Network Function Virtualization Technologies Research Articles

Efficient and secure service function chain deployment method for delay optimization in air traffic information network

The air traffic information network is fundamental for ensuring the efficient and safe operation of flights. However, with the increasing demands of air traffic services, the rigidity of the traditional air traffic information network has become increasingly pronounced. Network function virtualization (NFV) technology presents an effective solution to address this issue. Within the NFV environment, the deployment of service function chains (SFC) forms the basis for achieving end-to-end air traffic services. This paper addresses the low delay and high security requirements inherent in air traffic services and proposes an efficient and secure SFC deployment for delay optimization (ESSFCD-DO) method. Primarily, the paper conducts a theoretical analysis of SFC deployment, and derives four key principles related to SFC delay, security, and resource cost. Subsequently, based on these principles, the ESSFCD-DO method is developed, which encompasses the virtual network function (VNF) aggregation algorithm for security and resources cost optimization (VNFAA-SRCO), the VNF deployment algorithm based on topology aware (VNFD-TA), and the algorithm for deploying virtual links based on k-shortest path. Experimental results demonstrate that, in comparison to other SFC deployment methods within this domain, ESSFCD-DO achieves significant optimizations in terms of end-to-end delay of SFC and long-term revenue-cost ratio, while ensuring security and service request acceptance rate.

Efficient SFC Protection Method against Network Attack Risks in Air Traffic Information Networks

With the continuous development of the civil aviation industry toward digitalization and intelligence, the closed architecture of traditional air traffic information networks struggles to meet the rapidly growing demands for air traffic services. Network function virtualization (NFV) is one of the key technologies that can address the rigidity of traditional air traffic information networks. NFV technology has facilitated the flexible deployment of air traffic services, but it has also expanded the attack surface of the network. In addressing the network attack risks faced by service function chains (SFCs) in NFV environments, a SFC protection method based on honeypots and backup technology (PBHB) is proposed to reduce the resource cost of protecting air traffic information networks while enhancing network security. Initially, PBHB utilizes the TAPD algorithm to deploy the primary VNFs as closely as possible to the shortest path between the source and destination endpoints, thus aiming to reduce SFC latency and save bandwidth resource costs. Subsequently, the RAHDR algorithm is employed to install honeypot VNFs in each physical platform that is at risk of side-channel attacks, thus updating the deployment status of honeypot VNFs in real time based on the VNF lifecycle in order to offer primary protection for SFCs. Lastly, the BDMPE algorithm was used to calculate the backup scheme with the highest protection efficiency to implement secondary protection for the SFCs that still do not meet the security requirements. Through experiments, the maximum backup limit for SFCs in PBHB was determined, confirming its satisfactory performance across various SFC arrival rates. Furthermore, performance comparisons with other SFC protection methods revealed that PBHB achieves optimizations in resources cost while ensuring SFC security and latency.

Bi-LSTM/GRU-based anomaly diagnosis for virtual network function instance

In edge–cloud networks, Network Function Virtualization (NFV) technology provides users with high-performance Virtual Network Functions (VNFs) to replace dedicated hardware devices. In this paper, we focus on the VNF instance (VNFI) anomaly diagnosis (AD). In large-scale production data centers, VNFI anomalies are inevitable, causing degradation in network service performance, while accurate VNFI AD can minimize the losses. The AD task can be abstracted as a multi-classification problem, where the VNFI state is classified as normal or a specific fault type. Existing studies have not proposed mature VNFI AD systems and feature selection criteria, and the accuracy of AD tasks can still be improved. To address these gaps, we first design the Distributed Online Anomaly Diagnosis and Broadcasting (DOADB) system, providing online VNFI AD daemons, offline parameter training, and anomaly broadcasting. Then, we establish VNFI feature selection criteria and construct a compelling VNFI feature dataset containing simulated and real-time data for each VNFI. Based on the DOADB system and feature dataset, we adopt the Bi-LSTM and Bi-GRU networks to realize the AD multi-classification algorithm, leveraging the bidirectional network structure and long-term memory to enhance AD accuracy. Experimental results demonstrate that the proposed system and algorithm can provide effective AD daemons for VNFIs and outperform existing algorithms in both accuracy and macro F1-score metrics.

