High System Availability Research Articles

STUDY OF MULTI-STAGE SOFTWARE DEPLOYMENT STRATEGYWITH THE EXCLUSION OF SIDE EFFECTS

The article addresses a problem and its solution that arose during onboarding of new subscribers to the existing data import and distribution service. The analysis of all available solutions demonstrates that none fully meet the requirements. The process of searching for the values of the required indicators and the process of creating reports necessarily uses indexes. Messages of different types are placed in their respective MongoDB collections. Over the long period of operation of the repository, up to 64 indexes per collection have accumulated. According to the policies of connecting to the storage (Connect timeout) and waiting for a response (Socket timeout), the need to set such long-time expectations for consumers is unacceptable and usually, by default, is equal to 30 seconds. A multi-step strategy for delivering the solution that allows the service to meet availability requirements was proposed. The novelty of the article is in the description and investigation of a new method of delivering solutions to the software deployment environment—production. The implementation of the described strategy opens a new approach for solving similar classes of problems related to the type of interaction with users, when custom settings are a mandatory part of the service and are guaranteed to carry the threat of service unavailability. This approach opens up new opportunities for achieving high availability of systems, reducing risks and increasing user satisfaction. Based on the analysis of the proposed approaches, the latest strategy of multi-step delivery of the service to users was developed, the result of which was the acceptable response waiting time of up to 5 seconds. The implementation of this strategy contributed to the achievement of the availability of software products - 99.999%, ensuring the reliability and stability of services for end users.

Corrections to “A Comparative Analysis of Metaheuristic Techniques for High Availability Systems”

Corrections to “A Comparative Analysis of Metaheuristic Techniques for High Availability Systems”

A Comparative Analysis of Metaheuristic Techniques for High Availability Systems

A Comparative Analysis of Metaheuristic Techniques for High Availability Systems

Optimization of parametric problems with a negative value of the objective function using the ant colony method

A modification of the ant colony method is considered, which makes it possible to search for a rational (optimal) solution to a parametric problem with the possibility of obtaining a negative value of the objective function. The objective function in the original algorithm that calculates the traveling salesman's path is determined by the total length of the path and cannot be negative. But when solving parametric problems of optimization of difference values for high availability systems, negative values of the objective function may arise. Such values lead to a negative value of the weights (pheromone), which leads to incorrect operation of the probabilistic arc/vertex selection formula. A formula has been derived for recalculating the positive shift of the objective function and pheromone to obtain a positive value of the pheromone, which is used when the sum of the value of the objective function and the shift is negative. The approaches are compared: initial shift, single shift, calculation of the module of the objective function, and a gradual iterative shift algorithm is proposed. The proposed approach allows us to equate the minimum value of the objective function to 0 and mathematically recalculate the weights (pheromone) for the parametric problem taking into account the resulting shift. By applying relative values of the weights, such a shift does not affect the probability distribution, but takes into account new values for previous calculations of the objective function. The studies were carried out on various benchmarks with optimal values in the negative region. Statistically, the proposed algorithm is not inferior in performance to shift and modular algorithms for converting the value of the objective function to the positive semi-axis.

Studying on failure compensation in low-energy region of superconducting cavity in CAFe2

The high availability of Accelerator-Driven Sub-critical system (ADS) facilities poses a major challenge for the accelerators, which sets stringent requirements on the beam trip rate. However, beam trips caused by component failures in the low-energy region of the superconducting (SC) radio-frequency (RF) cavity are always a bottleneck for the long-term stable operation of high-power beams. In this paper, we present a compensation method. This method can be used to compensate for cavity faults that occur at low-energy ([Formula: see text][Formula: see text]MeV). The compensation is achieved by rematching the beam with nearby components and compensating for energy using all cavities. The new Epeak and RF phases of the remaining components are calculated in the simulation environment using a multi-objective genetic algorithm. The compensation scheme is then tested on the super-heavy element research accelerator II (CAFe2). The experimental results show that the scheme can compensate for the energy, maintaining the same level as the normal operation status, while keeping the transmission efficiency [Formula: see text]. The entire compensation process takes only 36[Formula: see text]s. This is the first compensation experiment in the low-energy region, and it demonstrates the feasibility of long-term stable operation.

