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.

Cognizant of the ease with which softwarized functions can be dynamically scaled according to real time resource requirements, and the fact that multiple services can have common VNFs in their chaining, this paper tackles the problem of cost effective deployment of online services from the perspective of sharing their VNF instances. First, we formally formulate the deployment problem under VNFs sharing. Secondly, given the NP-hard nature of the above problem, we propose a reinforcement learning (RL) algorithm capable of making intelligent placement decisions while considering multiple conflicting costs. Costs of transmission, VNF instantiation or energy consumption, among others. Thanks to the intelligence of the RL algorithm, simulation results show that the performance of the proposed algorithm is within a 14% margin and similar to an optimal solution in terms of request provisioning cost and acceptance ratio, respectively. Moreover, the algorithm results in more than a 20% and a 70% improvement in terms of request deployment cost and time compared to a state-of-the-art algorithm, and up to more than a 40% improvement in terms of cost compared to an algorithm that greedily minimizes the transmission or VNF activation costs.

Network Service Providers (NSPs) envisage to support the divergent and stringent requirements of future services by instantiating these services as service chains, commonly referred to as Service Function Chains (SFCs), that are customized and configured to meet specific service requirements. However, due to the limited footprint of the Infrastructure Providers (InPs), these SFCs may have to transcend multiple InPs/domains. In this regard, determining the optimal set of InPs in which to embed the SFC request emerges as a complex problem for several reasons. First, the large number of possible combinations for selecting the InPs to embed the different sub-chains of the request makes this problem computationally complex, rendering optimal solutions only after long computations, especially in large scale networks, which is unfeasible for delay sensitive applications. Second, the unwillingness of InPs to disclose their internal information, which may be vital for making embedding decisions, usually implies the provisioning of single-domain solutions, which are unsuitable in this working scenario. In this regard, this paper first formulates the multi-domain service deployment problem under multiple request constraints, such as bandwidth or delay, among others. Then, due to the NP-hardness nature of the above problem, this paper proposes an algorithm that is aided by a multi-stage graph for computing a request embedding solution in a distributed manner, solving the problem in acceptable run-times. Results from different simulations reveal that the proposed algorithm is optimized in terms of acceptance ratio and embedding cost, with up to 60.0% and 88.7% improvements in terms of embedding cost and execution time, respectively, for some scenarios, in comparison with a benchmark state-of-the-art algorithm.