Digital twin-assisted service function chaining in multi-domain computing power networks with multi-agent reinforcement learning

The emerging computing power network (CPN) is believed to undergo the paradigm reformation of network function virtualization (NFV) and service function chaining (SFC). It is prerequisite to explore the performance upper bound of NFV-assisted CPN before truly deploying the NFV and SFC technologies onto physical networks. Inspired by the application of digital twin (DT) in the industry and due to its advantage in synchronizing physical objects with their virtual replicas, we propose to use the DT to assist the SFC deployment in the multi-domain CPN, with the aid of multi-agent deep deterministic policy gradient (MADDPG) framework. First, we build a dynamic SFC mapping problem in the virtual twin network layer, by modeling the computing power, link bandwidth, delay performance and the VNF ordering as DT objects and constraints, to jointly optimize the energy consumption, end-to-end delay and the VNF re-deploying cost. Then, the prioritized experience replay and re-parameterization trick-empowered centralized training and decentralized execution MADDPG framework is utilized to learn the SFC deployment, by taking each domain controller as one agent. Finally, numerical experiments are carried out to validate the effectiveness of MADDPG in the cross-domain SFC deployment. For performance verification, the deployment success rate, number of crossed domains, energy consumption, end-to-end latency and load balancing degree are all taken as metrics, to show the performance of MADDPG compared to other learning frameworks.

Implementing Internet of Things Service Platforms with Network Function Virtualization Serverless Technologies

The need for adaptivity and scalability in telecommunication systems has led to the introduction of a software-based approach to networking, in which network functions are virtualized and implemented in software modules, based on network function virtualization (NFV) technologies. The growing demand for low latency, efficiency, flexibility and security has placed some limitations on the adoption of these technologies, due to some problems of traditional virtualization solutions. However, the introduction of lightweight virtualization approaches is paving the way for new and better infrastructures for implementing network functions. This article discusses these new virtualization solutions and shows a proposal, based on serverless computing, that uses them to implement container-based virtualized network functions for the delivery of advanced Internet of Things (IoT) services. It includes open source software components to implement both the virtualization layer, implemented through Firecracker, and the runtime environment, based on Kata containers. A set of experiments shows that the proposed approach is fast, in order to boost new network functions, and more efficient than some baseline solutions, with minimal resource footprint. Therefore, it is an excellent candidate to implement NFV functions in the edge deployment of serverless services for the IoT.

Virtualization of a 4G Evolved Packet Core Network Using Network Function Virtualization (NFV) Technology with NS3 for Enterprise and Educational Purpose

The current networks of many operators dispose an increasing variety of purpose built, vertically integrated, and vendor-locked hardware equipment. This makes it difficult to easily scale their radio access and core networks as it requires yet another variety of equipment, leading to increased Time-to-Market and inefficient resource utilization. Furthermore, network equipment are expensive to procure and upgrade (increased CAPEX), and are difficult to adapt and program for new services (increased OPEX). These trends have recently spurred several efforts to redesign various components of mobile networks, notably the 4G Evolved Packet Core (EPC). With regards to this, the present work proposes a solution based on Network Function Virtualization (NFV), which can be deployed for educational or enterprises purpose like in Data Centers, or network nodes with a fine-grained QoS, while maintaining the scalability of the virtualized network function entities, and optimum resource utilization. The actual work develops a mobile network simulation module centered on the 4G Core network comprising its major components; MME, SGW, and PGW (vEPC), as well as the networking amongst these entities, core network related aspects such as its interaction with the Enhanced Radio Access Network (E-UTRAN) and the Packet Data Network Services (Internet). This solution gives to core network engineers and EPC network agents the possibility to design, analyze and test variable types of network scenarios. The solution can be also integrated in engineering schools and college for labs’ practical work or to any e-laboratory initiative to allow students in virtual environment to analyze signaling, modify the network configuration and better understand some theoretical concepts taught during the courses. The results obtained from the present work can be of great help during the final deployment phase of the network for full production for engineers of carriers’ network or improve the engineering understanding of core network in academic domain. In this light, the network can easily be scaled, adjust virtually for the time to commercialize a service reduced for carrier or enterprises’ solutions or for testing in e-laboratories.

Performance evaluation of a firewall service based on virtualized IncludeOS unikernels

Network function virtualization technology has long moved beyond the experimental phase to become a standard in the implementation of modern telecommunications networks. It is anticipated that in the near future all network services will be implemented in software based on cloud-native architecture. As a result, telecommunications service providers have started exploring containers and unikernels as alternative technologies to traditional virtual machines. This paper presents performance evaluation of a firewall service based on IncludeOS unikernels. It shows that IncludeOS unikernels achieve promising performance results compared to Ubuntu-based virtual machines and containers. The presented evaluation is based on a number of experiments and benchmarks performed to investigate how different parameters of a firewall service change depending on the number of firewall rules.

