In the domain of mineral resource exploration, a pronounced imbalance often exists between the number of mineralized samples and the number of samples that are found to be non-mineralized. To address this imbalance, this paper proposes an AC-CTGAN method for expanding mineralized samples based on generative adversarial networks, utilising the northeast Guizhou manganese mining area as a case study. The findings of the research demonstrate that the data generated by AC-CTGAN exhibits excellent performance in terms of spatial distribution and bears a strong resemblance to the feature distribution of real data. The average cross-validation accuracy of the prediction model trained using the expanded dataset is improved by 8%, and the prediction ability of the model is significantly enhanced.

Mobile Edge Computing (MEC) supports ultra-low latency and high-bandwidth services as an emerging network architecture by deploying servers at the edge of the network to provide computing and storage resources. Along with the MEC technology, Network Function Virtualization (NFV) provisions Service Function Chains (SFC) on MEC servers to improve user service experience and achieve fast access to the mobile user. However, users are constantly moving in the edge network, and different users usually have different delay requirements for service requests. To guarantee the QoS of mobile users, it is necessary to migrate SFCs to an advisable edge server when users move across Base Stations (BS). This paper focuses on the SFCs reconfiguration scheme with resource capacity constraints in the MEC network to support the seamless migration of mobile user services. We first formalize the SFCs reconfiguration problem of the edge network as a mathematical model, which aims to minimize the end-to-end delay and operating costs of user services. Then, we convert the problem into an equivalent shortest path problem and design a Dynamic Programmingbased SFC Migration algorithm (DPSM). Finally, we conduct simulation experiments to evaluate the performance of the algorithm based on a real-world dataset. The experiment results show the effectiveness and efficiency of our algorithm.

Network Function Virtualization (NFV) is a promising paradigm that separates network functions from proprietary devices. Network service in NFV-enabled networks is achieved as a Service Function Chain (SFC), consisting of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within dynamic networks is a key challenge. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF Instance (VNFI). In this paper, we assume that each deployed VNFI is used by multiple SFCs and deal with the optimal location allocation for the concurrent migration of VNFIs based on the actual network situation. We first formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the end-to-end delay for all affected services and to guarantee network load balancing after the migration simultaneously. To this end, we prove the NP-hardness of this problem and propose the Improved Hybrid Genetic Evolution (IHGE) algorithm to address it. Besides, to reduce the computation overhead of IHGE for large-scale networks, a multi-stage heuristic algorithm based on optimal order (MSH-OR) is designed. Finally, we perform a side-by-side comparison with prior algorithms. Extensive evaluation shows that the proposed approaches can effectively reduce the average delay for different scale networks while ensuring network load balancing.

Since the advent of network function virtualization (NFV), cloud service providers (CSPs) can implement traditional dedicated network devices as software and flexibly instantiate network functions (NFs) on common off-the-shelf servers. NFV technology enables CSPs to deploy their NFs to a cloud data center in the form of virtual network functions (VNFs) without costly capital expenditures and operating expenses. However, it is an essential but intractable issue for CSPs to devise a suitable VNF placement scheme to optimize network resource consumption and improve network performance. In this article, we focus on the VNF placement problem for mapping users’ service function chain requests (SFCRs) in cloud networks. To enhance network resource utilization, we consider the fundamental resource overheads and implementation method of VNFs. The VNF placement problem is formulated as an integer linear programming model with the aim of minimizing the total network resource consumption while guaranteeing the delay requirements of SFCRs. We devise a two-phase optimization solution (TPOS) to solve the problem. TPOS contains a mapping phase to map SFCRs on servers and an adjustment phase to optimize the placement of VNFs and VNF requests. Evaluation results demonstrate that TPOS can derive near-optimal server resource consumption and significantly enhance network resource utilization. TPOS can guarantee the delay requirements of SFCRs and outperform contrastive schemes in terms of activated servers, SFCR acceptance ratio, and average VNF utilization.

