With the rapid development of high-speed assistant driving and smart inspections, the edge network is required to provide quick and reliable service to avoid large service response delays and frequent re-transmissions caused by interruption. However, the reasonable service component caching, efficient edge collaboration and reliable cross-domain computation offloading are still key problems to be solved. Thus, we consider an AI and mobile edge computing (MEC) integrated service framework, which is highly reliable for high-speed mobile businesses, and we divide the service process into component caching phase and task offloading phase. In the first phase, we novelly define the edge collaborative service domain (ECSD) which allows multiple edge nodes to collaboratively share resources from a global perspective and design a user behavior aware service component pre-caching method to increase resource utilization. In the second phase, based on the formed ECSDs and cached service components, we present an AI-empowered cross-domain computation task offloading mechanism including task partition and backup to enhance the reliable service capability of edge networks. Simulation results verify that the proposed mechanism can jointly optimize the allocation of caching, computation, and communication resources, while improving the service response speed and resource utility of edge networks.

To deal with the explosion connections and data volume for emergency communication or hot spot capacity enhancement with massive Internet of Things (IoT) devices, deploying aerial base stations (AeBSs) on unmanned aerial vehicles (UAVs) to generate heterogeneous space–air–ground networks is considered to be a quite effective method. However, the flying AeBSs and back-hauling to existing heterogeneous networks (HetNets) lead to network energy consumption a key point. To ensure the energy-efficient operation of space–air–ground networks for smart IoT applications, we put forward the cluster-based HetNets energy-efficient resource allocation mechanism (CHERA). The scheme first divides the entire network into multiple independent BS clusters with the K-means++ algorithm for distributed energy efficiency (EE) optimization. Then, we propose a greedy BS sleeping strategy and a Lagrangian-dual-based optimal power allocation algorithm for the maximization of EE in each BS cluster. The EE optimization of space–air–ground IoT networks is implemented under the self-organizing network framework to make sure of the efficient and reliable operation of the network. Simulation results indicate that energy consumption is effectively decreased with the mechanism. It boosts the EE of space–air–ground networks by 23.8% compared with a baseline algorithm in which BSs are all in active mode with no power optimization. The result is expected to be useful for achieving future green space–air–ground networks IoT applications.

Vehicle-to-everything communication is an indispensable component of 6G networks that could help to facilitate future transportation systems. However, massive vehicles and unstable vehicle-to-vehicle (V2V) links may become bottlenecks for the low-latency delivery of contents, such as safety-critical emergency messages and multimedia. Instead of resolving the problem in a centralized way, we propose a massive vehicular Internet-of-Things system and investigate the approach that would enable each vehicle to decide the transmission mode from three modes, i.e., vehicle-to-network, vehicle-to-infrastructure and V2V sidelinks, and wireless resources. Specifically, a multiagent deep reinforcement learning (RL) framework is formulated by combining the multiagent RL approach, WoLF-PHC, with the techniques from deep$Q$-learning (DQN) to gain the formulated framework with the capability of capturing the effects of interaction between learning agents and states of complex environment. The framework is set to maximize the throughput of vehicles while maintaining the latency and reliability constraints of the vehicle communication links. However, it could be easily extended to other objectives. The simulation results demonstrate that the proposed approach outperforms the compared ones in total traffic capacity and satisfaction rate of the vehicles in communication.

With the development of the Internet of Things, trust has become a limited factor in the integration of heterogeneous IoT networks. In this regard, we use the combination of blockchain technology and SDN/NFV to build a heterogeneous IoT network resource management model based on the consortium chain. In order to solve the efficiency problem caused by the full amount of data on the chain, we deploy light nodes and full nodes for the consortium chain. At the same time, we use the idea of identification to realize the separation of identification and resource information, build the application mode of on-chain identification and off-chain information, and realize resources endogenous trust management. We also propose a practical Byzantine fault-tolerant consensus mechanism based on reputation value to save consensus costs and improve efficiency. Combined with artificial intelligence technology, we introduce deep reinforcement learning for service function chain orchestration, and design a service function chain orchestration algorithm based on Asynchronous Advantage Actor-Critic to optimize orchestration costs. The final simulation results show that the consensus algorithm and service function chain orchestration algorithm we designed have good performance in terms of cost saving and efficiency improvement.

