System Performance Degradation Research Articles

Software Aging Effects on Kubernetes in Container Orchestration Systems for Digital Twin Cloud Infrastructures of Urban Air Mobility

It is necessary to develop a vehicle digital twin (DT) for urban air mobility (UAM) that uses an accurate, physics-based emulator to model the statics and dynamics of a vehicle. This is because the use of digital twins in the operation and control of UAM vehicles is essential for the UAM operational digital twin infrastructure (UAM-ODT). There are several issues that need to be addressed in this process: (i) the lack of digital twin engines for the digitalization (twinization) of the dynamics and control of UAM vehicles at the core of UAM-ODT systems; (ii) the lack of back-end system engineering in the development of UAM vehicle DTs; and (iii) the lack of fault-tolerant mechanisms for the DT cloud back-end system to run uninterrupted operations 24/7. On the other hand, software aging and rejuvenation are becoming increasingly important in a variety of computing scenarios as the demand for reliable and available services increases. With the increasing use of containerized systems, there is also a need for an orchestrator to support easy management and reduce operational costs. In this paper, an operational digital twin (ODT) of a typical urban air mobility (UAM) infrastructure is developed on a private cloud system based on Kubernetes using a proposed cloud-in-the-loop simulation approach. To ensure the ODT can provide uninterrupted operational control and services in UAM around the clock, we propose a methodology for investigating software aging in Kubernetes-based containerized clouds. We evaluate the behavior of Kubernetes software using the Nginx and K3S tools while they manage pods in an accelerated lifetime experiment. We continuously execute operations for creating and terminating pods, allowing us to observe the utilization of computing resources (e.g., CPU, memory, and I/O), the performance of the Nginx and K3S environments, and the response time of an application hosted in those environments. In some conditions and for specific metrics, such as virtual memory usage, we observed the effects of software aging, including a memory leak that is not fully cleared when the cluster is stopped. These issues could lead to system performance degradation and eventually compromise the reliability and availability of the system when it crashes due to memory space exhaustion or full utilization of swap space on the hard disk. This study helps with the deployment and maintenance of virtualized environments from the standpoint of system dependability in digital twin computing infrastructures where a large number of services are running under strict continuity requirements.

Design a dynamic automation system to adaptively allocate functions between humans and machines

As automation spreads, the relationship between humans and machines brings increasingly questionable challenges. The static allocation of functions and features among the systems shows that automation brings more than just benefits, causing, for example, the out-of-the-loop condition of operators and the degradation of system performance over time. New Dynamic Automation (DA) rationales are emerging, although still marginally in manufacturing, showing theoretical and application potential. To date, the characteristics of DA systems are not addressed jointly and comprehensively, but the literature focuses on specific issues (e.g., adaptive functions or interfaces). The following paper provides a framework for Design A Dynamic Automation System (DADAS), e.g., the dynamic design aspects to implement a DA approach. By means of a sequential approach, the research also highlights logical relationships between the various aspects.

Adaptive Resilient Tracking Control With Dual-Terminal Dynamic-Triggering for a Linear Multi-Agent System Against False Data Injection Attacks

False data injection (FDI) attacks on sensors and actuators are adverse in multi-agent systems (MASs), and they degrade system performance and even cause instability by tampering transmission signals. Resilient control for such attacks is critical in the real world. In this paper, a distributed adaptive resilient control scheme is proposed. This scheme is based on observers for reconstructing signals with projection operators and dual-terminal dynamic event-triggered mechanism. The update of the controller and intermittent communication among agents are determined by the proposed dual-terminal triggered mechanism, and two triggered functions work independently in individual follower. Compared with traditional static one, it not only achieves satisfactory consensus performance but also reduces triggered number. Also, state estimators are developed for reconstruction of combined measurements, and continuous monitoring is removed. Theoretical analysis shows that our proposed adaptive resilient scheme attenuates adverse effects of FDI attacks and the followers track the leader with Zeno-free behaviors. Finally, two illustrative examples are presented to verify the effectiveness of our adaptive resilient scheme.

