In recent years, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) has emerged as a novel paradigm for providing computing services to devices in the industrial Internet of Things (IIoT) domain. However, due to the constraints of a single UAV’s battery and processing capabilities, it is unable to fulfill the computational and communication requirements of IIoT devices. We presents a multi-UAV-assisted IIoT system wherein computational services for IIoT devices are collaboratively provided by a terrestrial base station (BS) and UAVs. Considering the impact of energy consumption on the environment, we formulate a long-term weighted system energy consumption minimization problem by jointly optimizing UAVs trajectories, offloading and resource allocation decisions while considering the constraints of task deadlines. Due to the network dynamics and complexity of the optimization problem, we reformulate it as a Markov decision process (MDP) and apply a multi-agent twin delayed deep deterministic policy gradient (MATD3) approach to solve the problem. The simulation results obtained show that our proposed MATD3-based method achieves 6% higher rewards and 30% faster convergence over previous algorithms such as multi-agent deep deterministic policy gradient and the proximal policy optimization.

The widespread deployment of intelligent Internet of Things (IoT) devices brings tighter latency demands on complex workload patterns such as workflows. In such applications, tremendous dataflows are generated and processed in accordance with specific service chains. Edge computing has proven its feasibility in reducing the traffic in the core network and relieving cloud datacenters of fragmented computational demands. However, the efficient scheduling of workflows in hybrid edge–cloud networks is still challenging for the intelligent IoT paradigm. Existing works make dispatching decisions prior to real execution, making it difficult to cope with the dynamicity of the environment. Consequently, the schedulers are affected both by the scheduling strategy and by the mutual impact of dynamic workloads. We design an intelligent workflow scheduler for use in an edge–cloud network where workloads are generated with continuous steady arrivals. We develop new graph neural network (GNN)-based representations for task embedding and we design a proximal policy optimization (PPO)-based online learning scheduler. We further introduce an intrinsic reward to obtain an instantaneous evaluation of the dispatching decision and correct the scheduling policy on-the-fly. Numerical results validate the feasibility of our proposal as it outperforms existing works with an improved quality of service (QoS) level.

Unmanned aerial vehicles (UAVs) have been widely used in Intelligent Transportation Systems (ITS) due to their rapid deployment and high mobility, which are considered as a promising solution to expand the scope of communication, especially in inaccessible areas. However, there is a lack of a universal and extensible multi-hop collaboration model in the existing research on UAV-involved ITS. In this paper, we innovatively introduce a novel UAV-enabled multi-hop collaborative fog computing (FC) system model, in which several moving UAVs with unpredictable locations provide effective and efficient communication and computation services for ground user equipments (UEs). With this model, we mathematically formulate a joint user association, UAV association, task offloading, transmission power, computation resource allocation, and UAV location optimization problem, which is a mixed integer nonlinear programming (MINLP) problem and challenging to deal with. To solve the non-convex problem, we propose a novel multi-hop collaborative algorithm to derive the optimal task offloading and resource allocation decisions for each UAV. Simulation results demonstrate the superiority of the UAV-enabled multi-hop collaborative FC system and validate the effectiveness of the proposed scheme.

Task offloading combined with reinforcement learning (RL) is a promising research direction in edge computing. However, the intractability in the training of RL and the heterogeneity of network devices have hindered the application of RL in large-scale networks. Moreover, traditional RL algorithms lack mechanisms to share information effectively in a heterogeneous environment, which makes it more difficult for RL algorithms to converge due to the lack of global information. This article focuses on the task offloading problem in a heterogeneous environment. First, we give a formalized representation of the Lyapunov function to normalize both data and virtual energy queue operations. Subsequently, we jointly consider the computing rate and energy consumption in task offloading and then derive the optimization target leveraging Lyapunov optimization. A Deep Deterministic Policy Gradient (DDPG)-based multiple continuous variable decision model is proposed to make the optimal offloading decision in edge computing. Considering the heterogeneous environment, we improve Hetero Federated Learning (HFL) by introducing Kullback-Leibler (KL) divergence to accelerate the convergence of our DDPG based model. Experiments demonstrate that our algorithm accelerates the search for the optimal task offloading decision in heterogeneous environment.