The Industrial Internet of Things (IIoT) is one of the important applications under the 5G massive machine type of communication (mMTC) scenario. To ensure the high reliability of IIoT services, it is necessary to apply an efficient resource allocation method under the dynamic and complex environment. In view of the absence of energy-efficient resource management architecture for the entire network, this article proposes an intelligent-driven green resource allocation mechanism for the IIoT under 5G heterogeneous networks. First, an intelligent end-to-end self-organizing resource allocation framework for IIoT service is given. Next, an energy-efficient resource allocation model within the framework is proposed. It is then solved by an intelligent mechanism with the asynchronous advantage actor critic driven deep reinforcement learning algorithm. Through the comparison analysis of different methods and rewards under IIoT scenarios with proper parameters setting, the proposed method can achieve better performance than other traditional deep learning (DL) methods and maintain service quality above accepted levels as well.

With the development of information technology, the network consists of various proprietary hardware devices, and the use of these devices brings problems. To solve problems, network function virtualization is proposed, which decouples the software and hardware in the network, and deploys the existing network function devices to a common physical platform. However, network virtualization needs will inevitably face reliability problems during resource virtualization and service function chain deployment. This article proposes a service function chain reliability evaluation method and reliability optimization algorithm. The composition relationship and reliability influencing factors of service function chain were analyzed, including resource preemption, common cause failure, fault recovery and redundant backup. The service function chain was modeled as a Petri net model, and reliability evaluation results related to execution time were obtained. Based on the reliability assessment results, a VNF migration strategy is designed, with reliability as the optimization goal while considering costs. Simulation results show that, compared with the reliability optimization strategy based on backup, our algorithm costs less and reduces the impact of resource preemption on service reliability.

In the network function virtualization (NFV) environment, network services are usually provided in the form of service function chains (SFCs), which defines the link order of virtual network functions required in service requests and are mapped to the physical network. Although NFV facilitates the flexible provision of network services, service interruptions may occur as a result of software and hardware failures. Current solutions mostly use the backup method to ensure the reliability of SFCs. However, these methods ignore the SFC construction phase that has an impact on reliability. Besides, the resource efficiency still requires improvement. To address these issues, reliability-oriented SFC construction and backup problems are investigated in this work. First, an instance-sharing and reliable construction algorithm (ISRCA) is proposed to aggregate multiple SFCs into a service function graph (SFG), and perform reliability screening for the SFG set. After mapping the SFG to the physical network, a node-ranking algorithm with centrality and reliability (NRCR) is proposed for backup node selection and backup instance deployment to improve the reliability of SFCs that have not met the requirements. Experimental results demonstrate that under the premise of ensuring reliability, the proposed backup method can reduce the consumption of bandwidth resources by about 11.7%, when combined with the proposed construction method, it can further reduce the backup resources by 13.9%.

With the rapid development of data centers, the energy consumption brought by more and more data centers cannot be underestimated. How to intelligently manage software‐defined data center networks to reduce network energy consumption and improve network performance is becoming an important research subject. In this paper, for the flows with deadline requirements, we study how to design the rate‐variable flow scheduling scheme to realize energy‐saving and minimize the mean completion time (MCT) of flows based on meeting the deadline requirement. The flow scheduling optimization problem can be modeled as a Markov decision process (MDP). To cope with a large solution space, we design a DDPG‐EEFS algorithm to find the optimal scheduling scheme for flows. The simulation result reveals that the DDPG‐EEFS algorithm only trains part of the states and gets a good energy‐saving effect and network performance. When the traffic intensity is small, the transmission time performance can be improved by sacrificing a little energy efficiency.