Data Utilization-Based Adaptive Data Management Method for Distributed Storage System in WAN Environment

Recently, research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand increase. Physical expansion limits exist for traditional standalone storage systems, such as I/O and file system capacity. However, the existing distributed storage system does not consider where data is consumed and is more focused on data dissemination and optimizing the lookup cost of data location. And this leads to system performance degradation due to low locality occurring in a Wide Area Network (WAN) environment with high network latency. This problem hinders deploying distributed storage systems to multiple data centers over WAN. It lowers the scalability of distributed storage systems to accommodate data storage needs. This paper proposes a method for distributing data in a WAN environment considering network latency and data locality to solve this problem and increase overall system performance. The proposed distributed storage method monitors data utilization and locality to classify data temperature as hot, warm, and cold. With assigned data temperature, the proposed algorithm adaptively selects the appropriate data center and places data accordingly to overcome the excess latency from the WAN environment, leading to overall system performance degradation. This paper also conducts simulations to evaluate the proposed and existing distributed storage methods. The result shows that our proposed method reduced latency by 38% compared to the existing method. Therefore, the proposed method in this paper can be used in large-scale distributed storage systems over a WAN environment to improve latency and performance compared to existing methods, such as consistent hashing.

An Investigation of Exhaust Gas Temperature of Aircraft Engine Using LSTM

A significant obstacle to creating efficient machine health monitoring systems is estimating performance degradation in dynamic systems, like aero plane engines. In exceedingly complex systems with many components, states, and parameters, conventional model-based and data-driven methods fall short of producing satisfactory results. While traditional methods had several drawbacks, deep learning has emerged as a viable computational tool for dynamic system prediction. In order to track system deterioration and estimate the EGT, a novel technique based on the Long Short-Term Memory (LSTM) network, (an architecture created to find the hidden patterns hidden in time series data) is provided in this research. The health monitoring information of aircraft turbofan engines is used to assess the effectiveness of the proposed strategy. As a result of this network’s ability to recognize the input data as a real-time series, the output in the following step can be predicted. Results of the suggested study show a significant ability to anticipate the output in the following time step. Additionally, the proposed model has a shorter learning curve and is more accurate.

Multilayer Model-Aided INS Based on Real-Time Corrected Vehicle Dynamics Model

Vehicle dynamics model-aided inertial navigation system (INS) is an effective approach for autonomous navigation when the Global Navigation Satellite System fails, However, the accuracy of the vehicle dynamics model (VDM) will decrease, or even diverge, in low-speed and high-dynamic situations, which leads to the performance degradation of the whole system. The traditional VDM-aided INS uses the velocity information from VDM to estimate and correct INS errors, but most previous research ignored that the acceleration and angular velocity from INS are more accurate than VDM. To reach the full potential of INS and VDM, this paper proposed a three-layer structure for the VDM-aided INS. In the proposed method, the navigation information is divided into the sensor layer, system layer I, and system layer II. In the sensor layer, VDM is corrected by INS through the strong tracking cubature Kalman filter, and the chi-square test is adopted to avoid redundant computation. In the system layer I, INS is aided by the corrected velocity information from VDM. In the system layer II, the position is updated with the velocity and attitude that are corrected in the system layer I. As the result, VDM and INS can be corrected in different layers simultaneously, and the accuracy and robustness of the whole system can be improved. The simulation experiment proved that, with the sensor layer filter, the velocity errors of VDM can be reduced by 35% in low-speed and high-dynamic situations, and reduced by 20% in the normal situation. Then, a 31-minute field test shows that the maximum horizontal position error of the proposed method is 241.8 m, while those of two traditional VDM-aided INS methods are more than 500 m.