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With the rapid and wide development and deployment of system virtualization, service availability analysis has become increasingly important in a virtualized system (VS) which suffers from software aging. Software rejuvenation techniques can be applied to improve service availability but its effectiveness depends on the rejuvenation policy, which defines when and where to rejuvenate, and which rejuvenation technique to be triggered. This article aims to analyze the optimal inspection time interval for maximizing application service (AS) availability under a three-level rejuvenation policy, in which rejuvenation techniques are deployed at each level, namely, AS, virtual machine (VM), and virtual machine monitor (VMM) levels. We first apply Markov regenerative process to construct an analytical model for the VS. Experiments of injecting memory leaks are conducted to measure aging-related parameters. Furthermore, numerical analysis is carried out to study the quantitative relationship between AS availability and inspection time interval, and determine the approximate optimal inspection time interval.

Migration-based Dynamic Platform (MDP) technique, a type of Moving Target Defense (MTD) techniques, defends against sophisticated cyber-attacks by randomly and dynamically selecting a platform for executing service/job. Security defense mechanisms protect service/job usually at the cost of degrading its performance. Therefore, it is valuable to make a trade-off between service/job security and its performance. However, previous researches on MTD techniques either focused on analyzing MTD effectiveness of protecting service/job or studied service/job performance with the assumption that attacks on service/job make no influence on its execution. This article aims to apply analytical modeling techniques to investigate the impact of MDP technique on job completion time in a system under attack. We use Stochastic Reward Nets (SRNs) to develop a Markov chain-based model for capturing typical behaviors of the adversary, the vulnerable system and a job. The formulas are derived for calculating the metrics of interest. Numerical analysis is conducted to study the impact of key parameters on job completion time and job security loss.

Energy efficiency is one of the most important concerns in cloud/edge computing systems. A major benefit of the Dynamic Voltage and Frequency Scaling (DVFS) technique is that a Virtual Machine (VM) can dynamically scale its computation frequency on an on-demand basis, which is helpful in reducing the energy cost of computation when dealing with stochastic workloads. In this paper, we study the joint workload allocation and computation resource configuration problem in distributed cloud/edge computing. We propose a new energy consumption model that considers the stochastic workloads for computation capacity reconfiguration-enabled VMs. We define Service Risk Probability (SRP) as the probability a VM fails to process the incoming workloads in the current time slot, and we study the energy-SRP tradeoff problem in single VM. Without specifying any distribution of the workloads, we prove that, theoretically there exists an optimal SRP that achieves minimal energy cost, and we derive the closed form of the condition to achieve this minimal energy point. We also derive the closed form for computing the optimal SRP when the workloads follow a Gaussian distribution. We then study the joint workload allocation and computation frequency configuration problem for multiple distributed VMs scenario, and we propose solutions to solve the problem for both Gaussian and unspecified distributions. Our performance evaluation results on both synthetic and real-world workload trace data demonstrate the effectiveness of the proposed model. The closeness between the simulation results and the analytical results prove that our proposed method can achieve lower energy consumption compared with fixed computation capacity configuration methods.

Abstract The problem of risk classification and prediction, an essential research direction, aiming to identify and predict risks for various applications, has been researched in this paper. To identify and predict risks, numerous researchers build models on discovering hidden information of a label (positive credit or negative credit). Fuzzy logic is robust in dealing with ambiguous data and, thus, benefits the problem of classification and prediction. However, the way to apply fuzzy logic optimally depends on the characteristics of the data and the objectives, and it is extraordinarily tricky to find such a way. This paper, therefore, proposes a general membership function model for fuzzy sets (GMFMFS) in the fuzzy decision tree and extend it to the fuzzy random forest method. The proposed methods can be applied to identify and predict the credit risks with almost optimal fuzzy sets. In addition, we analyze the feasibility of our GMFMFS and prove our GMFMFS‐based linear membership function can be extended to a nonlinear membership function without a significant increase in computing complex. Our GMFMFS‐based fuzzy decision tree is tested with a real dataset of US credit, Susy dataset of UCI, and synthetic datasets of big data. The results of experiments further demonstrate the effectiveness and potential of our GMFMFS‐based fuzzy decision tree with linear membership function and nonlinear membership function.

