To support multiple on-demand services over fixed communication networks, network operators must allow flexible customization and fast provision of their network resources. One effective approach to this end is network virtualization, whereby each service is mapped to a virtual subnetwork providing dedicated on-demand support to network users. In practice, each service consists of a prespecified sequence of functions, called a service function chain (SFC), while each service function in a SFC can only be provided by some given network nodes. Thus, to support a given service, we must select network function nodes according to the SFC and determine the routing strategy through the function nodes in a specified order. A crucial network slicing problem that needs to be addressed is how to optimally localize the service functions in a physical network as specified by the SFCs, subject to link and node capacity constraints. In this paper, we formulate the network slicing problem as a mixed binary linear program and establish its strong NP-hardness. Furthermore, we propose efficient penalty successive upper bound minimization (PSUM) and PSUM-R(ounding) algorithms, and two heuristic algorithms to solve the problem. Simulation results are shown to demonstrate the effectiveness of the proposed algorithms.

We consider a heterogeneous network (HetNet) of base stations (BSs) connected via a backhaul network of routers and wired/wireless links with limited capacity. The optimal provision of such networks requires proper resource allocation across the radio access links in conjunction with appropriate traffic engineering within the backhaul network. In this paper we propose an efficient algorithm for joint resource allocation across the wireless links and the flow control within the backhaul network. The proposed algorithm, which maximizes the minimum rate among all the users and/or flows, is based on a decomposition approach that leverages both the Alternating Direction Method of Multipliers (ADMM) and the weighted-MMSE (WMMSE) algorithm. We show that this algorithm is easily parallelizable and converges globally to a stationary solution of the joint optimization problem. The proposed algorithm can also be extended to deal with per-flow quality of service constraint, or to networks with multiantenna nodes.

We consider the distributed network routing problem in a large-scale hierarchical network whereby the nodes are partitioned into subnet works. each managed by a network controller (NC). and there is a central NC to coordinate the operation of the distributed NCs. We propose a semi-asynchronous routing algorithm for such a network. whereby the computation is distributed across the NCs and is parallel within each Ne. A key feature of the algorithm is its ability to han dle a certain degree of asynchronism: the distributed NCs can per form their local computation asynchronously at different processing speed. The efficiency of the proposed algorithm is validated through numerical experiments.

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he wireless telecommunication industry is facing unprecedented challenges due to the increasing demands on network capacity to support a large number of devices requiring always-on connectivity and applications demanding stringent requirements such as low latency and high peak data rates. In addition, services of future wireless networks (WNs) have significant diversity in service requirements and service characteristics. There is also an emerging trend demanding openness in the wireless telecommunication industry in order to utilize third party resources and services by establishing appropriate partnerships.From the perspective of network resources, future 5G WN models should intelligently integrate a variety of network resources from multiple resource owners, including mobile network and wired network infrastructures, spectral resources, and data centers, in order to maximize resource utilization and meet traffic load requirements. From the perspective of services delivered by networks, future WNs should provide service-customized virtual networks (SCVNs) to satisfy the diverse traffic demands and requirements. From the perspective of network operation, full automation of network service provisioning and network control/management is required to enable rapid service provisioning and flexible network operation. In addition, future WNs will feature a completely open market and extensive cooperation among partners. We can anticipate more types of players being introduced to this industry due to the openness of the market. One type of player is the infrastructure provider, which includes telecommunication network infrastructure (network nodes [NNs], physical connection links, etc.) providers, spectrum providers, and data center (DC) providers. Another type of player is the physical network operator such as wireless network operators (WNOs), which control and manage the WNs. More WNOs can be envisioned in the future due to this openness. A third type of player is a virtual network operator (VNO), which provides networking services for its customers using the services obtained from physical network operators. Then, there are over-the-top (OTT) customers and end customers. The former are application service providers, which provide application services to their subscribers using wireless network resources; the latter are the customers that send or receive data traffic using the wireless network resources.This tutorial discusses a future wireless network architecture along with major fifth generation (5G) research activities in the industry. Assuming the availability of network programmability provided by network functions virtualization (NFV) and software defined networking (SDN), a novel 5G wireless network architecture, MyNET, is proposed along with a number of supporting use cases. The architecture facilitates the provisioning of SCVNs and redefines wireless network control/management functionality. In MyNET, basic logical functions are identified for both the control and data planes. These basic functions include ...