Reinforcement Learning-Based Physical Cross-Layer Security and Privacy in 6G

Sixth-generation (6G) cellular systems will have an inherent vulnerability to physical (PHY)-layer attacks and privacy leakage, due to the large-scale heterogeneous networks with booming time-sensitive applications. Important wireless techniques including non-orthogonal multiple access, mobile edge computing, millimeter-wave, massive multiple-input and multiple-output, visible light communication, terahertz, and intelligent reflecting surface can improve the spectrum efficiency and quality-of-service but will raise challenges for the 6G PHY and cross-layer security and privacy protection. Existing optimization based PHY and cross-layer security and privacy protection schemes such as the convex optimization method have to rely on accurate attack patterns and strategies and thus suffer from performance degradation in 6G systems that have shorter communication latency, more devices and higher spectrum efficiency than 5G. Reinforcement learning (RL) algorithms help wireless devices optimize their security policies to enhance the security performance in dynamic networks against smart attacks without depending on the attack model. Therefore, this article provides a comprehensive survey on the RL based 6G PHY cross-layer security and privacy protection. In this article, we investigate the potential attacks in 6G systems and discuss the PHY cross-layer security solutions. A brief overview of reinforcement learning algorithms is provided. Afterward, we review the RL based PHY-layer security and privacy protection and discuss how to apply RL algorithms in 6G security scenarios, especially focusing on the game with jammers, eavesdroppers, spoofers and inference attackers. The RL based security solutions for unmanned aerial vehicles (UAVs) and cross-layer scenarios are also reviewed. The future research directions are identified and the corresponding RL based potential solutions are discussed for 6G.

Analysis and Design of Secure Sampled-Data Control Subject to Denial-of-Service Attacks

This study addresses the issue of secure control design for cyber-physical systems (CPS) against denial of service (DoS) attacks. We take into account a continuous-time linear system with a convex quadratic performance measure and a sampled linear state feedback control. DoS attacks impose constraints on the CPS, where packets may be jammed between the sensor and controller by a malicious entity, potentially resulting in system instability and performance degradation. We assume that the attacker can perform DoS attacks with a limited time and frequency due to energy restrictions. We devise an efficient procedure using the linear matrix inequality approach to compute an upper bound on the performance degradation brought on by the DoS attack. We also propose a redesign of the controller to minimize this performance degradation. Finally, a simulation example illustrates the computation of the performance degradation under a bounded DoS attack and the design of a secure controller. Simulation results show that the designed controller effectively keeps the feedback loop’s performance and stability under attack.

Sum Rate Maximization of MIMO Monostatic Backscatter Networks by Suppressing Residual Self-Interference

Monostatic backscatter (MBS) networks provide connectivity for ultra-low power and low-cost tags for numerous applications. However, the reader operates in the full-duplex (FD) mode and experiences self-interference (SI) levels much higher (e.g., 160 dB) than the desired signal. However, hardware-based SI cancellation (SIC) techniques can remove SI partially only. Therefore, residual SI (RSI) dramatically degrades system performance. To remedy this problem, we develop a sum-rate maximization algorithm that suppresses the RSI and ensures that the tags harvest sufficient energy. It jointly optimizes the reader precoder and combiners and the tags reflection coefficients. Because of the non-convexity of this problem, we utilize alternating optimization (AO) to split it into three parts. They are then solved using successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques to yield the precoder, a generalized Rayleigh quotient-based closed-form solution for the combiners, and geometric programming (GP) to get the reflection coefficients. Simulation results validate the fast convergence of the algorithm and show significant sum rate improvements (more than 21%) over the baselines.

Recursive Watermarking-Based Transient Covert Attack Detection for the Industrial CPS

The subject of attack detection for industrial cyber-physical systems (IPCSs) is covered in this paper, facing threats from transient covert attacks (TCAs), also referred to as a special version of replay attacks with a specific frequency and short duration. With the active instant and period of the attacks, as well as the dynamics of a virtual system to replicate IPCSs’ function and produce attack signals, a comprehensive model of the TCAs is built. Though watermarking-based detection algorithms have been shown effective in detecting TCAs, the induced system performance loss is too significant, as such our primary goal is to minimize system performance degradation while maintaining the detection rate. Because the active periods of TCAs are substantially shorter than their sleep ones or even they are practically always silent, it makes sense that reducing superfluous watermarking will facilitate system performance. So, using an “event-triggered” strategy, a unique recursive watermarking-based detection algorithm is proposed. Here, the trigger modes of watermarking are divided into three types, namely forced, high probability, and low probability. The design principles are proved via algorithms and criteria, and theoretical analysis of the detection rate and the system performance loss are also supplied. The advantages of the suggested algorithms are finally demonstrated by numerical simulations of a quadruple-water-tank system and experiments with a permanent magnet synchronous motor on the dSpace platform.