PENERAPAN REDUNDANCY FIREWALL PFSENSE MENGGUNAKAN METODE CARP DENGAN PFSYNC DAN XMLRPC SYNC

The use of information technology (IT) has grown rapidly in various sectors, such as business, industry, education, healthcare, and government. With the increasing dependence on IT, it is important for organizations to ensure high availability of the firewall system. When the firewall experiences problems or is unavailable, computer networks can be vulnerable to cyber attacks such as DoS (Denial of Service), Port Scanning, and others, resulting in financial loss, reputation damage, and loss of customer trust. On the other hand, high availability of the firewall system can help organizations meet regulatory requirements, maintain customer trust, and improve operational efficiency. The method used in this research is the Network Development Life Cycle (NDLC), which consists of six stages: Analysis, Design, Simulation Prototyping, Implementation, Monitoring, and Management. From the test results, it can be concluded that after testing 10 times with the CARP method, sending 30 packets and then the master node dies, this method has an average of 27.2 packets received, downtime of around 2.8 seconds, and packet loss of around 9.2%. These results make the CARP method have high availability against system or network failures.

Adaptable 2D to 3D Stereo Vision Image Conversion Based on a Deep Convolutional Neural Network and Fast Inpaint Algorithm.

Algorithms for converting 2D to 3D are gaining importance following the hiatus brought about by the discontinuation of 3D TV production; this is due to the high availability and popularity of virtual reality systems that use stereo vision. In this paper, several depth image-based rendering (DIBR) approaches using state-of-the-art single-frame depth generation neural networks and inpaint algorithms are proposed and validated, including a novel very fast inpaint (FAST). FAST significantly exceeds the speed of currently used inpaint algorithms by reducing computational complexity, without degrading the quality of the resulting image. The role of the inpaint algorithm is to fill in missing pixels in the stereo pair estimated by DIBR. Missing estimated pixels appear at the boundaries of areas that differ significantly in their estimated distance from the observer. In addition, we propose parameterizing DIBR using a singular, easy-to-interpret adaptable parameter that can be adjusted online according to the preferences of the user who views the visualization. This single parameter governs both the camera parameters and the maximum binocular disparity. The proposed solutions are also compared with a fully automatic 2D to 3D mapping solution. The algorithm proposed in this work, which features intuitive disparity steering, the foundational deep neural network MiDaS, and the FAST inpaint algorithm, received considerable acclaim from evaluators. The mean absolute error of the proposed solution does not contain statistically significant differences from state-of-the-art approaches like Deep3D and other DIBR-based approaches using different inpaint functions. Since both the source codes and the generated videos are available for download, all experiments can be reproduced, and one can apply our algorithm to any selected video or single image to convert it.

