Improve System Availability Research Articles

An Optimized Telecommunication System Topology for Expenditure Minimization and Improved Spectral Utilization Efficiency

Capital expenditures (CAPEX) and operational expenditure (OPEX) has always been on the high side if transmission networks are not efficiently planned to minimize these costs. The arrays of antenna often sighted on mast/tower around often post a major risk to the industry as CAPEX increases with increased hardware and signal processing cost. This paper explores the use of point to multipoint (PMP) method of backhauling traffic for cognitive radio network as veritable alternative to existing transmission Point to Point (PTP) Topology in other to minimize the CAPEX and OPEX as well as improve spectra utilization efficiency. The work is focused on small cell deployment for more data penetration in a redundant setup to improve system availability. In the work, the Dijkstra Algorithm for Shortest route analysis was used to prove the advantage of Point to multi point approach in the optimal path planning in a transmission network taking cognizance of path costs as the capital expenditure, path loss, latency, throughput, frequency channel allocation. Furthermore, a spectrum utilization problem was defined, formulated and analyzed. Solution of this model was validated using a simulated setup on a section of Ikorodu (6°35”.37 N and 3°31”.53.99 E) transmission axis. Results obtained from the analysis show that our model of PMP method of transmitting/backhauling is more capable of improving the spectral efficiency of a wireless telecommunication network and outperforms the PTP topology on all network measures influencing capital and operational expenditures.

Research on the Key Technologies of Resource Dynamic Management in Cloud Computing Environment

For the "hot spots" problem caused by the number of replica could not satisfy the soaring request from users, we present a Cloud Computing storage resource replica dynamic scheduling scheme based on HDFS. There are two sub algorithms which is the number of replica dynamic adjustment algorithm and replica placement algorithm. This scheme could improve system stability and file access through put. The experiment result shows this optimized scheme lower the access delay obviously than the original scheme with some cost of storage resource and network workload. For the problem of metadata management, this paper introduced a distributed metadata management scheme. This scheme distributed metadata into several name node according to their ability, and provides an efficient inquiry algorithm for metadata to improve system availability and reliability. The experiment result shows the multi-name nodes system with this metadata management scheme could improve the performance than the original system facing the huge metadata operation requests.

Addressing concept drift to improve system availability by updating one-class data-driven models

Data-driven models have been used to detect system faults, thereby increasing industrial system availability. The ability to search data streams while dealing with concept drift are challenges for data-driven models. The objective of this work is to demonstrate a general method to manage concept drift when using one-class data-driven models. The method has been used to develop an automatically retrained and updated polygon-based model. In this paper, the available industrial data allowed for use of one-class data-driven models, and the polygon-based model was selected because it has previously been successful. Possible scenarios that allow one-class data-driven models to be retrained or updated were identified. Based on the identified scenarios, a method to automatically update a polygon-based model online is proposed. The method has been tested and verified using data collected from a Bosch Rexroth Mellansel AB hydraulic drive system. Data representing relevant faults was inserted into the data set in close collaboration with engineers from the company. The results show that the developed polygon-based model method was able to address the concept drift issue and was able to significantly improve the classification accuracy compared to the static polygon-based model. Thereby, the model could significantly improve industrial system availability when applied in the relevant production process. This paper shows that the developed polygon-based model requires small memory space while its updating procedure is simple and fast. Finally, the identified scenarios may be helpful as input for supporting other one-class data-driven models to cope with concept drift, thus increasing the generalizability of the results.

