Effective network slicing requires an infrastructure network provider to deal with the uncertain demand and real-time dynamics of network resource requests. Another challenge is the combinatorial optimization of numerous resources, e.g., radio, computing, and storage. This article develops an optimal and fast real-time resource slicing framework that maximizes the long-term return of the network provider while taking into account the uncertainty of resource demand from tenants. Specifically, we first propose a novel system model which enables the network provider to effectively slice various types of resources to different classes of users under separate virtual slices. We then capture the real-time arrival of slice requests by a semi-Markov decision process. To obtain the optimal resource allocation policy under the dynamics of slicing requests, e.g., uncertain service time and resource demands, a Q-learning algorithm is often adopted in the literature. However, such an algorithm is notorious for its slow convergence, especially for problems with large state action spaces. This makes Q-learning practically inapplicable to our case in which multiple resources are simultaneously optimized. To tackle it, we propose a novel network slicing approach with an advanced deep learning architecture, called deep dueling that attains the optimal average reward much faster than the conventional Q-learning algorithm. This property is especially desirable to cope with real-time resource requests and the dynamic demands of users. Extensive simulations show that the proposed framework yields up to 40 higher long-term average return while being few thousand times faster, compared with state of the art network slicing approaches.
A number of research works have been introduced recently to address the network slicing resource allocation problem for the network provider @cite_28 - @cite_0 . In particular, the authors in @cite_28 and @cite_2 developed a two-tier admission control and resource allocation model to answer two fundamental questions, i.e., whether a slice request is accepted and how much radio resource is allocated to the accepted slice. To address this problem, the authors in @cite_28 used an extensive searching method to achieve the globally optimal resource allocation solution for the network provider. However, this searching method cannot be applied to complex systems with a large number of resources. To address this problem, a heuristic scheme with three main steps was introduced in @cite_2 to effectively allocate resources to the users. Yet this heuristic scheme cannot guarantee to achieve the optimal solution for the network provider. In addition, both network slicing resource allocation solutions proposed in @cite_28 and @cite_2 are heuristic methods with only radio resource taken into consideration. Thus, these solutions may not be appropriate to implement in dynamic network slicing resource allocation systems with a wide range of resource demands and services.
@cite_28 @cite_0 @cite_2
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5G mobile network is expected to serve flexible requirements hence dynamically allocate network resources according to the demands. Network slicing, where network resources are packaged and assigned in an isolated manner to set of users according to their specific requirements, is considered as a key paradigm to fulfil diversity of requirements. There will clearly be conflicting demands in allocation of such slices, and the effective provisioning of network slicing poses several challenges. Indeed, network slicing has a twofold impact in terms of user traffic prioritization as it dictates for the simultaneous management of the priority among different slices (i.e., interslice) and the priority among the users belonging to the same slice (i.e., intra-slice). In this paper, we propose a novel heuristicbased admission control mechanism able to dynamically allocate network resources to different slices in order to maximize the satisfaction of the users while guaranteeing to meet the requirements of the slices they belong to. Through simulations, we demonstrate how our proposal provides (i) higher user experience in individual slices, (ii) increased utilization of network resources and (iii) higher scalability when the number of users in each slice increases. The emerging network slicing paradigm for 5G provides new business opportunities by enabling multi-tenancy support. At the same time, new technical challenges are introduced, as novel resource allocation algorithms are required to accommodate different business models. In particular, infrastructure providers need to implement radically new admission control policies to decide on network slices requests depending on their Service Level Agreements (SLA). When implementing such admission control policies, infrastructure providers may apply forecasting techniques in order to adjust the allocated slice resources so as to optimize the network utilization while meeting network slices' SLAs. This paper focuses on the design of three key network slicing building blocks responsible for (i) traffic analysis and prediction per network slice, (ii) admission control decisions for network slice requests, and (iii) adaptive correction of the forecasted load based on measured deviations. Our results show very substantial potential gains in terms of system utilization as well as a trade-off between conservative forecasting configurations versus more aggressive ones (higher gains, SLA risk). In this paper, we study resource allocation for a multicarrier-based cognitive radio (CR) network. More specifically, we investigate the problem of secondary users’ energy-efficiency (EE) maximization problem under secondary total power and primary interference constraints. First, assuming cooperation among the secondary base stations (BSs), a centralized approach is considered to solve the EE optimization problem for the CR network where the primary and secondary users are using either orthogonal frequency-division multiplexing (OFDM) or filter bank based multicarrier (FBMC) modulations. We propose an alternating-based approach to solve the joint power-subcarrier allocation problem. More importantly, in the first place, subcarriers are allocated using a heuristic method for a given feasible power allocation. Then, we conservatively approximate the nonconvex power control problem and propose a joint successive convex approximation-Dinkelbach algorithm (SCADA) to efficiently obtain a solution to the nonconvex power control problem. The proposed algorithm is shown to converge to a solution that coincides with the stationary point of the original nonconvex power control subproblem. Moreover, we propose a dual decomposition-based decentralized version of the SCADA. Second, under the assumption of no cooperation among the secondary BSs, we propose a fully distributed power control algorithm from the perspective of game theory. The proposed algorithm is shown to converge to a Nash-equilibrium (NE) point. Moreover, we propose a sufficient condition that guarantees the uniqueness of the achieved NE. Extensive simulation analyses are further provided to highlight the advantages and demonstrate the efficiency of our proposed schemes. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users.