Digital Twin-Assisted, SFC-Enabled Service Provisioning in Mobile Edge Computing

Mobile Edge Computing (MEC) has been identified as a desirable computing paradigm that provides efficient and effective services for various applications, while meeting stringent service delay requirements. Orthogonal to the MEC computing paradigm, Network Function Virtualization (NFV) technology is another enabling technology that provides the network resource management with great flexibility and scalability, where the instances of Virtual Network Functions (VNFs) are deployed in edge servers as Service Function Chains (SFCs) for SFC-enabled services. Although reliable service provisioning in MEC environments is fundamentally important, the deployed VNF instances usually are not reliable, which can be affected by their software implementation, their execution duration, the workload among edge servers, and so on. Empowered by digital twin techniques, the states of VNF instances can be maintained by their digital twins in a real-time manner and their reliability can be accurately predicted through their digital twins. In this paper, we study digital twin-assisted, SFC-enabled reliable service provisioning in MEC networks by exploiting the dynamics of VNF instance reliability. We concentrate on two novel optimization problems of reliable service provisioning: the service cost minimization problem, and the dynamic service admission maximization problem. We first show their NP-hardness. We then formulate an Integer Linear Program (ILP) solution, and devise an approximation algorithm with a constant approximation ratio for the service cost minimization problem. We thirdly provide an ILP solution to the offline version of the dynamic service admission maximization problem. Built upon this offline ILP solution, we also develop an online algorithm with a provable competitive ratio for the problem, by adopting the primal-dual dynamic updating technique. We finally evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the proposed algorithms outperform their comparison benchmarks, and improve the performance of their comparison counterparts by no less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10.2 \%$</tex-math></inline-formula> .

Dynamic service function chains placement based on parallelized requests in edge computing environment

AbstractThe advent of Network Function Virtualization (NFV) technology brings flexible traffic engineering to edge computing environments. Online services in NFV are chained as Service Function Chains (SFCs), which consist of ordered sequences of Virtual Network Functions (VNFs). The SFC Placement (SFCP) problem is solved under Quality of Service (QoS) requirements and limited resource availability by directing traffic to the required VNFs. However, SFC assembly leads to high latency and network congestion by increasing the count of VNFs, which parallelized SFC can overcome this problem. With parallelizing an SFC request, independent VNFs are activated simultaneously and computational acceleration is realized by reducing the SFC length. Any pair of VNFs that do not conflict with traffic can be activated simultaneously. Most VNFs are deployed on distributed servers for load balancing, which makes SFC parallelization challenging. Meanwhile, the cost of merging/duplicating packets for parallelized SFCs between different servers is not negligible. Hence, in this article, Distributed Parallel Chaining (DPC) is proposed which is an algorithm based on Deep Reinforcement Learning (DRL) approaches. The DPC algorithm solves the SFCP problem to maximize the Long‐Term Expected Cumulative Reward (LTECR). DPC incorporates an Asynchronous Advantage Actor‐Critic (A3C) algorithm as a new approach based on DRL to increase the admission ability of future SFC requests by maximizing LTECR. The evaluation results show the effectiveness of the proposed algorithm from different aspects. Specifically, compared to the best existing approaches, DPC can reduce SFC latency by 8.7%.

Understanding NFV-Enabled Vehicle Platooning Application: A Dependability View

This paper aims to use analytical modeling technique to quantitatively study the dependability of Vehicle Platooning Application, which consists of Multiple Sub-Services (VPP-MSS) to achieve its functionality. Each sub-service (SS), based on network function virtualization technology, is executed in a container. Both SSes and OSes which SSes run on can suffer from software aging after a long and continuous running, reducing VPP-MSS dependability. Rejuvenation techniques are usually used to combat software aging, but they require the support of backup components. Quantitative study of VPP-MSS dependability enables in-depth understanding of the effectiveness of rejuvenation techniques based on analytical models. In contrast to the existing studies, we develop a semi-Markov process (SMP) model to jointly analyze the impact of rejuvenation technique trigger intervals (RTTIs), backup components' behaviors, time-dependent interactions between various behaviors and the number of active SSes deployed on an OS on the effectiveness of rejuvenation technique. Sensitivity analysis helps identify key parameters for improving the dependability of VPP-MSS. Extensive numerical experiments demonstrate the necessity of considering backup components' behaviors and investigating non-exponentially distributed failure times. We also determine both the optimal RTTI combination and the optimal combination of SSes and OSes, which can maximize VPP-MSS dependability.