In ubiquitous stub network, it is a critical challenge to realize resource discovery and share under disconnected network topology. In this paper, a cluster-based resource discovery mechanism is proposed with resource registration, distribution and routing model. Firstly, we use resource directory index nodes to assist in resource management. Secondly, we use inter-cluster mobile terminals to support resource routing. In addition, we take the nodes contact probability into account and establish the minimum expectation delay routing standard to opportunistically route between terminals. At last, the simulation result shows this mechanism is better applied to support disconnected ubiquitous resource discovery.

LTE-based Public Safety Network (PSN) is a wireless communication network which can provide efficient and reliable communication in disasters or emergencies for disaster relief and public protection. Therefore, ensuring that network congestion will not happen in PSN during an emergency is becoming increasingly important. LTE-based PSN is easy to be congested because part of spectrum resources is compressed to guarantee priority requirements of public safety users. In this paper, we develop a new method namely Selective Admission Control (SAC) mechanism to manage the radio bearers access to the commercial radio for Public Safety (PS) in LTE-based PSN. In the case of emergency, we select the traffic bearer with minimum estimated load increment accessing to the LTE-based PSN. The channel quality of new bearers should be taken into account, which means that in congestion, users who arrive earlier with poor channel quality will be rejected to reserve sufficient resources for users who arrive later with good channel quality. The simulation results show that the SAC mechanism can improve throughput by 36% and lower the rejection rate by 73% at most than reference method based on non-selective access control model, as a result effectively avoiding the network congestion and improving the utilization of spectrum resources for public safety communication.

To realise load optimisation in cellular and public wireless local area network (WLAN) interworking network, a fairness preferred throughput maximisation (FPTM) optimisation model and a particular algorithm for it named joint UE‐AN association and resource allocation optimisation based on generalised benders decomposition are proposed in the study. The derived solution will give guidance on UE's access selection and resource allocation in cellular network to optimise the overall performance of the interworking network. Simulation results validate the performance on optimising access load in the interworking networks of FPTM model, which can practically enhance the effect of offloading from cellular network to WLAN and improve the total throughput.

Activity recognition is a hot topic of research that is widely adopted by many applications such as fall detection of elderly people. Emerging passive RFID (radio-frequency identification) is creating huge opportunity for wearable devices to achieve activity recognition. However, performance of activity recognition is constrained by RFID localization accuracy and low quality of data streams characterized by sparsity and noise. In this paper, we present a novel activity recognition system, called RTagCare, which is a low-cost, unobtrusive and lightweight RFID based system. The RTagCare system leverage RFID localization technology, 3D-accelerometer base human activity identification and data mining algorithm to overcome traditional activity recognition system issues. RTagCare has been implemented and deployed in a test environment. As a result, RTagCare generally performs well to recognize human activity with high performance (F-score >94%).

Failure instances in distributed computing systems (DCSs) have exhibited temporal and spatial correlations, where a single failure instance can trigger a set of failure instances simultaneously or successively within a short time interval. We investigate an effective approach to predict correlated failures of computing elements (CEs) in DCSs. Correlated-failure patterns are modeled using the concept of probabilistic shared risk groups (PSRG). Firstly, we design a new structure for PSRG, named SPSRG, to describe features of correlated failures. Then we exploit an association rule mining technique in a parallel way to generate and update our SPSRG using information of CE-failure states. Finally, we propose a correlated failure prediction approach to evaluate the probabilities of upcoming failures from the SPSRG. The experimental results show that the proposed approach outperforms other approaches in failure prediction performance in terms of precision, recall and F-measure. Moreover, it allows employing customizable thresholds by which the trade-off between precision and recall can be adjusted for various requirements.