Long-term performance evaluation of CO2 heat pump water heater under different discharge pressure control strategies

CO2 heat pump water heater has raised considerable attention as an environmentally efficient technology for hot water production. However, after long-term operation, the deterioration of component performance will not only directly cause system performance degradation, but also lead to a deviation of discharge pressure from the actual optimum under different discharge pressure control strategies, further resulting in additional performance loss. So far, the additional performance loss in the long run has not been in-depth investigated. In this study, based on a validated system model of CO2 heat pump water heater with component performance degradation factors, the long-term additional loss of system performance under eight different discharge pressure control strategies are compared. The results reveal that additional performance loss in the long-term operation can be significant under certain control strategy and working conditions. As for the investigated system, the control strategy with the parameter group of ambient and gas cooler outlet temperatures is favorable for application under the nominal operating condition with long-term degradation of heat exchangers, and the average additional loss of COP (coefficient of performance) can be decreased to 0.62%. As for the compressor degradation, the optimal refrigerant charge management strategy is preferred with an average COP loss as low as 0.05% under certain working conditions. In facing with year-round performance degradation, the control strategy with the parameter group of evaporating temperature and gas cooler outlet temperature shows better robustness, since the average additional annual performance loss can be reduced to only 2.52%.

Novel attack‐defense framework for nonlinear complex networks: An important‐data‐based method

AbstractThis article addresses the state estimation problem for a class of nonlinear complex networks (CNs) under attack. First, a novel important‐data‐based (IDB) attack strategy is skillfully proposed from the adversary's point of view to maximize the attack effect. Different from most existing attack models, the IDB attacker has the ability to eavesdrop measurements and only attacks the packets which play an important role in the system. As such, a larger system performance degradation can be expected. Second, a new kind of resilient estimator is designed, from the perspective of the defenders, to alleviate the negative effect of the attack. In a word, a novel unified attack‐defense framework for nonlinear CNs is established. In order to make up for the defect that the IDB attacker's parameter is unknown to the defender, an algorithm is developed to approximate the attack parameter. With the help of the Lyapunov functional method, sufficient conditions are obtained to resist the proposed IDB attack and ensure the performance of the augmented system. At last, two examples are given to demonstrate the destructiveness of the proposed IDB attack strategy and the effectiveness of the developed resilient estimator, respectively.

Analysis of Heat Source System Degradation Due to Aging and Evaluation of Its Effect on Energy Consumption

The performance of air conditioning systems deteriorate due to the natural aging and wear caused by operating the devices. This is termed “aging degradation,” and it results from a lack of appropriate maintenance which accelerates the degree of performance degradation. The performance degradation of an air conditioning system can cause problems such as increased energy consumption, deteriorated indoor heating environment, and shortened lifespan of air conditioning equipment. To prevent such problems, it is important to establish a long-term maintenance plan to recover degraded performance, such as predicting an appropriate maintenance time by identifying the real-time performance degradation rate based on a system’s operation data. In this study, the performance degradation rate, according to the operating time, was estimated using long-term operation data for devices constituting a heat source system, and the effect of performance degradation of the heat source system’s operation and energy consumption was reviewed using a simulation. The performance degradation rate of the target device was estimated by analyzing the variation trend of the calibration coefficient, which was calculated when the initial performance prediction model was calibrated through operating data. Using this approach, it was confirmed that the annual performance degradation rate was 1.0–1.4% for the heat source equipment, 0.4–1.2% for the cooling towers, and 0.8–1.3% for the pumps. In addition, a heat source system energy simulation calculated the 15-year performance degradation of the heat source equipment to be 34–52% and 7–19% for both the cooling towers and pumps. Due to the equipment performance deterioration, the number of operating heat source equipment and cooling tower fans, and the pump flow rate gradually increased every year, thus accelerating the performance deterioration even further. As a result, energy consumption in the 15th year increased by approximately 41% compared with the initial energy consumption.

