allocations and placements on the computing continuum. The obtained evaluation results indicate that the proposed model achieves a prediction error below 10% when considering different evaluation scenarios. Compared to existing methodologies, our approach balances prediction accuracy and computational efficiency, making it viable for real-time deployments. Consequently, ML-based performance prediction can significantly enhance the resource management and elasticity control of microservice-based architectures, leading to more resilient and efficient systems.

Opportunistic Networks (ONs) are complex systems where nodes move and meet stochastically. For data to be trans-mitted, it must be copied between nodes and then carried around through multiple hops. Because the network is continuously evolving and can be highly fragmented, the usual connectivity graph representation, typically used to compute routes or doing offline traffic engineering, is not suitable.This article proposes a model specially designed for route management on ONs, and we show its usefulness by implementing an ML-based route management algorithm for those networks. This algorithm is trained offline over the model built from historical traces of a particular network setup and then deployed to make fast, real-time forwarding decisions.

The traditional telecommunications business is evolving towards offering a richer set of services beyond basic connectivity, leveraging on network programmability and virtualization. A versatile execution environment is required, capable of running different workloads in different locations in the network. Cloud computing is the key paradigm that allows fostering this trending change. One interesting question to solve is to what extent those computing environments have to move towards the network edge. Some services can be enabled by environments with increased capillarity, while others can be implemented in environments with more relaxed constraints (e.g., in terms of latency). This paper explores this topic by differentiating service edge from physical network edge and proposing an architecture based on the ALTO server for assisting orchestration systems in discriminating the suitable environments for each service. We present a network-flow strategy for assigning services to infrastructure elements following those precepts, together with an initial scalability evaluation of the proposed assignment solution to show its feasibility.

The NECOS (Novel Enablers for Cloud Slicing) platform controls the entire lifecycle of end-to-end cloud-network slice instances, so that new services and applications can run in a broader perspective for quality-guaranteed and highly isolated multi-tenancy. Elasticity plays a vital role in ensuring the efficiency of the NECOS platform. According the state of the art, mainstream reactive elasticity solutions are not suitable for dealing with cloud-network slicing-defined systems by the fact that they cannot trigger time-burdensome vertical elasticity on deriving resource-depleting conditions. This gap can be filled by introducing in the elaSticity cLOud-neTwork Slices (SLOTS) solution, which participates in the NECOS platform in the form of new building blocks that interwork with others to add new patterns of cloud- and networking-type resources under resource-depleting conditions. The unique contributions made by SLOTS are as follow, and go beyond the state–of-the-art by: (i) fully supporting cloud-network slice-defined systems; (ii) offering a hybrid elasticity approach; (iii) provide a sophisticated mechanism to enable a slice solidarity approach, and (iv) providing a real lab-premised large-scale testbed for a SLOTS prototyping assessment. Analysis on the evaluation results suggest that SLOTS performs well by accomplishing a higher number of elasticity events under resource-critical conditions, while slightly adding computing overhead, when compared with a widely-used stochastic-based representing solution.

To support future 5G computing and communication scenarios, cloud-network management tools should deploy cloud-network services adopting uncomplicated ways, reducing not only the time to market but also broadening the community capable of deploying new services. In this paper, we present the support of NECOS Platform, an EU-Brazil jointly funded project, towards slice-as-a-service creation from non-structured service description. We describe how NECOS architecture allows such functionality during the slice creation loop, and we present the initial efforts we took for structuring such a mechanism.

Network slicing has recently been proposed as one of the main enablers for 5G networks; it is bound to cope with the increasing and heterogeneous performance requirements of these systems. To "slice" a network is to partition a shared physical network into several self-contained logical pieces (slices) that can be tailored to offer different functional or performance requirements. Moreover, a defining characteristic of the slicing paradigm is to provide resource isolation as well as efficient use of resources. In this context, the thesis described in this paper contributes to the problem of slicing WiFi networks by proposing a solution to the problem of enforcing and controlling slices in WiFi Access Points. The focus of the research is on a variant of network slicing called QoS Slicing, in which slices have specific performance requirements. In this document, we describe the two main contributions of our research, a resource allocation mechanism to assign resources to slices, and a solution to enforce and control slices with performance requirements in WiFi Access Points.