In network virtualization environment, if a virtual network (VN) needs to be deployed across multiple infrastructure domains, a splitting scheme of the VN should be found. With the goal of minimizing embedding cost, the existing methods solve VN splitting by linear programing. However, since the VN splitting problem is NP-Hard, these methods will take a lot of computing time when the problem scale gets bigger. In this paper, a max-flow/min-cut theory based VN splitting mechanism is proposed. The proposed method first creates a binary tree of the InPs by system clustering method, based on which the multidomain VN splitting problem is decomposed into several two-domain VN splitting problems. Then the method transforms each two-domain splitting problem into a max-flow/min-cut problem, and solves it by the shortest augmenting path algorithm efficiently. Simulations show that the proposed mechanism can improve the efficiency of VN splitting steadily and save the embedding cost.

Due to off-the-shelf hardware and software applications integrated with distinct manufactures are widely used, networked computing systems incur high risk of failures and exceptions. Failures play a crucial role and must be timely handled to ensure system survivability and reliability. This paper focuses on on-line failure prediction for networked computing systems using system runtime data. We propose a failure prediction approach based on cloud theory (CT) and hidden Markov model (HMM). This approach expands the HMM, training with the CT. Additionally, we define the parameter ω as the correlations between various indices and failures, taking account of multiple runtime indices in networked computing systems. And we use multiple dimensions to describe failure prediction in detail, by extending parameters in HMM. In order to reduce computing cost in model training phase, we exploit the likelihood and membership degree computing algorithms in CT, instead of traditional HMM algorithms. Finally, the results from our simulations show the feasibilities and effectiveness of our approach. The experiments show that the execution time of the proposed failure prediction is reduced in terms of promised prediction performance.

Because of the advantages of multi-hop communication, self-organizing, self-healing and reliability, wireless mesh network becomes an ideal choice for data collection. However, wireless mesh network for data collection faces challenge on communication performance of network caused by application layer data traffic. When a large number of data occurs in emergence, some mesh nodes (the last hop nodes) which are in pivotal location will face great communication pressure and probably lead to extremely data congestion, especially in smart grid. For the idea of load balancing, this paper proposes a new random switching traffic scheduling algorithm based on data collection tree. Simulation data show that the new algorithm can create a balanced data collection tree, significantly reduce the packet loss ratio of the burst data and release congestion of system.

Survivable virtual network embedding (SVNE) aims at embedding a virtual network (VN) in a way, that after being affected by substrate failures, the VN is still operating. Based on the single node failure assumption, that at any time there can be at most one failed substrate node, the existing studies for the SVNE against substrate node failures back up VNs with a maximum resource sharing. However, multiple node failures do happen in reality, thus those methods are not always effective. In this paper, we propose a topology-aware VN embedding approach to enhancing the survivability against multiple node failures. We make use of the topology attributes to provide each substrate node with multiple potential failover choices, based on which a recoverability-based VN embedding algorithm and a profit-driven VN remapping algorithm are presented. Simulation results show that the proposed approach can achieve rational resource allocation and effectively increase the long term business profit to the infrastructure provider.

One of the key technologies of smart grid is an efficient, reliable and secure two-way communication system for meter data collection. Because of the advantages of muti-hop communication, self-organizing, self-healing and reliability, wireless muti-hop communication technology becomes an ideal choice for smart grid meter data collection. However, forming wireless mesh network with advanced electricity devices (smart meters) which have the communication capabilities for meter data collection faces challenge on communication performance of network caused by application layer data traffic. When a large number of data occur in emergence, some smart meters (the last hop nodes) which are in pivotal location will face great communication pressure and probably lead to extremely data congestion. With the idea of load balancing, this paper proposes a new random switching traffic scheduling algorithm based on meter data collection tree. Simulation data show that the new algorithm can create a balanced meter data collection tree, significantly reduce the packet loss ratio of the burst data and release congestion of system.