Modular organization and informal structure: Modularity, performance, and the alignment of organizational networks

Abstract Modularizing a system, a product, or a process can have positive effects on performance in many instances. That it may have adverse consequences has also been discussed, although on balance the literature seems inclined to consider that modularity is desirable in most cases. In this paper, we put forward a feature of modular systems that extant research has not considered in spite of its strong performance-hindering potential. Starting from the premise that organizations are essentially patterns of formal and informal interactions, and modules within organizations are places where these interactions accumulate and reinforce, we argue that modularity can exacerbate issues related to (low-)performance spillovers from one organizational actor to another. With psychosocial affect as the channel connecting the formal and the informal spheres, we develop a simple model that combines elements of psychology research, network science, and organization theory to study how formal and informal elements jointly determine organizational performance under different modularity configurations. We find that organizational performance is lower when the alignment of formal and informal is stronger: informal interaction augments local accumulation of stress caused by formal aspects of firm organization. However, this result depends heavily on the modularity of formal organization: feedbacks between formal and informal elements cause much harsher performance degradation in modular systems. This suggests the exercise of caution when considering the possibility to modularize a production system.

Mixed THz/FSO Relaying Systems: Statistical Analysis and Performance Evaluation

In this paper, the performance of a mixed Terahertz/free-space optical (THz/FSO) wireless transmission system is studied, where the joint effects of channel fading and pointing errors are considered for both THz and FSO links. Employing the semi-blind amplify-and-forward protocol, we derive the cumulative distribution function and probability density function of the end-to-end signal-to-noise ratio. By applying the derived statistics, exact expressions for the outage probability (OP), average bit error rate (BER), and average channel capacity are obtained. In order to attain useful physical insights, asymptotic OP and average BER expressions are also presented. Based on them, the diversity gain is determined, which is shown to depend on channel fading and pointing errors of both links. In addition, by taking into account the hardware impairments, the OP of the non-ideal hardware system is derived. Moreover, the analysis is extended to multi-antenna scenarios and the asymptotic OP is obtained. Finally, illustrative numerical results are plotted and it can be observed that the path loss, channel fading, pointing errors and hardware impairments lead to a considerable degradation in system performance.

IoT-Based Monitoring in Carbon Capture and Storage Systems

Carbon capture and storage (CCS) is critical for climate-change policies and strategies targeting global warming within the Paris Agreement. The overarching technological requirements are well described in the strategic plans, yet several barriers exist to the technology's wide-spread deployment, including improved cost-effectiveness and enhanced process integration. For the safe and reliable operation of large-scale CCS systems, the development of effective Internet of things (IoT)-based monitoring tools to ensure flow assurance of CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> throughout the CCS value chain is crucial. Further, reliable sensor measurements related to different transport parameters such as temperature, pressure, and flow across the process are essential to develop these methods. However, sensors are prone to errors due to inherent issues or environmental conditions which result in performance degradation of the overall monitoring system. Developing techniques for detecting anomalies in the measurements, identifying the faulty sensors and accommodating them with appropriately estimated data is one of the paramount requirements for the reliable operation of the CCS systems. In such context, the present article provides an overview of CCS's monitoring and control requirements, emphasizing data-fusion synergies. This work investigates the state-of-the-art methods for sensor validation and proposes a roadmap to further deploy metering technologies for industrial needs.

Liquid film formation: prediction accuracy of different numerical approaches

In counteracting fouling phenomenon in gas turbines, which leads to system inefficiencies and performance degradation, water washing technique is very often adopted. Water droplets sprays are injected and, hitting the solid surfaces, remove the dirt deposition. Among the collateral undesirable phenomena related to water washing, blades erosion and liquid film formation are the most remarkable. Despite the former issue was extensively assessed by the authors in previous works, up to the authors’ knowledge the risk of liquid film formation due to water washing was scarcely investigated. Liquid film formation and spreading on a solid surface is a complex phenomenon involving a large number of physical events, such as: droplets impact on a solid surface, splashing phenomena, liquid film dragging under the effect of the carrier phase and droplets separation from the film in proximity of geometry discontinuities. In this paper, an extensively used experimental test case involving all these phenomena was used to test different numerical wall film models available in literature. The test case consists in the injection of a liquid jet in a high velocity crossflow. Some of the liquid jet mass impacts on the opposite solid surface generating a wall film which develops under the dragging effect of the crossflow. A Lagrangian approach was used to track the suspended droplets within the flow field by also considering the turbulent dispersion by means of a Random Walk model. Droplets-wall interaction is considered according to the Stanton-Rutland model, which provides the outcome of a collision (deposit, rebound or splashing), depending on the local impact conditions. If a droplet sticks on a solid boundary, a liquid film generates. Droplets atomization is also accounted for by using the Madabhushi model while Friederich separation model was selected to take into account the detachment of droplets from the film at the geometry edge. Three different numerical simulations have been performed based on different approaches used to solve the liquid film evolution, namely Eulerian one-way coupling, Eulerian two-way coupling and Lagrangian two-way coupling. Numerical results have been compared with the experimental ones from both a qualitative and a quantitative point of view. The wall film shape, its spatial distribution and the variation of the film thickness of the wall centreline have been compared between experimental and numerical simulations proving that the Lagrangian 2-way coupling approach better reproduces the liquid film dynamics observed in the experiments.

