Cloud radio access network (CRAN) offers low-latency and high-speed services to mobile end-users by locating remote radio heads (RRHs) near end-users and centralizing the baseband units (BBUs) corresponding to the RRHs in a central cloud. However, considering the time-varying traffic loads, a self-adjustable base station (BS) can respond to changing traffic loads for meeting user demands is needed. This paper focuses on minimizing the cost of fronthaul establishment and bandwidth consumption in forming a self-adjustable virtual BS (VBS). Since the fronthaul is critical infrastructure of CRAN connecting BBUs and RRHs that affects the VBS formation but cannot be adjusted dynamically, we jointly consider their decision making and formulate it as a two-stage stochastic problem. Since this problem can be linearized to an integer linear program (ILP) but is difficult to solve given a large network scale, we propose an approach called FEBD inspired by Benders decomposition (BD) to achieve the optimal fronthaul connection. Furthermore, we propose a heuristic approach that can quickly adjust the VBS formation in response to user demand. The numerical experiments show that the combined use of FEBD and the heuristic approach provides results that are close to the ILP results with a significantly shorter computation time.

Network function virtualization (NFV) is a new network architecture concept that simplifies the deployment of network services and improves service management. However, it is challenging for a network service provider (NSP) to decide where to place virtual network functions (VNFs). Most previous studies have considered only single-chain services, wherein the VNFs for a request are executed in sequence. In contrast to previous approaches, we consider more general and practical situations in which the VNFs of a request can be executed in parallel and are represented as a forwarding graph. Our objective is to maximize the profits earned by providing network services while satisfying the delay requirements of requests. We formulate the VNF placement problem as an integer linear programming (ILP) problem. Due to the complexity of this problem, we propose a probability-based approach called PBP, in which the placements of the VNFs are determined based on their probabilities of contributing to the profit. Furthermore, we propose a heuristic reliability-aware algorithm to guarantee service reliability, in which each VNF of a request is assigned a backup that can be shared with other requests. Simulation experiments show that PBP achieves a much shorter computation time than previous algorithms while earning higher profit, and furthermore, our reliability-aware algorithm provides the same reliability as a previous algorithm while yielding much higher profit.