Edge computing has emerged as a promising technique because of its advantages in providing low-latency computation offloading services for resource-limited mobile user devices and Internet of Things applications. Computationally intensive artificial intelligence (AI) tasks are well suited to be offloaded to the Cloudlet server, but there is a lack of energy-delay optimization models specifically designed for this edge AI scenario. In this paper, we propose a multiple algorithm service model (MASM) that provides heterogeneous algorithms with different computation complexities and required data sizes to fulfill the same task, and develop an optimization model that aims at reducing the energy and delay cost by optimizing the workload assignment weights and computing capacities of virtual machines, at the same time guaranteeing the quality of the results (QoRs). We propose a tide ebb algorithm to solve the MASM optimization model, and we prove its Parato optimality. Numerical results obtained demonstrate the effectiveness of our proposed method, and prove that the energy and delay costs can be significantly reduced by sacrificing the QoR of the offloaded AI tasks.

Availability evaluation of a virtualized system is critical to the wide deployment of cloud computing services. Time-based, prediction-based rejuvenation of virtual machines (VM) and virtual machine monitors, VM failover and live VM migration are common high-availability (HA) techniques in a virtualized system. This paper investigates the effect of combination of these availability techniques on VM availability in a virtualized system where various software and hardware failures may occur. For each combination, we construct analytic models rejuvenation mechanisms to improve VM availability; (2) prediction-based rejuvenation enhances VM availability much more than time-based VM rejuvenation when prediction successful probability is above 70%, regardless failover and/or live VM migration is also deployed; (3) failover mechanism outperforms live VM migration, although they can work together for higher availability of VM. In addition, they can combine with software rejuvenation mechanisms for even higher availability; (4) and time interval setting is critical to a time-based rejuvenation mechanism. These analytic results provide guidelines for deploying and parameter setting of HA techniques in a virtualized system.

With the fast development of industrial Internet of things (IIoT), a large amount of data is being generated continuously by different sources. Storing all the raw data in the IIoT devices locally is unwise considering that the end devices' energy and storage spaces are strictly limited. In addition, the devices are unreliable and vulnerable to many threats because the networks may be deployed in remote and unattended areas. In this paper, we discuss the emerging challenges in the aspects of data processing, secure data storage, efficient data retrieval and dynamic data collection in IIoT. Then, we design a flexible and economical framework to solve the problems above by integrating the fog computing and cloud computing. Based on the time latency requirements, the collected data are processed and stored by the edge server or the cloud server. Specifically, all the raw data are first preprocessed by the edge server and then the time-sensitive data (e.g., control information) are used and stored locally. The non-time-sensitive data (e.g., monitored data) are transmitted to the cloud server to support data retrieval and mining in the future. A series of experiments and simulation are conducted to evaluate the performance of our scheme. The results illustrate that the proposed framework can greatly improve the efficiency and security of data storage and retrieval in IIoT.

Transient survivability analysis of a virtualized system (VS) is critical to the wide deployment of cloud services. The existing research of VS availability and/or reliability focused on the steady-state analysis. This paper presents a model and the closed-form solutions to analyze the survivability of both cloud service and VS after a service breakdown occurrence by using continuous-time Markov chain. Service breakdown may be caused by software rejuvenation of virtual machine (VM) and/or VM monitor (VMM), or caused by VM and/or VMM bugs. The VS applies two techniques for improving service survivability: VM failover and live VM migration. The proposed model and the defined survivability metrics not only enable us to quantitatively assess the system survivability but also provide insights on the investment efforts in system recovery strategies. Sensitivity analysis through numerical analysis is carried out to study the impact of key parameters on system survivability.

In harsh and heterogeneous wireless environments, the communication reliability and latency of industrial wireless sensor networks (IWSNs) seriously suffer from both intra- and interinterference. This paper presents an interference resistant approach for IWSNs by utilizing cognitive radio techniques. To improve the interference detection performance while uploading data as little as possible, we present a new computationally efficient and effective belief function (BF) theory-based reliability-probability decision fusion rule for cooperative sensing. A factor called reliability degree is introduced to characterize the imprecision of sensor observations, and the basic belief assignments are constructed by combining this reliability degree and local detection performance. Unlike the inefficient existing BF-based fusion schemes, the proposed rule has an explicit form and it is equivalent to the well-known Chair-Vashney (CV) rule in high signal-to-noise ratio conditions. We applied the proposed rule in interference resistant IWSNs to detect and avoid interference. Both numerical results and tests results demonstrate that the proposed rule has significant improvement in detection performance, diversity gains, and throughput compared with existing BF fusion schemes and CV rule.