Subsea Injection Systems Enable Carbon Capture and Sequestration

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 32460, “Subsea CO2 Injection Systems for CCS Applications: Themes, Challenges, and Opportunities,” by Julian P. Tucker and Kamaldip Randhawa, Baker Hughes. The paper has not been peer reviewed. Copyright 2023 Offshore Technology Conference. Reproduced by permission. _ The complete paper presents a discussion of challenges and opportunities relating to subsea system design for CO2 injection in carbon capture and storage (CCS) applications. The scope of the text includes field development considerations, systems operation and control, intervention, monitoring, and barrier testing. A global review of existing and planned CCS developments is used to identify common system characteristics and core functional requirements. The paper reviews topics associated with CO2 transportation, injection, and storage across multiple disciplines to provide holistic guidance and commentary specifically for subsea system design relating to CO2 injection for CCS applications. Value Drivers For widespread adoption of CCS, dedicated geologic carbon storage (GCS) must be a commercially viable activity. Simply put, the cost of storage per tonne of CO2 must be less than the price that can be charged for storage as a service. Financial incentives also must be present to support the carbon price for development to take place. Transitional incentives to close the gap between costs and the low value of CO2 emissions reduction also may be required to support development. In all cases, however, the common denominator is that the cost of storage per tonne must be as low as possible. This is driven by the capital expenditure and operating costs of a project (as well as post-injection-period obligations), which include the cost of CO2 transportation and injection systems and associated equipment. Types of Transportation and Storage Networks For dedicated GCS, several types of offshore storage systems are available that are linked to different types of emitters, transportation networks, and value chains. Storage systems linked to natural gas processing facilities may be considered single-point emitters. The overall architecture of these systems, however, will depend on where the CO2-rich production stream is processed and separated before reinjection. The most common method of CO2 transportation to offshore locations is through a subsea pipeline, which enables a continuous flow of CO2 and a high system availability. It is worth mentioning that some developments also have considered transportation to offshore storage sites by ship, with injection through an intermediate floating storage vessel. Once installed, a pipeline provides continuous injection with high availability, whereas a vessel will be affected by transit time, weather conditions, downtime caused by maintenance, and high operating costs owing to fuel use. Furthermore, challenges with field expansion also may result.

Validation of High-Availability Model for Edge Devices and IIoT

Competitiveness in industry requires smooth, efficient, and high-quality operation. For some industrial applications or process control and monitoring applications, it is necessary to achieve high availability and reliability because, for example, the failure of availability in industrial production can have serious consequences for the operation and profitability of the company, as well as for the safety of employees and the surrounding environment. At present, many new technologies that use data obtained from various sensors for evaluation or decision-making require the minimization of data processing latency to meet the needs of real-time applications. Cloud/Fog and Edge computing technologies have been proposed to overcome latency issues and to increase computing power. However, industrial applications also require the high availability and reliability of devices and systems. The potential malfunction of Edge devices can cause a failure of applications, and the unavailability of Edge computing results can have a significant impact on manufacturing processes. Therefore, our article deals with the creation and validation of an enhanced Edge device model, which in contrast to the current solutions, is aimed not only at the integration of various sensors within manufacturing solutions, but also brings the required redundancy to enable the high availability of Edge devices. In the model, we use Edge computing, which performs the recording of sensed data from various types of sensors, synchronizes them, and makes them available for decision making by applications in the Cloud. We focus on creating a suitable Edge device model that works with the redundancy, by using either mirroring or duplexing via a secondary Edge device. This enables high Edge device availability and rapid system recovery in the event of a failure of the primary Edge device. The created model of high availability is based on the mirroring and duplexing of the Edge devices, which support two protocols: OPC UA and MQTT. The models were implemented in the Node-Red software, tested, and subsequently validated and compared to confirm the required recovery time and 100% redundancy of the Edge device. In the contrast to the currently available Edge solutions, our proposed extended model based on Edge mirroring is able to address most of the critical cases, where fast recovery is required, and no adjustments are needed for critical applications. The maturity level of Edge high availability can be further extended by applying Edge duplexing for process control.

An evaluation of relational and NoSQL distributed databases on a low-power cluster.

The constant growth of social media, unconventional web technologies, mobile applications, and Internet of Things (IoT) devices create challenges for cloud data systems in order to support huge datasets and very high request rates. NoSQL databases, such as Cassandra and HBase, and relational SQL databases with replication, such as Citus/PostgreSQL, have been used to increase horizontal scalability and high availability of data store systems. In this paper, we evaluated three distributed databases on a low-power low-cost cluster of commodity Single-Board Computers (SBC): relational Citus/PostgreSQL and NoSQL databases Cassandra and HBase. The cluster has 15 Raspberry Pi 3 nodes with Docker Swarm orchestration tool for service deployment and ingress load balancing over SBCs. We believe that a low-cost SBC cluster can support cloud serving goals such as scale-out, elasticity, and high availability. Experimental results clearly demonstrated that there is a trade-off between performance and replication, which provides availability and partition tolerance. Besides, both properties are essential in the context of distributed systems with low-power boards. Cassandra attained better results with its consistency levels specified by the client. Both Citus and HBase enable consistency but it penalizes performance as the number of replicas increases.