Cache-assisted relay selection algorithm based on incentive cooperation

An unrealistic preset condition exists in the cache-assisted relay system, namely that the social attributes of all nodes are highly overlapping and similar. In practice, however, this prerequisite is frequently not fulfilled because nodes exhibit a certain degree of selfishness. This paper, therefore, proposes a cache-assisted relay selection algorithm based on incentive cooperation to resolve the problem of system performance degradation precipitated by differences in the social attributes of nodes in cache-assisted networks. By incorporating the contribution of nodes to the system, nodes with poor social attributes are encouraged to participate in optimizing feedback effects actively. Nodes with high competition function values have a higher priority for information storage and transmission, improve the participation of low-attribute nodes, and attenuate the impact of channel changes, thereby enhancing network sustainability and system efficiency.

Load frequency predictive control for power systems concerning wind turbine and communication delay

AbstractThis article addresses a model predictive control (MPC) technique for load frequency control (LFC) system in the presence of wind power, communication delay, and denial‐of‐service (DoS) attack. In this article, communication delay is incorporated into a single area control error transmission for simplicity, wind power and load disturbance are regarded as Lipschitz nonlinear terms, as for the randomly occurring DoS attack, it is modeled as Bernoulli processes with known conditional probability. Thinking all these adverse factors to stability and the limitation of input constraint synthetically, the stability of LFC system can be guaranteed by delay‐dependent Lyapunov function lemma and a state feedback MPC controller is designed to solve the LFC problems by minimizing the infinite‐horizon objective function. Although some scholars have studied the performance degradation and instability of LFC system caused by cyber attack and/or communication delay and some very nice results have been addressed, limited works have considered the MPC approach to deal with both the problems of cyber attack and communication delay which explicitly considers the physical constraints. In addition, the delay‐dependent Lyapunov function is adopted to deal with the problem of communication delay, which results in less conservatism of the presented method. Finally, the optimization problem with input constraint is solved and proven to be recursive feasibility, and the closed‐loop system turns out to be stable. The reasonability and validity of the provided strategy is verified through several groups of simulation experiments. It illustrates that the proposed control method can keep the system frequency steady in the standard range in spite of various attack conditions.

Optical wireless communication system performance in natural water turbulence of any strength.

The recently introduced power spectrum model for natural water turbulence, i.e., that at any average temperature, average salinity, and stratification [J. Opt. Soc. Am. A37, 1614 (2020)JOAOD61084-752910.1364/JOSAA.399150], is extended from weak to moderate-to-strong regimes with the help of the spatial filtering approach. Based on the extended spectrum, the expressions for the scintillation index (SI) are obtained, and based on its signal-to-noise ratio and bit error rate of the underwater wireless optical communication (UWOC) system with the on-off-keying modulation and gamma-gamma irradiance distribution model, the analysis is performed. The obtained results are compared with those derived from the widely used Nikishov and Nikishov spectrum. It is shown that the natural water turbulence results in the SI for plane (spherical) waves attaining higher maxima values at shorter propagation distances, about 20 m (40 m) with respect to 30 m (50 m) of Nikishovs turbulence. Therefore, it predicts a stronger degradation of the UWOC system performance in weak and moderate turbulence regimes.