The Rise and Fall of CBM (Condition Based Maintenance)

Aircraft and avionics, offshore, marine and other complex engineering systems often operate in harsh environmental and operational conditions and must meet stringent requirements of cost, reliability, safety and maintainability. To achieve these aims modern design management and a vast array of computer aided techniques are applied during the design and testing stages. Maintainability requirements, long ignored by designers and manufacturers, assumed great importance and forced rethinking of the way the design of new systems and their maintenance should be carried out. One of the technologies to deal with this problem is Condition Based Maintenance (CBM) which is used widely in various industries to reduce risk and improve system availability. CBM is based on detection and diagnosis of failures, assessing effects of such failures on life of the system health and prescribing maintenance actions to extend system life. However, the experience with CBM shows that high expectation of reduced costs, improve maintainability etc. are often not met, and the major reasons for it are problems with risk identification (what failures can be expected?) and detection (what sensors to select?). The paper reviews these problems and other barriers to effectiveness of CBM and presents model-based approach to risk identification which can lead to improved CBM.

A novel approach of testability modeling and analysis for PHM systems based on failure evolution mechanism

Prognostics and health management (PHM) significantly improves system availability and reliability, and reduces the cost of system operations. Design for testability (DFT) developed concurrently with system design is an important way to improve PHM capability. Testability modeling and analysis are the foundation of DFT. This paper proposes a novel approach of testability modeling and analysis based on failure evolution mechanisms. At the component level, the fault progression-related information of each unit under test (UUT) in a system is obtained by means of failure modes, evolution mechanisms, effects and criticality analysis (FMEMECA), and then the failure-symptom dependency can be generated. At the system level, the dynamic attributes of UUTs are assigned by using the bond graph methodology, and then the symptom-test dependency can be obtained by means of the functional flow method. Based on the failure-symptom and symptom-test dependencies, testability analysis for PHM systems can be realized. A shunt motor is used to verify the application of the approach proposed in this paper. Experimental results show that this approach is able to be applied to testability modeling and analysis for PHM systems very well, and the analysis results can provide a guide for engineers to design for testability in order to improve PHM performance.

Improving system availability with near-miss analysis

One major function of computer security is to ensure the availability of an IT system. Software failures disrupt the availability of a system and its underlying resources. In addition, a system crash can cause data loss and data corruption, affecting the integrity of the stored information. It can also open the door for unauthorised access to confidential information by weakening security barriers such as firewalls and intrusion detection systems. Preventing major failures can thus prevent the unavailability of critical resources and any eventual information leakage and data corruption. It can also mitigate the business impact of such events, which can be disastrous.

High Availability Solution: Resource Usage Management in Virtualized Software Aging

As businesses increasingly rely on IT for their mission-critical operations, continuous availability is a universal concern. Nowadays, virtualized platform has become the popular option to deploy complex enough services. Deployed services are expected to be always, available, but these long running services are especially sensitive to suffer from software aging phenomenon. Software aging of virtual machine monitors (VMMs) is becoming critical because performance degradation or crash failure of a VMM affects all virtual machines (VMs) on it. To counteract this issue, we deploy software rejuvenation methodology. To eliminate the service outage during software rejuvenation process, we combine the rejuvenation with live migration technology. Live VM migration enables a running VM on a host serer to move onto the other host server with very small interruption of the execution. Live VM migration depends on VMs placement and efficient resource available is required. The idea behind our paper is two-fold. First, we present the optimization of the resource usage as accepting as many services as in virtualized environment which support of VM live migration. Second, to demonstrate how much it can improve system availability, the stochastic Petri nets model a virtualized server system in case of using time based software rejuvenation for VMM is presented. Finally, we perform the numerical analysis to evaluate the model.