Network-Compute Co-Optimization for Service Chaining in Cloud-Edge-Radio 5G Networks

Flexible resource management can be facilitated by recent drastic advances in software-defined networking (SDN) and network function virtualization (NFV) technologies. Most of recent studies on resource management have mainly focused on the independent optimization of network function and computing function on top of a single-layer SDN architecture. In this paper, we focus on networking and processing resource orchestration to enhance the performance of network/computing functions in a hierarchical cloud-edge-radio 5G network architecture. Specifically, we propose a dynamic resource management algorithm, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RACER</i> , to process network/computing functions by opportunistically exploiting a tradeoff between the characteristics of the cloud and edge with respect to the amount of available resources and transmission delay. Herein, we decompose an original long-term problem including complex decisions in all network layers (that is, cloud, edge, and radio) into spatio-temporal subproblems without loss of optimality by leveraging Lyapunov optimization. Then, we derive the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RACER</i> algorithm to find the decisions in each layer and each time slot. This online-fashioned algorithm is pragmatic because it not only exploits information obtained from instantaneous time slots, but also operates in a distributed manner with small message exchanges between the different layers. Moreover, we demonstrate the excellent performance improvement of the proposed dynamic and holistic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RACER</i> algorithm compared to static and independent policies in the 5G network environment.

Dynamic service function chain placement with instance reuse in Fog–Cloud​ Computing

The advent of Network Function Virtualization (NFV) technology has brought flexible provisioning to Fog–Cloud Computing-based Networks (FCCNs) for enterprises to outsource their network functions to data center networks. Service Function Chaining (SFC) is a networking concept in NFV by which traffic is steered through an ordered set of Virtual Network Functions (VNFs) composing an end-to-end service. When hundreds of users outsource their network functions to FCCN, the optimal placement of VNFs in the network becomes important for assembling SFCs with the aim of resource utilization efficiency. Motivated by the scalability shortcomings of existing schemes, we propose Deep Reinforcement Learning (DRL)-based approaches by simultaneously considering parallelized SFC and reuse of VNFs to solve this problem, i.e., Asynchronous Advantage Actor–Critic (A3C). A parallelized SFC consists of several sub-SFCs, which can reduce delay and guarantee availability. Also, reuse of preliminary VNFs in SFC placement can improve computation acceleration. The proposed scheme pursues the maximization of the long-term cumulative reward for the trade-off between Quality of Service (QoS) and service cost. The results of the experiments show that the proposed scheme performs better than the state-of-the-art methods.

SFC-based multi-domain service customization and deployment

In order to satisfy diversified service demands, future networks are expected to provide higher quality network services, especially in 5G scenarios. Network Function Virtualization (NFV) is a promising architecture to achieve flexible service provisioning by introducing Service Function Chain (SFC) technology. With explosive expansion of network devices and end users, the complex multi-domain network environment and personalized service demands of different users pose serious challenges to efficient service provisioning. To this end, this paper presents a novel SFC-based Multi-domain Service Customization and Deployment framework, MSCD, by blending merits of Software Defined Networking (SDN) and NFV technologies. MSCD framework can automatically customize multi-domain network services based on users’ personalized service preferences, and deploy them in multi-domain networks, to provide customizable multi-domain services for different users. Taking into account user personalized service preferences, resource allocation and VNF dependency constraints, the multi-domain service customization and deployment problem is mathematically formulated as an optimization model with the target of service satisfaction degree maximization. Furthermore, a Greedy strategy based Heuristic Service Customization and dePloyment (GHSCP) algorithm is proposed to customize and deploy the multi-domain services efficiently. Simulation results demonstrate that the proposed GHSCP algorithm is efficient and outperforms comparison algorithms in terms of service acceptance ratio and service satisfaction degree.