Network slicing has recently been proposed as one of the main enablers for 5G networks. The slicing concept consists of the partition of a physical network into several self-contained logical networks (slices) that can be tailored to offer different functional or performance requirements. In the context of 5G networks, we argue that existing ubiquitous WiFi technology can be exploited to cope with new requirements. Therefore, in this paper, we propose a novel mechanism to implement network slicing in WiFi Access Points. We formulate the resource allocation problem to the different slices as a stochastic optimization problem, where each slice can have bit rate, delay, and capacity requirements. We devise a solution to the problem above using the Lyapunov drift optimization theory, and we develop a novel queuing and scheduling algorithm. We have used MATLAB and Simulink to build a prototype of the proposed solution, whose performance has been evaluated in a typical slicing scenario.

In forthcoming 5G networks, slicing has been proposed as a means to partition a shared physical network infrastructure into different self-contained logical parts (slices), which are set up to satisfy certain requirements. Although the topic has been thoroughly investigated by the scientific community and the industry, there are not many works addressing the challenges that appear when trying to exploit slicing techniques over WiFi networks. In this paper, we propose a novel method of allocating resources for WiFi networks to satisfy minimum bit rate requirements. We formulate an optimization problem, and we propose a solution based on the theory of Lyapunov drift optimization. The validity of the proposed solution is assessed by means of a simulation-based evaluation in Matlab.

Network slicing has recently appeared as one of the most important features that will be provided by 5G networks and is attracting considerable interest from industry and academia. At the wireless edge of these networks, most of the contributions in this area are related to cellular technologies leaving behind WiFi networks. In this work, we present a resource allocation mechanism based on airtime assignment to achieve infrastructure sharing and slicing in WiFi Access Points. The approach is simple and has the potential to be straightforwardly used within scenarios of wireless access infrastructure sharing.

Nowadays there is a proliferation of smart devices used for media consumption. Usually, media contents are streamed from a Content Distribution Network (CDN), through a cellular network, which can get overloaded when there is a large number of active users per cell. At the same time the devices, i.e. smartphones, typically possess a set of wireless interfaces such as WiFi and Bluetooth that can be used to communicate with other devices in its proximity. In this work we propose a publish/subscribe, opportunistic network protocol aimed at off-load infrastructure network nodes. It contributes to optimize the cellular network usage among mobile devices and to reduce the costs and energy consumption induced by the reproduction of media that are temporally popular.

Opportunistic Networks have been designed for transmitting data in difficult environments, characterized by high mobility, sporadic connectivity, and constrained resources. To sustain these networks, the literature describes methods such as Epidemic and Spray&Wait, which do not learn from the network behaviour, and Gossiping-based algorithms that collect historical network data to improve efficiency. In this paper, we show that Gossiping-based solutions suffer from pathological behaviour in some simple network scenarios. Under certain conditions almost all the data transmitted by some nodes may get lost in the network, not reaching its destination. To address this problem we have proposed an algorithm that responds in a more robust manner while staying relatively simple. In this work, we show that our algorithm achieves delivery rates comparable to gossiping-based algorithms, while being more robust and providing better fairness. To illustrate this result, we test native implementations of our solution, Path Sampling, and related algorithms on a network simulator.

New architectural and design approaches for radio access networks have appeared with the introduction of network virtualization in the wireless domain. One of these approaches splits the wireless network infrastructure into isolated virtual slices under their own management, requirements, and characteristics. Despite the advances in wireless virtualization, there are still many open issues regarding the resource allocation and isolation of wireless slices. Because of the dynamics and shared nature of the wireless medium, guaranteeing that the traffic on one slice will not affect the traffic on the others has proven to be difficult. In this paper, we focus on the detailed definition of the problem, discussing its challenges. We also provide a review of existing works that deal with the problem, analyzing how new trends such as software defined networking and network function virtualization can assist in the slicing. We will finally describe some research challenges on this topic.

Nowadays, trying to obtain better coverage and performance, and allowed by the low-hardware prices, it is common to deploy a large number of IEEE 802.11 devices in offices, meeting rooms or auditoriums configuring the so called high-density networks. In such a scenario, the shared nature of the transmission medium causes interference problems. Some physical-layer- and link-layer-adaptation mechanisms to palliate those problems have been developed, however, most of them have not been independently implemented and assessed. In this paper, we implement in a simulator some of the existent solutions, compare them in a simulation environment and show that, in some situations, the existing solutions can lead to a starvation problem. Finally, we propose a new mechanism that manages datarate, transmit power and carrier-sense threshold to ameliorate this problem.