Product support improvement by considering system operating environment

PurposeThe ongoing availability of existing industrial systems/machines depends to a great extent on the form and level of product support. Product support, or the after sale service of a product, is important because it assures the expected function of the product in its operational phase. Product support is affected by a number of factors, including system reliability and maintainability characteristics and the operating environment. The purpose of this paper is to analyze the influence of time independent external factors of industrial systems on product support requirements and spare parts need.Design/methodology/approachThis paper, after discussing the factors influencing product support, describes a method to estimate spare part requirements based on estimation of the actual reliability of a product under the influence of the product‐operating environment using a proportional hazard model. A spare parts estimation software, Spare Management Software (SMS), is used to check the results. Then a case study addresses the management of the spare parts inventory based on the geographical location and required performance of the product.FindingsThe lack of good support and critical spare parts can cause the untimely stoppage of a machine/system. The forecasting of product support and spare parts requirements based on the reliability and maintainability characteristics of systems/components, along with influencing environmental factors, is one of the most effective strategies for preventing unplanned stoppages. The operating environment of a system/machine has a considerable influence on the performance of the system and its technical characteristics, such as its reliability, maintainability, and, consequently, availability. Therefore, the system operating environment should be considered when the required support and spare parts estimation is under review.Research limitations/implicationsIn this research, the focus is on the estimation of the number of spare parts required. Only non‐repairable components/parts in repairable systems are studied. In other words, the paper considers one‐component systems or a single component within a larger system. The operation and maintenance phases are dealt with in the study, along with the external operating environment and time independent influencing factors.Practical implicationsThe introduced method for spare parts estimation will enable management to improve system availability and production line efficiency while minimizing total production costs. Consequently, the plant life cycle cost will be minimized by releasing the tied‐up costs incurred when stocking extra parts for a long time.Originality/valueThe paper provides a new outlook on product support and spare parts forecasting by taking the actual system operating environment into consideration. It helps managers and engineers to be realistic and act pragmatically while running and analyzing technical/industrial systems.

Modeling a Dynamic Data Replication Strategy to Increase System Availability in Cloud Computing Environments

Failures are normal rather than exceptional in the cloud computing environments. To improve system availability, replicating the popular data to multiple suitable locations is an advisable choice, as users can access the data from a nearby site. This is, however, not the case for replicas which must have a fixed number of copies on several locations. How to decide a reasonable number and right locations for replicas has become a challenge in the cloud computing. In this paper, a dynamic data replication strategy is put forward with a brief survey of replication strategy suitable for distributed computing environments. It includes: 1) analyzing and modeling the relationship between system availability and the number of replicas; 2) evaluating and identifying the popular data and triggering a replication operation when the popularity data passes a dynamic threshold; 3) calculating a suitable number of copies to meet a reasonable system byte effective rate requirement and placing replicas among data nodes in a balanced way; 4) designing the dynamic data replication algorithm in a cloud. Experimental results demonstrate the efficiency and effectiveness of the improved system brought by the proposed strategy in a cloud.

Benefits from condition monitoring techniques: a case study on maintenance scheduling of ball grid array solder joints

In the recent decade, more and more efforts in the field of reliability engineering are targeted towards developing condition-based maintenance (CBM) techniques for improving system availability and reducing maintenance cost. To justify the increased condition monitoring cost in a CBM programme, it is necessary to benchmark the performance of a maintenance policy enhanced by condition monitoring techniques with that of the conventional preventive maintenance (PM) policies. However, such studies are very limited in literature. This article presents a comparative study on maintenance scheduling of ball grid array (BGA) solder joints in a maintenance policy enhanced by condition monitoring techniques (policy A) and a conventional PM policy (policy B). Driven by the practical characteristics of the degradation signals of BGAs, a two-phase treatment (stable phase and unstable phase) is adopted in policy A: statistical process control techniques are used on the initial stable condition monitoring data of BGAs to detect the shift from the stable phase to the unstable phase, and trigger a traditional PM policy within the unstable phase. Benefits of the condition monitoring techniques are demonstrated in terms of a nontrivial reduction of maintenance cost rate in policy A, compared with that in policy B. From anther perspective, the current study also demonstrates the feasibility of incorporating condition monitoring techniques with the conventional PM policies for improved maintenance performance. The modelling approaches herein are generic and can be directly applied for other systems with similar degradation processes, such as bearings, mills, and gears.