Nowadays, network softwareization is an emerging techno-economic transformation trend that significantly impacts how enterprises deploy their network services. As an essential technology in this trend, Network Function Virtualization (NFV) enables scalable and inexpensive network services by flexibly instantiating Virtualized Network Functions (VNFs) on commercial-off-the-shelf devices. In this paper, we focus on the VNF placement problem in NFV-enabled networks, aiming to maximize the number of accepted Service Function Chain Requests (SFCRs) while guaranteeing their delay requirements. To improve resource utilization efficiency, we take account of Fundamental Resource Overheads (FROs) and the shareability of VNF instances. We mathematically formulate the VNF placement problem and propose the Throughput Optimization and Delay Guarantee (TO-DG) heuristic solution, consisting of an affinity-based SFCR mapping algorithm and a VNF request adjustment algorithm. The evaluation results show that the performance of TO-DG is near to results derived by ILP solver for small scale problems. Moreover, TO-DG obtains higher network throughput than contrasting schemes in different scenarios and significantly improves network resource utilization.

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within the dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF Instance (VNFI). In this paper, we assume that each deployed VNFI is used by multiple SFCs and deal with the optimal location allocation for the contemporaneous migration of VNFIs based on the actual network situation. We first formalize the VNFI migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the end-to-end delay for all affected SFCs and to guarantee network load balancing after the migration simultaneously. Then, we prove the NP-hardness of this problem and propose a multi-stage heuristic algorithm based on optimal order (MSH-OR) to solve it. Extensive evaluation shows that the proposed approach can reduce the average delay by about 16% - 25% for different scale networks while ensuring network load balancing compared with the previous algorithms.

Mobile Edge Computing (MEC) is an emerging architecture that supports ultra-low latency and high-bandwidth services by deploying servers at the edge of the network to provide computing and storage resources. Recent studies tend to combine (Network Function Virtualization) NFV with MEC and deploy (Virtualized Network Functions) VNFs on MEC servers to achieve fast access to the edge user equipment (UE). However, to guarantee the QoS requirements of mobile users, it is necessary to migrate VNFs to an advisable edge server when users move across Base Stations (BS). How to choose the target BS for VNFs migration? How to select the path for VNF data migration? How to ensure the QoS of user services during the migration process? To solve these issues, we study the seamless VNFs migration problem in mobile edge networks and formulate it as an ILP model, which aims to minimize the migration delay and cost. Then we propose a migration algorithm based on Dijkstra (MBD) to obtain the migration destination BS and migration paths. We implement the mathematical model in Gurobi and design a Greedy algorithm to compare the performance with the MBD algorithm. The experiment results show the effectiveness and efficiency of our algorithm.

Network function virtualization (NFV) and mobile edge computing (MEC) enable internet service providers (ISPs) to deploy service function chains (SFCs) to achieve the convenience and performance benefit without incurring high service delay, capital expenditures, and operating expenses. In MEC-NFV networks, network services are deployed in the form of service function chains (SFCs), each consisting of an ordered set of virtual network functions (VNFs). In this paper, we focus on the VNF placement problem in MEC-NFV enabled networks, aiming to optimize the throughput of SFC requests (SFCRs). First, we involve the sharing mechanism of VNF instances in the problem formulations, which can improve network resource utilization and save more node resources. Then we formulate the problem mathematically and propose a correlation-based mapping algorithm to map SFCRs in the network. Moreover, we design an adjustment algorithm to optimize the mapped SFCRs. Evaluation results show that our proposed solution efficiently improves the throughput of SFCRs compared with the benchmarks.

With the increasing scale of cloud datacenters and rapid development of virtualization technologies, many cloud-based services have been deployed to meet requirements. Virtual machines (VMs) are placed on physical servers, and often provide virtual environment for cloud services. Therefore, virtual machines placement (VMP) problem has gradually attracted many attentions. It is meaningful that how to effectively and efficiently place VMs on servers to guarantee the service reliability and reduce the bandwidth consumption. In this paper, we first formulate VMP with a reliability model and a bandwidth consumption model, and analyse its complexity. Then we propose a VMP optimization approach to solve the problem and prove its effectiveness and efficiency. The core algorithm of our approach is an approximation algorithm to get VM partitions under the constraint of a specified reliability parameter. Then placement problem is transformed into matching problem between VM partitions with physical servers. Finally, the evaluation results show the effectiveness of the proposed approach and performance advancement over the existing approaches.