Several countries such as Uruguay and Brazil are implementing the well-known One Laptop Per Child Program (OLPC) by which every child that assists to the primary school obtains in property a laptop with wireless capabilities. They carry their laptops from home to school and back every day and, as seen in the experience, they also carry their laptops to parks, community centers etc. This provides a wide platform for the deployment of Disruption Tolerant network applications. This paper presents a self-managed, opportunistic and content-based routing protocol that supports a network of environmental sensors implemented using consumer-grade wireless routers working together with OLPC laptops. We evaluate the impact of the density, diversity and connectivity of the mobile network on the performance of the protocol and we show how it self-configures it parameters to adapt to changing network conditions.

In order to execute a handover processes in a GSM or UMTS Radio Access Network, each cell has a list of neighbors to which such handovers may be made. Today, these lists are statically configured during network planning, which does not allow for dynamic adaptation of the network to changes and unexpected events such as a cell failure. This paper advocates an autonomic, decentralized approach to dynamically configure neighboring cell lists. The main contribution of this work is a novel protocol, called DOC, which detects and continuously tracks the coverage overlaps among cells. The protocol executes on a spanning tree where the nodes are radio base stations and the links represent communication channels. Over this tree, nodes periodically exchange information about terminals that are in their respective coverage area. Bloom filters are used for efficient representations of terminal sets and efficient set operations. The protocol aggregates Bloom filters to reduce the communication overhead and also for routing messages along the tree. Using simulation, we study the system in steady state, when a base station is added or a base station fails, and also during the initialization phase where the system self-configures.

During the last years there has been a strong research effort on the autonomic communications and self-management paradigms. Following this impulse, the academic community and the industry have proposed several architectures and techniques to allow network devices to make their own configuration decisions. Those proposals often include resource-expensive technologies such as complex inference machines, ontological modeling and probabilistic prediction that may not be suitable for the most pervasive and inexpensive network-enabled devices. This paper addresses this facet of the autonomic systems introducing RAN. This system aims to be a complete rule-based, distributed system specially designed and implemented to enable autonomic behavior on very constrained devices, such as domestic wireless routers with resources as low as 16 MB of RAM and 4 MB of storage memory. The RAN system was developed to serve the objectives of rural ambient networks, a project that targets the so-called digital divide deploying low-cost wireless mesh infrastructure in rural communities. In this context, RAN, in autonomic and distributed manners, optimizes the network configuration to minimize the monetary cost that the community has to pay for using the IT infrastructure. Finally, this work presents an evaluation of RAN that shows how it makes possible to perform sophisticated optimization decisions with a very small overhead in terms of CPU and memory.

Various centralized, distributed or cooperative management systems have been proposed to address the demands of wireless telecommunication networks. However, considering the size, complexity and heterogeneity that those networks will have in the future, current solutions either do not scale properly, or have no support for automation, or lack of the flexibility and simple control that operators will need for managing future networks in a cost-effective way. To address this problem, we designed Omega, a distributed and policy-based network management system that uses rich knowledge-modeling techniques to develop self-configuration capabilities. Omega also implements a novel conflict-resolution method that uses high-level goals and machine learning techniques to optimize its policy-based decisions. Using simulations, in this paper we show how Omega reduces the impact of a node crash on the overall availability of a radio access network by optimizing the lists of neighboring cells of the nodes in the vicinity.

In order to execute a handover processes in a Radio Access Network, each cell has a configured list of neighbors to which such handovers are made. Rapid re-configuration ofthe neighborhood list in response to network failures and other events is currently not possible. To address this problem this paper suggests an autonomic approach for dynamically configuring neighboring cell lists and introduces a decentralized, three-layered framework. As a key element of this framework, a novel probabilistic protocol that detects and continuously tracks the coverage overlaps among cells is presented and evaluated. The protocol called DOC maintains a distributed graph of over-lapping cells. Dae to asing Bloom filters and aggregation techniques it exhibits a low traffic and computational overhead. A first series of simulation studies suggests that DOC is scalable with respect to network size and the namber of terminals.

Individual wireless access networks show limitations that can be overcome through the integration of different technologies into a unified platform [i.e., fourth-generation (4G) system]. Nevertheless, the integration of heterogeneous networks poses many challenges such as adding complexity to the processes of deciding when to handoff, selecting the best network, and minimizing roaming effects using appropriate handover methods. This paper presents PROTON, a novel solution that supports decision-making processes related to roaming between heterogeneous technologies. PROTON deploys a formal policy representation model, based on finite-state transducers, that evaluates policies using information from the context to manage mobiles' behavior in a transparent manner, hiding 4G systems' complexities. We blend concepts of autonomic computing into the design of the solution and manage to improve user experience in typical 4G scenarios, while keeping transparency.