Optimization of test interval for ageing equipment: A multi-objective genetic algorithm approach

Reducing the unavailability of safety systems at nuclear power plants, by utilizing the probabilistic safety assessment (PSA) methodology, is one of the prime goals in the nuclear industry. In that sense, optimization of test and maintenance activities, which are defined within the technical specifications, represents quite popular and interesting domain. Obtaining optimal test and maintenance schedule is of great significance for improving system availability and performance as well as plant availability in general. On the other side, equipment aging has gradually become a major concern in the nuclear industry since the number of safety systems components, that are approaching their wear-out stage, is rising fast. Nuclear power plants life management programs, considering safety components aging, are being developed and employed. The immense uncertainty associated to the available component aging rates databases poses significant difficulties in the process of incorporation and quantification of the aging effect within the PSA and, subsequently, in the decision-making process. In this paper, an approach for optimization of surveillance test interval of standby equipment with highly uncertain aging parameters, based on genetic algorithm technique and PSA, is presented. A standard standby safety system in nuclear power plant is selected as a case study. A Monte Carlo simulation-based approach is used to assess uncertainty propagation on system level. Optimal test interval is derived on the basis of minimal system unavailability and minimal impact of components aging parameters uncertainty. The results obtained in this application indicate the fact that risk-informed surveillance requirements differ from existing ones in technical specifications as well as show the importance of considering aging data uncertainties in component aging modeling.

Pump System Efficacy

This article proposes an analytical technique that can reduce unscheduled maintenance and save energy. Pump systems are examined for the connection between high maintenance costs and excess energy use. Basic principles are reviewed, and the practical design environment of a pump system is modeled in software. Mechanical output from the pump system is compared with the electrical power delivered. Losses are identified. The connection between low system efficiency and high maintenance is developed. An example is provided to illustrate the power of computer analysis to save costs and improve system availability.

Separating computation and storage with storage virtualization

Recent advances of hardware, software, and networks have made the management and security issues increasingly challenging in PC usage. Due to the tight coupling of hardware and software, each one of the hundreds or thousands of PCs connected in a networked environment has to be managed and administrated individually, leading to a high Total Cost of Ownership (TCO). We argue that a centralized storage of software and data, while distributed computation in clients, i.e., transparent computing, can address these challenges potentially and reduce the complexity with reduced software maintenance time, improved system availability, and enhanced security. This paper presents a novel approach, named StoreVirt, to realize transparent computing, which separates computation and storage from inside a single physical machine to different machines with a storage virtualization mechanism. With virtualization, all the OSes, applications, and data of clients are centered on the servers and scheduled on demand to run on different clients in a “block-streaming” way. Therefore, due to the central storage of OSes and applications, the installation, maintenance, and management are also centralized, leaving the clients light-weighted. Further, due to timely patching and upgrading, the system security can be improved. Experimental and real-world experiences demonstrate that this approach is efficient and feasible for real usages.

Failure-aware workflow scheduling in cluster environments

The goal of workflow application scheduling is to achieve minimal makespan for each workflow. Scheduling workflow applications in high performance cluster environments is an NP-Complete problem, and becomes more complicated when potential resource failures are considered. While more research on failure prediction has been witnessed in recent years to improve system availability and reliability, very few of them attack the problem in the context of workflow application scheduling. In this paper, we study how a workflow scheduler benefits from failure prediction and propose FLAW, a failure-aware workflow scheduling algorithm. We propose two important definitions on accuracy, Application Oblivious Accuracy (AOA) and Application Aware Accuracy (AAA), from the perspectives of system and scheduling respectively, as we observe that the prediction accuracy defined conventionally imposes different performance implications on different applications and fails to measure how that improves scheduling effectiveness. The comprehensive evaluation results using real failure traces show that FLAW performs well with practically achievable prediction accuracy by reducing the average makespan, the loss time and the number of job rescheduling.

