The expected complexity of shared 5G/6G cloud and network infrastructures requires a functional management and orchestration (MANO) architecture to automatically provision distinct vertical services. These are mapped as generic network services (NSes) specifying their computing and networking needs: Virtual Network Functions (VNFs) and Virtual Links. We focus on a hierarchical MANO implementation where a Resource Layer orchestrator handles the configuration of the physical infrastructure and exposes an abstract view of it to the upper-layer Service Orchestrator. Two different abstraction philosophies are adopted, namely the Infrastructure Abstraction, which pre-calculates the resource allocations before advertising them, and the Connection Service Abstraction, which exposes potential connectivity services without an actual resource allocation. The resulting abstracted infrastructure becomes the input of the Service Orchestrator to make (placement) decisions fulfilling the NS demands. Thus, a novel placement algorithm is devised which, unlike previous works, can handle NSes with arbitrary VNF topology demands. The performance of the MANO functions is experimentally evaluated by dynamically creating/terminating heterogeneous NSes in terms of: NS blocking, blocked bandwidth/VNF ratio, bandwidth occupancy, and VNF distribution per cloud site. From the results, Connection Service Abstraction does attain a more efficient use of resources and in general, performs better than the Infrastructure Abstraction approach.

Online Network Resource Allocation (ONRA) for service provisioning is a fundamental problem in communication networks. As a sequential decision-making under uncertainty problem, it is promising to approach ONRA via Reinforcement Learning (RL). But, RL solutions suffer from the sample complexity issue; i.e., a large number of interactions with the environment are needed to find an efficient policy. This is a barrier to utilize RL for ONRA as on one hand, it is not practical to train the RL agent offline due to lack of information about future requests, and on the other hand, online training in the real network leads to significant performance loss because of the sub-optimal policy during the prolonged learning time. The performance degradation is even higher in non-stationary ONRA where the agent should continually adapt the policy with the changes in service requests. To address this issue, we develop a general resource allocation framework, RADAR, using model-based RL for a class of ONRA problems with known immediate rewards. RADAR improves sample efficiency via exploring the state space in background and exploiting the policy in decisiontime using synthetic samples by the model of the environment, which is trained by real interactions. Applying RADAR on the multi-domain service federation problem to maximize profit via selecting proper domains for service requests deployment, shows its continual learning capability and up to 44% performance improvement w.r.t. the standard model-free RL solution.

One of the main tasks of new-generation cellular networks is the support of the wide range of virtual services that may be requested by vertical industries, while fulfilling their diverse performance requirements. Such task is made even more challenging by the time-varying service and traffic demands, and the need for a fully-automated network orchestration and management to reduce the service operational costs incurred by the network provider. In this paper, we address these issues by proposing a softwarized 5G network architecture that realizes the concept of ML-as-a-Service (MLaaS) in a flexible and efficient manner. The designed MLaaS platform can provide the different entities of a MANO architecture with already-trained ML models, ready to be used for decision making. In particular, we show how our MLaaS platform enables the development of two ML-driven algorithms for, respectively, network slice subnet sharing and run-time service scaling. The proposed approach and solutions are implemented and validated through an experimental testbed in the case of three different services in the automotive domain, while their performance is assessed through simulation in a large-scale, real-world scenario. In-testbed validation shows that the use of the MLaaS platform within the designed architecture and the ML-driven decision-making processes entail a very limited time overhead, while simulation results highlight remarkable savings in operational costs, e.g., up to 40% reduction in CPU consumption and up to 30% reduction in the OPEX.

Network service federation in 5G/B5G networks enables service providers to extend service offering by collaborating with peering providers. Realizing this vision requires interoperability among providers towards end-to-end service orchestration across multiple administrative domains. Smart admission control is fundamental to make such extended offering profitable. Without prior knowledge of service requests, the admission controller (AC) either determines the domain to deploy each demand or rejects it to maximize the long-term average profit. In this paper, we first obtain the optimal AC policy by formulating the problem as a Markov decision process, which is solved through the policy iteration method. This provides the theoretical performance bound under the assumption of known arrival and departure rates of demands. Then, for practical solutions to be deployed in real systems, where the rates are not known, we apply the Q-Learning and R-Learning algorithms to approximate the optimal policy. The extensive simulation results show that learning approaches outperform the greedy policy and are capable of getting close to optimal performance. More specifically, R-learning always outperformed the rest of practical solutions and achieved an optimality gap of 3-5% independent of the system configuration, while Q-Learning showed lower performance and depended on discount factor tuning.

