The emergence of Network Function Virtualization (NFV) and Software Defined Network (SDN) has greatly reformed the network. It is important to reduce the queuing delay spent/observed in NFV servers for the placement of Virtual Network Functions (VNFs). In this work, we mainly focus on the placement of VNFs with Poisson Arrived Traffic (VNFPPAT) to tackle the queuing delay problem. Both the Poisson distribution of traffic flows and various resource-sharing VNFs lead to the prolonged queuing delay in NFV servers with limited processing capacities. Considering the end-to-end delay as our optimization objective, we formulate this problem as a 0-1 quadratic fractional programming problem. This formulation is linearized to obtain the optimal solution for small scale networks. After proving VNFPPAT is NP-hard, we propose heuristic algorithms to obtain sub-optimal placement schemes. Through extensive simulations, we have shown that our proposed algorithms outperform the related state-of-the-art Improve Service Chaining Performance (ISCP) by 72% in terms of the end-to-end delay.

The emerging network-softwarization technologies, such as software-defined networking and network function virtualization play important roles in 5G communication and future networks. One of the critical challenges of the practical application of the softwarized networks is to appropriately place virtual network functions (VNFs). The underlying resources and traffic requirements are often factored in the previous works of VNF placement. However, VNFs’ dynamic abilities of changing traffic are usually ignored. Resources allocated to VNFs can vary their traffic-change ratios, and the adjustment of resource volumes should be provisioned to support traffic changes. In this work, we pay attention to the joint optimization problem of VNF Placement, CPU Allocation, and flow Routing (VNFPAR) in the scenarios consisting of VNFs that can dynamically change traffic. We employ the logarithmic functions to approximate VNFs’ traffic change relations and formulate VNFPAR as a mixed-integer nonlinear programming (MINLP) problem. We demonstrate that this problem is highly nonconvex and involves highly coupled variables. For small-scale VNFPAR problems, we propose an optimal algorithm based on relaxation and programming to consume the minimum bandwidth resources. Because VNFPAR is NP-hard, to quickly find near-optimal solutions for large-scale VNFPAR problems, we present heuristic algorithms based on multistage greedy and simulated annealing, respectively. Besides, to achieve a tradeoff between solution quality and execution time, we decompose VNFPAR into subproblems and design an alternating optimization-based method. We evaluate our algorithms on real-work topologies and traffic patterns. Extensive simulations show that our proposed heuristic algorithms are convergent, stable, and effective in terms of solving VNFPARs. The proposed algorithms have small optimality gaps within 7.4%–26.3%. Meanwhile, they save 39.3%–48.4% bandwidth resources compared with relevant baseline technologies.

The virtual network functions (VNFs) placement problem has drawn significant attention from both academia and industry in recent years. Most of the researchers have ignored the fact that the probability of traffic flows through VNFs cannot always be 100%. In this paper, we study the placement scheme for virtual network function considering randomized data traffic (VNFPRAT). Our objective is to determine optimal deployment locations for VNFs and minimize total end-to-end delay. We formulate the VNFPRAT problem as a 0–1 nonlinear programming problem and prove its NP-hardness. This formulation is linearized to obtain the optimal solution for small scale networks. Besides, two efficient metaheuristics, i.e., greedy and simulated annealing, are proposed quickly find a near-optimal placement solution. Extensive simulations demonstrate that our proposed approach achieves 38.8% less end-to-end delay than the generic algorithm.

Due to the global coverage and real-time access advantages of the Tracking and Data Relay Satellite System (TDRSS), the demonstration platform based on TDRSS can satisfy the new technology verification and demonstration needs of the space information network (evolution from sensorweb). However, the comprehensive evaluation research of this demonstration platform faces many problems: complicated and diverse technical indicators in various areas, coupling redundancy between indicators, difficulty in establishing the number of indicator system layers, and evaluation errors causing by subjective scoring. Concerning the difficulties, this paper gives a method to construct this special index system, and improves the consistency of evaluation results with Analytic Hierarchy Process in Group Decision-Making (AHP-GDM). A comprehensive evaluation index system including five criterions, 11 elements, more than 30 indicators is constructed according to the three-step strategy of initial set classification, hierarchical optimization, and de-redundancy. For the inconsistent scoring of AHP-GDM, a high-speed convergence consistency improvement strategy is proposed in this paper. Moreover, a method for generating a comprehensive judgment matrix (the aggregation of each judgment matrix) aggregation coefficient is provided. Numerical experiments show that this strategy effectively improves the consistency of the comprehensive judgment matrix. Finally, taking the evaluation of TDRSS development as an example, the versatility and feasibility of the new evaluation strategy are demonstrated.

The continuous development trend of expressways urgently needs expressway sensing, which is required to be able to perceive unknown risk factors to ensure a safe and stable transportation environment as well as effectively and continuously monitor the safe operation of expressways. Using unconscious payment can greatly improve traffic efficiency and meet the development direction of intelligent transportation. However, charging errors occur when using unconscious payment, primarily because the expressway network fails to realize national networking. Most of the literature focuses on two aspects of sensing, transmission and computing, while research on the overall network architecture is lacking. A ground network cannot solve the network coverage problem in remote areas. Therefore, the basic infrastructure needed to realize expressway sensing includes both aerial and ground nodes, such as cars, roadside equipment, unmanned aerial vehicles, airships, and satellites. This network communication architecture requires effective management of infrastructure and support of various applications. The software-defined network has strong flexibility; thus, in this paper, we propose a software-defined space-ground-vehicle integrated network (SD-SGVIN) architecture that includes three layers: physical facility, logic control, and application. The SD-SGVIN can separate the highway sensing applications from the physical entity. All kinds of applications can acquire, transmit, and process vehicle data through a shared physical infrastructure. We discuss the design, implementation, and advantages of the proposed network structure, hoping to provide a new direction for the research and promotion of expressway sensing and unconscious payment.