Salience-based VNF placement method for evolving multiple network slices

Network Function Virtualization (NFV) technology enables the deployment of a distinct network slice composed of various virtualized network functions (VNFs) required for offering a communication service. As the user population or network traffic volume fluctuates, the capacity of the network slices must be dynamically adjusted. It is necessary to decide whether to place VNFs in appropriate locations such that the quality of services (QoS) is guaranteed while optimally utilizing the allocated resources. The above challenge can be addressed by solving an optimization problem, such as integer linear programming. However, as solving the optimization problem requires a considerable amount of time, it is not applicable in agile network control mechanisms. In this paper, we propose a network function placement algorithm based on salience, which is one of the characteristic metrics of links in a graph. Our proposal is aimed to provide the adequate placement in short time to achieve the following objectives of keeping the slice reconstruction cost low and avoiding the cases of QoS degradation. The simulation results show that our scheme performs equally well for different sized slices (i.e., the user population). We found that the network reconfiguration costs are low and almost the same for various sized slices when they are reconstructed by the proposed method in a substrate network of 200 nodes. Furthermore, we confirmed that the proposed method keeps the slice reconstruction cost low and avoids blocking of slice reconstruction even in the situations when a resource competition occurs among multiple slices.

Time-efficient distributed virtual network embedding for round-trip delay minimization

Virtual Network Embedding (VNE) aims at determining the placement of virtual services both internally in the edge/cloud infrastructure (EC) and distributed, among distinct ECs, under several resource and network constraints. With regard to Network Function Virtualization technology, a virtual service is referred to as Virtual Network Function (VNF). Modern 5G applications related to the Internet of Things (IoT) are deployed as interconnected VNFs with specific execution sequence in the form of a Service Function Chain (SFC), composing a Virtual Network (VN), and the execution result has to be returned back to the original end-device. Thus, the minimization of the round-trip delay is of major importance to guarantee the performance requirements of IoT-based applications. Additionally, in such a dynamic environment, the re-optimization of VNE is often required to meet the network delay requirements due to end-device mobility, and other orchestration constraints related to the limited edge resources. In this paper, we propose a distributed VNE (DVNE) approach, which focuses on minimizing the round-trip delay. Initially, we introduce an algorithm, namely sV-VNM, that associates every VNF of the VN with a set of candidate ECs that can be hosted onto. Given this initial mapping, an efficient path-based algorithm, namely kSP-DVNE, is proposed to provide the DVNE solution, in polynomial time. Evaluation results show that the sV-VNM algorithm maps the VN onto the ECs, with decreased resource utilization within the infrastructure, while the kSP-DVNE achieves optimal or near optimal solutions regarding the round-trip delay minimization, with a significant reduction in the execution time, compared to existing relevant approaches in the literature.

Towards deploying SFC with parallelized VNFs under resource demand uncertainty in mobile edge computing

The increasing demand in Mobile Edge Computing (MEC) networks has led to the emergence of Network Function Virtualization (NFV) technology. Through the virtualization technique, NFV can decouple each Virtual Network Function (VNF) from the hardware and deploy it flexibly in the MEC. Each service request in NFV assembles a Service Function Chain (SFC) by tying different VNFs and processes the traffic in the chained order. How to allocate resources and deploy SFC in MEC to meet Quality of Service (QoS) requirements is an important challenge for NFV. An increase in the number of VNFs with traffic passing one after the other causes high delay in SFC assembly. Also, the demanded resources may change during SFC assembly, which is clearly neglected in existing works. Therefore, high delay and resource demand uncertainty during SFC assembly affect QoS. With this motivation, we solve the SFC deployment problem by Parallelizing VNFs under Resource Demand Uncertainty (PVRDU) in MEC. We use the dependency between VNFs to transform the original SFC into a parallel SFC and then assemble several sub-SFCs based on a Deep Reinforcement Learning (DRL) approach. Meanwhile, PVRDU uses a Markov-based approximation algorithm to handle resource demand uncertainty. We perform extensive trace-driven simulations to verify the effectiveness of the proposed algorithm. The evaluation results of PVRDU are promising in different aspects compared to state-of-the-art methods. Specifically, for the considered scenario, DPC reduced SFC delay by as much as 8.7%.