Service Function Chaining and Network Function Virtualization are enabling technologies that provide dynamic network services with diverse QoS requirements. Regarding the limited infrastructure resources, service providers need to prioritize service requests and even reject some of low-priority requests to satisfy the requirements of high-priority services. In this paper, we study the problem of deployment and reconfiguration of a set of chains with different priorities with the objective of maximizing the service provider's profit; wherein, we also consider management concerns including the ability to control the migration of virtual functions. We show the problem is more practical and comprehensive than the previous studies, and propose an MILP formulation of it along with two solving algorithms. The first algorithm is a fast polynomial-time heuristic that calculates an initial feasible solution to the problem. The second algorithm is an exact method that utilizes the initial feasible solution to achieve the optimal solution quickly. Using extensive simulations, we evaluate the algorithms and show the proposed heuristic can find a feasible solution in at least 83% of the simulation runs in less than 7 seconds, and the exact algorithm can achieve 25% more profit 8 times faster than the state-of-the-art MILP solving methods.

Emergency services must be able to transfer data with high priority over different networks. With 5G, slicing concepts at mobile network connections are introduced, allowing operators to divide portions of their network for specific use cases. In addition, Software-Defined Networking (SDN) principles allow to assign different Quality-of-Service (QoS) levels to different network slices.This paper proposes an SDN-based solution, executable both offline and online, that guarantees the required bandwidth for the emergency flows and maximizes the best-effort flows over the remaining bandwidth based on their priority. The offline model allows to optimize the problem for a batch of flow requests, but is computationally expensive, especially the variant where flows can be split up over parallel paths. For practical, dynamic situations, an online approach is proposed that periodically recalculates the optimal solution for all requested flows, while using shortest path routing and a greedy heuristic for bandwidth allocation for the intermediate flows.Afterwards, the offline approaches are evaluated through simulations while the online approach is validated through physical experiments with SDN switches, both in a scenario with 500 best-effort and 50 emergency flows. The results show that the offline algorithm is able to guarantee the resource allocation for the emergency flows while optimizing the best-effort flows with a sub-second execution time. As a proof-of-concept, a physical setup with Zodiac switches effectively validates the feasibility of the online approach in a realistic setup.

Service function chaining along with network function virtualization enable flexible and rapid provisioning of network services to meet increasing demand for short-lived services with diverse requirements. In this paradigm, the main question to be answered is how to deploy the requested services by means of creating virtual network function (VNF) instances and routing the traffic between them, according to the services specifications. In this paper, we define the energy aware service deployment problem, and present the ILP formulation of it by considering limited traffic processing capacity of VNF instances and management concerns. We apply the Benders decomposition technique to decompose the problem into two smaller problems: master and sub-problem. As it is NP-Hard to find a non-trivial solution to the ILP master problem, we resort to the relaxed LP version of the problem. Then, we design methods based on the feasibility pump and duality theorem to rapidly calculate a near-optimal integer solution. The extensive simulation results show even in a network with 24 switches and 40 servers, our algorithm can deploy 35 requests in less than 3 seconds while the total power consumption is only about 1.3 times of the optimal solution obtained by the exhaustive exact approach. Moreover, it significantly outperforms the prominent SFC deployment algorithms in the fat-tree topology.

In this paper, we study node connectivity in multi-hop wireless networks. Nodal degree of connectivity as one of the fundamental graph properties is the basis for the study of network connectivity and has been a major research issue in multi-hop wireless networks. We use Random Geometric Graphs (RGG) to model multi-hop wireless networks and present a non-asymptotic analysis assuming bounded area and finite number of nodes. We assume random uniformly scattered nodes in a square-shaped bounded area. We derive a closed-form formula for the expected value of node degree of connectivity and propose an approximation algorithm for degree distribution in multi-hop wireless networks. Our extensive simulation results confirm that the proposed non-asymptotic analyses are quite accurate.

Multi-channel multi-radio WMNs are promising solutions for overcoming the limited capacity problem in multi- hop wireless networks. In these WMNs, each mesh router is equipped with multiple radios and each radio operates in a distinct frequency band. Channel assignment is the key issue that should be addressed in these networks. In this paper we propose a channel assignment scheme with the objective of maximizing per-flow bandwidth with fairness consideration to equalize the bandwidth assignment of flows. A novel problem formulation as multi-objective non-linear optimization problem is developed. We propose a heuristic randomized channel assignment algorithm, MFPFB, to obtain an approximate solution. The MFPFB assigns channels based on the interference level experienced by each flow, which is derived from the given traffic pattern and the proposed interference model, DWIG. For a given channel assignment, a simple algorithm allocates bandwidth for each flow. We used numerical and ns-2 simulations to compare our algorithm against others, investigate effect of routing mechanisms and to validate our model. The result indicates an improvement of up to 20% in the effectiveness of bandwidth assignment.

One of the most challenging issues in mobile ad-hoc networks (MANETs) arena, which consist of autonomous and self interested nodes, is the problem of providing incentives for nodes to cooperate in forwarding network packets. In this paper, we propose Express as a cooperation mechanism which is both efficient in computations imposed on mobile nodes and secure against deceptive threats by nodes. Using Express, network nodes gain credit for participating in forwarding network packets. Recent works utilize digital signatures to provide security against cheating actions by nodes. Express substitutes hash operations for digital signature operations as much as possible, which consequently reduces computational overhead to a great extent. Setting credits and penalties through this mechanism, each node beneficially tends to adopt cooperative behavior.