Cryogenic hydrogen Moderator infrastructure at ESS

The European Spallation Source (ESS) in Lund, Sweden, is designed to become the most powerful spallation neutron source in the world. As one subsystem of the Target Station, which was developed and built at Central Institute of Engineering, Electronics and Analytics – Engineering and Technology (ZEA-1) of Forschungszentrum Juelich, the cold Moderator slows down high-energy neutrons from the spallation process. To gain maximum neutron flux intensities along with high system availability for condensed and soft matter research, an optimized liquid hydrogen Moderator circuit has been developed. Hydrogen with a pressure around 1 MPa, a temperature around 20 K, and a para-hydrogen fraction of at least 0.995 will be utilized to interact with neutrons in a unique cold Moderator vessel arrangement. Hydrogen conversion from ortho- to para-hydrogen will be controlled using a catalyst. Two turbo pumps are arranged in series and circulate the cryogen. A helium refrigerator, the Target Moderator Cryoplant (TMCP), continuously recools the hydrogen mass flow. Pressure stabilization is achieved by a pressure control buffer. The individual ESS Cryogenic Moderator System (CMS) components, the first and second generation of hydrogen Moderators (BF1 and BF2) and a first draft of a deuterium Moderator upgrade are presented.

Mutually Protected Coherent P2MP Networks Enabled by Frequency Comb and Injection Locking

We propose a novel network protection scheme that features mutual protection between adjacent P2MP coherent networks without requiring complex switching or redundant devices. High system availability and fast service restoration have been achieved by utilizing coherent transceivers with remotely delivered optical carriers that are injection locked to an optical frequency comb. System functionality and performance have been verified experimentally. The mutual protection design was also verified using commercially available coherent optics. Full-duplex design that features simultaneous bidirectional transmission using the same wavelength has also been tested in the coherent P2MP system.

IoT-Powered UPS Battery Monitoring: Ensuring High availability and reliability for Critical Systems

An Uninterrupted Power Supply (UPS) is a critical component in any high availability system. However, the effectiveness of a UPS depends largely on its battery backup, which must be continuously monitored to ensure that it is working properly. In the past, this monitoring has been done manually or through local monitoring systems, but advances in IoT technology now make it possible to remotely monitor the status of UPS batteries and receive real-time alerts if any issues arise. The proposed system will have several benefits over existing battery monitoring systems. First, it will be fully automated, reducing the need for manual monitoring and minimizing the risk of human error. Second, it will be accessible remotely, allowing system administrators to monitor the status of the battery backup system from anywhere in the world. Finally, the system will be scalable, allowing additional sensors to be added to the network as needed. Nevertheless, physically checking the UPS battery is highly challenging since it requires more money and time. Data center operators, at the centre of the digital economy, are under pressure from several directions. sustaining the highest level of availability at the most affordable level. A leading provider of battery management solutions.

DATA CONSISTENCY MODELS IN APPLICATIONS WITH MICROSERVICE ARCHITECTURE

Individual database transactions can easily meet ACID requirements and ensure strong consistency, but distributed transactions have a number of limitations to address. Hard transactions are are found to be fully ACID compliant, while using flexible transactions isolation is not fully guaranteed. In practice, the isolation requirement is waived to some extent to ensure high throughput and system performance. Flexible transactions generally adhere to the Basic Availability, Flexible State, Eventually Consistency (BASE) theory. The BASE theorem is an extension of the CAP theorem. This is the balance between consistency and availability in CAP. According to the BASE theory, we cannot achieve strong consistency, however, each program can achieve ultimate consistency by using an appropriate method according to its own characteristics. CAP shows that a distributed system cannot achieve consistency, availability, and distribution resilience at the same time. It is worth to pay attention at the stage of system design. It was found that hard transactions are strive for persistent consistency, and so they sacrifice high availability. Flexible transactions sacrifice consistency in exchange for high system availability.

Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS

Forest fires are a significant threat to the ecological system’s stability. Several attempts have been made to detect forest fires using a variety of approaches, including optical fire sensors, and satellite-based technologies, all of which have been unsuccessful. In today’s world, research on flying ad hoc networks (FANETs) is a thriving field and can be used successfully. This paper describes a unique clustering approach that identifies the presence of a fire zone in a forest and transfers all sensed data to a base station as soon as feasible via wireless communication. The fire department takes the required steps to prevent the spread of the fire. It is proposed in this study that an efficient clustering approach be used to deal with routing and energy challenges to extend the lifetime of an unmanned aerial vehicle (UAV) in case of forest fires. Due to the restricted energy and high mobility, this directly impacts the flying duration and routing of FANET nodes. As a result, it is vital to enhance the lifetime of wireless sensor networks (WSNs) to maintain high system availability. Our proposed algorithm EE-SS regulates the energy usage of nodes while taking into account the features of a disaster region and other factors. For firefighting, sensor nodes are placed throughout the forest zone to collect essential data points for identifying forest fires and dividing them into distinct clusters. All of the sensor nodes in the cluster communicate their packets to the base station continually through the cluster head. When FANET nodes communicate with one another, their transmission range is constantly adjusted to meet their operating requirements. This paper examines the existing clustering techniques for forest fire detection approaches restricted to wireless sensor networks and their limitations. Our newly designed algorithm chooses the most optimum cluster heads (CHs) based on their fitness, reducing the routing overhead and increasing the system’s efficiency. Our proposed method results from simulations are compared with the existing approaches such as LEACH, LEACH-C, PSO-HAS, and SEED. The evaluation is carried out concerning overall energy usage, residual energy, the count of live nodes, the network lifetime, and the time it takes to build a cluster compared to other approaches. As a result, our proposed EE-SS algorithm outperforms all the considered state-of-art algorithms.

Optimization of Two-Dimensional Extended Warranty Scheme for Failure Dependence of a Multi-Component System with Improved PSO–BAS Algorithm

Human society is entering the era of Industry 4.0; engineering systems are becoming more complex, which increases the difficulties of maintenance support work. Failure dependence exists widely in multi-component systems. In this work, a model of two-dimensional (2D) warranty decision making was constructed by using a failure-dependence analysis for multi-component systems and by considering the extended warranty cost and the system availability. The decision was to cut the warranty cost as much as possible for manufacturers, while the constraint condition was the minimum acceptable availability for the customer. The model combined preventive maintenance as well as corrective maintenance strategies. Under the condition that the multi-component system is replaced upon the expiration of the extended warranty (EW), the optimal 2D EW duration and preventive maintenance interval could be obtained through the model. In a case analysis, the optimal EW scheme for the gearbox of an electric multiple unit (EMU) system was obtained by using a grid search algorithm, a PSO algorithm, and a PSO–BAS algorithm. Through comparison, the PSO–BAS algorithm obtained a better scheme with lower warranty costs and higher system availability. A comparative analysis and a sensitivity analysis showed that the model provides a theoretical basis for manufacturers to optimize their 2D extended gearbox warranties.