We address the problem of inferring the network link loss rates using end-to-end measurements, which can also be formulated as network tomography. As we have known that most tomography problems are rank-deficit. One kind of method uses multiple probe measurements to acquire more information about the system that may generate much additional overhead; the other method imposes unrealistic assumption on the system. To address the issue that most network tomography methods cannot take into account both accuracy and efficiency, a novel link loss rate inference algorithm is proposed. In this paper, we get all identifiable links and then we utilize the information of these determined links to acquire the global distribution of the system. Moreover we partition all links in the network into several subsets. For each group, nonlinear programming is used to get the optimization solution of link loss rate. Finally, we evaluate our method and two former representative methods by the simulation. The results demonstrate that our method not only reduces the probe costs and the running time to a low level, but also makes a great improvement on the accuracy. Furthermore, our method can also perform well in more congested and large networks.

In a composite service for multiple users, users that locate in the different network position are related to network parameters that change dynamically. Therefore, we need a service composition method that can not only handle many user requests, but also adapt to the change of the current network parameters. We use queuing theory and reliability theory to model services, and propose a runtime service composition method. The method obtains multiple service execution paths for each kind of user requests, and chooses the proper candidate service in runtime according to the current network state. The results show that our method is effective and can adapt to the changes of the network parameters.

Plenty of web services are emerging in clouds. They are distributed, heterogeneous, autonomous and dynamic. These characteristics may make a composite service unstable and inflexible. To adapt to this environment, we propose a machine learning strategy that is developed for and applied to web service composition. This way, the composition framework continually learns which web service candidates are currently best suited to be selected and composed to fulfill more complex tasks. Since the learning process is not stopped, the framework is able to adapt its composition strategies to changing conditions in dynamic environments. A case study is given and the learning algorithm is evaluated and compared to the results of related work, which shows that our method improves the success rate of service composition.

In this paper, we introduce a network performance anomaly detection and localization method based on active probing, aiming at avoiding waste of unnecessary probes and reducing detecting time by decreasing selecting rounds in detection phase. We propose a method of classifying detection strategies in order to find a balance between extra calculation and link load. Also we optimized the procedures of one of the strategies so that instead of finding a local optimal solution, we get a global optimal approach. An algorithm that can adapt to multi anomaly link networks is proposed and several issues during detection phase were being discussed. Finally we simulate a former representative algorithm and our improved method on different network topologies. The results show that our improved algorithm outperforms the former one in both probe selecting rounds during detection phase by 10%.

In the open MANETs, nodes with different goals expect to benefit from others, but are unwilling to share their own resources. These selfish behaviors have posed increasing research challenges for cooperation. Negotiation as a key form of interaction for two or more parties enables nodes to announce their contradictory demands and seek to an agreement by concession. In the paper, the Service Negotiation model for Selfish nodes in the MANETs (SNSM) combines the policies of imitating rivals' behaviors and fast-approaching reserve prices presented to generate mutual offer and counter-offer for service bargaining. Specially, the model provides three types of changing rates of bids to speculate the rivals' behaviors. In addition, we improve the Weber-Fechner's law to self-adjust the deadline in the negotiation. Simulation results demonstrate our model has superior performances in increasing the negotiation efficiency, achieving mutual benefits between the service buyer and seller.

Due to the efficiency and adaptability, the active probing technique has become an attractive tool for fault localization in large and complex computer networks. It performs diagnosis by appropriately selecting the probes and analyzing the results. However, selecting an optimal probe set in such environment has been proven to be NP-hard problem. And, even the current approximate methods that can achieve near-optimal solutions have exponential computing time with the network size. To address this issue, we utilize the properties of conditional independence and directed-separation of Bayesian network, and propose a novel methodology which is used to estimate the approximate conditional independence of probes. According to the methodology, the model can be divided into several approximate independent subsets, on which the probes could be selected respectively. Furthermore, by integrating the methodology with a former representative probe selection algorithm which is called BPEA, we design a new efficient probe selection algorithm. Several experiments are given afterwards to show how our algorithm outperforms BPEA. And we also present that our algorithm can be used in large-scale computer networks while the former one can not. Moreover, the methodology can be applied to other probing based techniques as well.