Network Function Virtualization (NFV) brings great flexibility and scalability to the deployment of network services by decoupling network functions from dedicated devices, which has attracted more attention from both academia and industry. Network services in NFV are deployed in the form of Service Function Chain (SFC), which consists of multiple ordered Virtual Network Functions (VNFs). However, how to effectively place VNFs remains a problem to be solved. In this paper, we investigate joint correlation-aware VNF selection and placement problem. We first formulate the problem as an Integer Linear Programming (ILP) problem and propose a method based on self-learning matrix to partition VNF correlation. Then, we design a Joint Correlation-aware VNF Placement (JCVP) algorithm based on Dynamic Programming to transform the problem into several VNF mapping subproblems. Extensive simulation results show that compared with the previous algorithms our approach has better performance in link occupancy, SFC acceptance, and VNF utilization rate.

Although network function virtualization (NFV) is a promising approach for providing elastic network functions, it faces several challenges. A critical but difficult issue for the service and network providers is deciding where to instantiate a list of virtual network functions (VNFs), namely VNF placement problem. In this paper, we investigate the VNF placement, for the purpose of resource and network traffic consumption minimization. Moreover, we consider the arrival rates of users' requests for different types of service function chains (SFCs). This allows the placement scheme to adapt to users' time-varying requests and improve the network resource utilization. Then we formulate the VNF placement problem as a jointly constrained optimization problem. Afterwards, we propose an approach called joint optimization resource and traffic consumption (JORTC) with enhanced biogeography-based (EBBO) optimization algorithm to resolve the VNF placement problem. Finally, the evaluation results show the effectiveness of J-ORTC approach and performance advancement over the benchmarks.

Network Function Virtualization (NFV) is an emerging technology decouples network functions from hardware. Network service in NFV is deployed as a service chain, also known as Service Function Chain (SFC). SFC consists of an ordered set of Virtual Network Functions (VNFs). However, VNFs bring new challenges in providing network services with availability guarantee. In addition, in a customizable and dynamic NFV-enabled network, the composition and mapping of service chain are different from that of a traditional network. In this paper, we define an availability model that takes both hardware and VNF failures into consideration. Then we propose Joint Path-VNF backup model to combine path and VNF backup in a joint way. And a priority-based algorithm is designed for service chain composition and mapping. Simulation results show that our proposed solutions can reduce resource consumption while guaranteeing availability.

With the explosive growth of large scale services, traditional mobile networks have become increasingly unable to guarantee the efficient operation of services. However in the fifth generation (5G), supported by Software Defined Network (SDN) and Network Function Virtualization (NFV), network slicing technology [1] makes mobile networks more intelligent and flexible. 5G network slicing allows a set of logically independent virtual networks to be created on a common physical infrastructure and provides appropriate monitoring, management and resource allocation for a variety of different types of communication services [2].

Network Function Virtualization (NFV) is a new network paradigm that decouples network functions from dedicated hardware. Network services in NFV are deployed as service chains, also known as Service Function Chains (SFCs). SFC consists of an ordered set of Virtual Network Functions (VNFs). One of the main challenge when deploying SFC is to efficiently make use of the resource. In this paper, we focus on the SFC composition and mapping considering resource optimization. We formulate the SFC composition and mapping problem as a weighted graph matching problem. Then we propose a Hungarian based algorithm to solve the SFC composition and mapping problem in a coordinated way.

With the emergency of IoT, there are many IoT network slices with different network requirements. Most of the current IoT system are specific and non-programmable and therefore their slices are difficult to reuse. It is difficult to meet different QoS requirements especially in IoT system because there are plenty of IoT sensors in IoT system. In this paper, we propose a novel IoT network slicing creation system which based on two emerging SDN and NFV technologies. It provides an easily-operating service creation environment and a service execution environment based on micro service architecture. We implement an IoT muti-flow transmission scenario. After adding subservices and QoS policies into a business process at the design plane, the IoT scenario can run automatically at the execution plane. Experiment results on the scenario show that the numbers of packets per second of different flows are changing gradually depend on QoS policies.