A Systematic Review on Hybrid Intrusion Detection System

As computer networks keep growing at a high rate, achieving confidentiality, integrity, and availability of the information system is essential. Intrusion detection systems (IDSs) have been widely used to monitor and secure networks. The two major limitations facing existing intrusion detection systems are high rates of false-positive alerts and low detection rates on zero-day attacks. To overcome these problems, we need intrusion detection techniques that can learn and effectively detect intrusions. Hybrid methods based on machine learning techniques have been proposed by different researchers. These methods take advantage of the single detection methods and leverage their weakness. Therefore, this paper reviews 111 related studies in the period between 2012 and 2022 focusing on hybrid detection systems. The review points out the existing gaps in the development of hybrid intrusion detection systems and the need for further research in this area.

Improving IoT data availability via feedback- and voting-based anomaly imputation

In the era of the Internet of Everything, the massive Internet of Things (IoT) data bring multiple challenges to IoT development, high system availability, and ultimately the quality of service (QoS) improvement of IoT systems. Abnormal data is one of the main reasons that lead to unexpected processing results and fatal functionality faults in IoT systems, which also hinder the development and deployment of IoT. Existing outlier identification approaches detect anomalies in IoT without further processing these anomalies to improve the availability of datasets. In this paper, we develop a feedback- and voting-based anomaly imputation technique that improves IoT data availability by imputing anomalous sensor data. Specifically, we first propose a feedback- and voting-based feature selection method that extracts crucial features from raw datasets by using mini-batch data. We then utilize the selected features to detect anomalies, followed by an anomaly imputation technique that replaces abnormal data in raw datasets. Experimental results show that the proposed dataset anomaly imputation technique can achieve anomaly detection accuracy and availability improvement of up to 94.88% and 97.96%, respectively.

Approach for Digitalization of Maintenance Processes within the Aircraft Cabin

<div class="section abstract"><div class="htmlview paragraph">Enhancing the comfort for passengers, airlines are constantly increasing the number of services within the aircraft cabin such as meal ordering directly from passenger seats. The payment and menu selection can be completely processed by means of a passenger-individual airline user account also considering the remaining inventory of the galley. The implementation of such type of services is supported by digitalization of cabin business processes. For these new services the airline requires a high availability of the process-related system functions to ensure airline’s revenue and customer satisfaction.</div><div class="htmlview paragraph">A possible approach to reach the target of high availability of these functions is to use the trend of digitalization for improving function-relevant maintenance processes within the aircraft cabin. This requires an introduction of flexible communication architectures to enhance the existing maintenance process by establishing an automated fault detection, root cause analysis, and/or even fault prediction. For implementation it is necessary to provide system data and parameters digitally, e.g. by sensor measurements or an online evaluation of already existing system data logs. An example is the working light within the aircraft galley used to create an appropriate working environment for the cabin crew to prepare ordered meals for the passengers. Currently, fault detection is only possible by identifying its failure effect, i.e. a not illuminating lighting unit. However, by measuring the current consumption of the light source via a digital sensor, it would be possible to detect a changing trend in the current consumption and, in turn, triggering a maintenance task to replace the light before it actually fails. The evaluation of such current measurement with the resulting fault prediction would expand the maintenance system by a new function.</div><div class="htmlview paragraph">This paper describes how digitally supportable maintenance processes for any cabin system can be systematically identified and which system modifications are necessary for realization. Further-more, a methodology for cost and benefit assessment is introduced to find the right level of digitization for the cabin system under analysis.</div></div>

Crowd tracking and monitoring middleware via Map-Reduce

This paper presents the design, implementation, and operation of a novel distributed fault-tolerant middleware. It uses interconnected WSNs that implement the Map-Reduce paradigm, consisting of several low-cost and low-power mini-computers (Raspberry Pi). Specifically, we explain the steps for the development of a novice, fault-tolerant Map-Reduce algorithm which achieves high system availability, focusing on network connectivity. Finally, we showcase the use of the proposed system based on simulated data for crowd monitoring in a real case scenario, i.e. a historical building in Greece (M. Hatzidakis’ residence). The technical novelty of this article lies in presenting a viable low-cost and low-power solution for crowd sensing without using complex and resource-intensive AI structures or image/video recognition techniques.