Online and reliable SFC protection scheme of distributed cloud network for future IoT application

The implementation of intelligent device-free sensing (IDFS) necessitates a substantial number of edge IoT devices to extend the service coverage. Prior to deploying these IoT applications on a large scale, a reliable service provision scheme is imperative. In recent years, network function virtualization (NFV) technologies enable fast and convenient service provision by instantiating service function chain (SFC) in the distributed cloud network. However, ensuring resilient service provision is a challenge in NFV environment due to the vulnerability of software-defined network functions. Furthermore, the sequential arrangement of virtual network functions (VNFs) in SFC exacerbates the situation, as the malfunction of any VNFs within the SFC could result in the collapse of the associated service. In this paper, we firstly built a mathematical model for SFC online reliability protection and resource consumption optimization. We then propose a multi-mode VNF backup scheme to guarantee the different reliability requirements for various services. Furthermore, a novel SFC Online Reliability Protection (SORP) scheme is introduced to handle the dynamic SFC requests in distributed cloud network. The simulation results evince the efficacy of the SORP scheme in mitigating service failures and achieving a superior request acceptance ratio, while concurrently reducing average bandwidth consumption, which is particularly noteworthy in high latency scenarios.

Dynamic SFC placement scheme with parallelized SFCs and reuse of initialized VNFs: An A3C-based DRL approach

Mobile Edge Computing (MEC) is a well-known network architecture that extends cloud computing to the network edge. Compared with cloud computing, Network Function Virtualization (NFV) can provide flexible services in MEC for mobile users. Virtual Network Functions (VNFs) have emerged as software-based hardware middleboxes by NFV technology to host real-time applications. Basically, the combination of multiple VNF instances is defined as a Service Function Chain (SFC), which can provide dynamic service requirements in the MEC. Despite the rapid growth of MEC and the widespread support of service providers for SFC, many issues are still challenging and need to be addressed. In MEC scenarios with limited resources, the effective placement of SFCs with the aim of resource efficiency remains a challenging problem. Motivated by the scalability shortcomings of existing schemes to solve dynamic placement of SFCs, we propose Deep Reinforcement Learning (DRL)-based approaches to solve this problem, i.e., Asynchronous Advantage Actor-Critic (A3C). The proposed scheme is based on the reuse of initialized VNFs to improve the Quality of Service (QoS), which is developed with the aim of maximizing the long-term cumulative reward. In addition, a parallel processing approach of SFCs is included in the proposed scheme, which can split the traffic in each flow into sub-flows. This shares the processing load by instantiating duplicate instances of each VNF type in the SFC. The simulation results guarantee the efficiency of the proposed scheme and improves the average performance between 6% and 24% compared to the state-of-the-art clustering methods.

Shared Backup Allocation Model of Middlebox Based on Workload-Dependent Failure Rate

With the network function virtualization technology, a middlebox can be deployed as software on commercial servers rather than on dedicated physical servers. A backup server is necessary to ensure the normal operation of the middlebox. The workload can affect the failure rate of backup server; the impact of workload-dependent failure rate on backup server allocation considering unavailability has not been extensively studied. This paper proposes a shared backup allocation model of middlebox with consideration of the workload-dependent failure rate of backup server. Backup resources on a backup server can be assigned to multiple functions. We observe that a function has four possible states and analyze the state transitions within the system. Through the queuing approach, we compute the probability of each function being available or unavailable for a certain assignment, and obtain the unavailability of each function. The proposed model is designed to find an assignment that minimizes the maximum unavailability among functions. We develop a simulated annealing algorithm to solve this problem. We evaluate and compare the performances of proposed and baseline models under different experimental conditions. Based on the results, we observe that, compared to the baseline model, the proposed model reduces the maximum unavailability by an average of 29% in our examined cases.

Resource Scheduling for UAV-Assisted Failure-Prone MEC in Industrial Internet

This paper focuses on reducing execution delays of dynamic computing tasks in UAV-assisted fault-prone mobile edge computing (FP-MEC) systems, which combine mobile edge computing (MEC) and network function virtualization (NFV) technologies. FP-MEC is suited to meet Industrial Internet (IIN) requirements such as data privacy, low latency, and low-cost industrial scalability in specific scenarios. However, the reliability of virtual network functions (VNFs) deployed on UAVs could impact system performance. Thus, this paper proposes the dynamic task scheduling optimization algorithm (DTSOA) based on deep reinforcement learning (DRL) for resource allocation design. The formulated execution delay optimization problem is described as an integer linear programming problem and it is an NP-hard problem. To overcome the intractable problem, this paper discretizes it into a series of single-time slot optimization problems. Furthermore, the experimental rigor is improved by constructing a real-time server state update system to calculate the real-time server load situation and crash probability. Theoretical analysis and experiments show that the DTSOA has better application prospects than Q-learning and the recent search method (RSM), and it is closer to the traversal search method (TSM).