Active probing is an effective tool for monitoring networks. By measuring probing responses, we can perform fault diagnosis actively and efficiently without instrumentation on managed entities. In order to reduce the traffic generated by probing messages and the measurement infrastructure costs, an optimal set of probes is desirable. However, the computational complexity for obtaining such an optimal set is very high. Existing works assume single-fault scenarios, apply only to small size networks, or use simplistic methods that are vulnerable to noises. In this paper, by exploiting the conditionally independent property in Bayesian networks, we prove a theorem on the information provided by a set of probes. Based on this theorem and structure property of Bayesian networks, we propose two approaches which can effectively reduce the computation time. A highly efficient adaptive probing algorithm is then presented. Compared with previous techniques, experiments have shown that our approach is more efficient in selecting an optimal set of probes without degrading diagnosis quality in large scale and noisy networks.

In a clustering-based MANETs, task allocation has posed increasing research challenges because the needs of management and coordination are accentuated by complicated demands of cluster members. A self-adaptive method of task allocation is designed to facilitate self-planning and self-negotiation for nodes during tasks being distributed and executed. The method is composed of two parts: for one part, the cluster head works out an integrated schedule for tasks, including selecting different sets of execution nodes and defining their functions according to task types. Cooperative group towards synergetic task is formed by policies of filtering and voting. Assignment modes based on either polling or mobile agents are also involved, the latter adopts an improved Ant Colony Optimization (ACO) algorithm to plan a migration path. For another, if a cluster member fails to accomplish a task, it could negotiate as a tenderee with other nodes using a revised contract net protocol. In addition, we employ a stimulation mechanism of distributing virtual task experience in connection with QoS guarantees to offer compensation for nodes' energy consumption and extra load. Simulation results demonstrate performance benefits of our self-adaptive method can efficaciously alleviate load of the cluster head, balance loads of nodes in consideration of energy restriction, and prolong the lifecycle of the cluster.

Future LTE RAN will benefit from a significant degree of self-organization. Autonomic Load Balancing (ALB) is considered as an important function of self-organization for LTE RAN. A novel distributed method to achieve ALB for LTE RAN, AFWBM (Autonomic Flowing Water Balancing Method), is presented, which works by AFWBM module. The eNBs with AFWBM modules can detect their load conditions depending on self-monitoring actions. When overload conditions are detected, eNBs can adjust their HOM (handover hysteresis margin) and trigger handover behaviors of users automatically to balance load. Simulation results have demonstrated that by AFWBM, load of eNBs can be balanced and system capacity can be improved significantly.

The modern society has come to rely heavily on IT services. To improve the quality of IT services it is important to quickly and accurately detect and diagnose their faults which are usually detected as disruption of a set of dependent logical services affected by the failed IT resources. The task, depending on observed symptoms and knowledge about IT services, is always disturbed by noises and dynamic changing in the managed environments. We present a tool for analysis of IT services faults which, given a set of failed end-to-end services, discovers the underlying resources of faulty state. We demonstrate empirically that it applies in noisy and dynamic changing environments with bounded errors and high efficiency. We compare our algorithm with two prior approaches, Shrink and Maxcoverage, in two well-known types of network topologies. Experimental results show that our algorithm improves the overall performance.

The research of service management information model can bring forward the following benefits: unified service planning and provisioning, consistency among the functionality models in the service supply chain and correctness of the mapping between management requirements and management functions. In this paper, we have proposed a lifecycle-based generic modeling method on service management information, which adopts a requirement mapping in a top-down manner to define managed objects in the segments of service lifecycle. The model can be referenced as a meta-model to direct the development of service management systems.

After proposed the framework of the VPN service management, the shortage of the current management information modeling methods in the network/service management is analyzed and the advantage of the ODP/UML based modeling method is given. The applying open distributed processing/unified modeling language (ODP/UML) for the management information modeling in the VPN service management is studied in detail. The implementation of the VPN SMS using CORBA is also given.