PSO and ANN-based fault classification for protective relaying

Fault classification in electric power system is vital for secure operation of power systems. It has to be accurate to facilitate quick repair of the system, improve system availability and reduce operating costs due to mal-operation of relay. Artificial neural networks (ANNs) can be an effective technique to help to predict the fault, when it is provided with characteristics of fault currents and the corresponding past decisions as outputs. This paper describes the use of particle swarm optimisation (PSO) for an effective training of ANN and the application of wavelet transforms for predicting the type of fault. Through wavelet analysis, faults are decomposed into a series of wavelet components, each of which is a time-domain signal that covers a specific octave frequency band. The parameters selected for fault classification are the detailed coefficients of all the phase current signals, measured at the sending end of a transmission line. The information is then fed into ANN for classifying the faults. The proposed PSO-based multi-layer perceptron neural network gives 99.91% fault classification accuracy. Moreover, it is capable of producing fast and more accurate results compared with the back-propagation ANN. Extensive simulation studies were carried out and a set of results taken from the simulation studies are presented in this paper. The proposed technique when combined with a wide-area monitoring system would be an effective tool for detecting and identifying the faults in any part of the system.

Sigma Overbounding using a Position Domain Method for the Local Area Augmentaion of GPS

The local area augmentation system (LAAS) is a differential GPS navigation system being developed to support aircraft precision approach and landing navigation with guaranteed integrity and availability. While the system promises to support Category I operations, significant technical challenges are encountered in supporting Category II and III operations. The primary concern has been the need to guarantee compliance with stringent requirements for navigation availability. This paper describes how a position domain method (PDM) may be used to improve system availability by reducing the inflation factor for standard deviations of pseudo-range correction errors. Used in combination with the current range domain method (RDM), a 30% reduction in the inflation factor is achieved with the same safety standard. LAAS prototype testing verifies the utility of the PDM to enhance Category II/III user availability.

Odds-based decision-making tool for opportunistic production-maintenance synchronization

The importance of the maintenance function has increased because of its role in keeping and improving system availability and safety, as well as product quality. Indeed a new role for maintenance exists to enhance the eco-efficiency of the product lifecycle. The concept of ‘lifecycle maintenance’ emerged to stress this role leading to promote, at the manufacturing stage, an innovative culture wherein maintenance activities become of equal importance to actual production activities. This equivalence requires mainly considering the integration of the maintenance and the production strategy planning for developing opportunistic maintenance tasks preserving conjointly the product–production–equipment performances. In this paper, a novel approach is proposed for integrating maintenance into production planning. The approach uses the ‘odds algorithm’ and is based upon the theory of optimal stopping. The objective is to select, among all the production stoppages already planned, those which will be optimal to develop maintenance tasks preserving the expected product conditions. It combines criteria such as product performance and component reliability. The feasibility and benefits of this approach are investigated first from a numerical point of view and then from an industrial point of view using TELMA (TÉLé-MAintenance) platform supporting the unwinding of metal bobbins.

Epidemic state analysis of computers under malware attacks

This paper presents a stochastic model of analyzing epidemic states of computers that are infected by malicious codes. The model is based on a finite-Markovian process. The methodology presented here is unique and simple to determine the state probabilities of susceptible populations in general. Although the emphasis is on a computer-based infection model, it can be applied to various types of epidemiology. The simple epidemic model [D. Daley, J. Gani, Epidemic Modeling, an Introduction, Cambridge University Press, Cambridge, UK, 1999.] has its limitations; it has only one state transition, which goes from susceptible to infected, and hence infected forever. This paper will show that the states are recurrent. A healthy but susceptible node can become infected by a virus, can then become a transmitter, and can also return to a healthy state. Efficient mitigation approaches and optimal system reliability depend on accurately determined parameters for failure-rate and repair-rate of attacked systems. The model presented here is useful in determining state transition dynamics for estimating infection and recovery rates of susceptible systems. Thus, our model can complement the existing worm propagation models for bias-corrections and fine-tuning of worm’s dynamics. Another major contribution of this paper is to promote further studies in determining performance criteria regarding the costs of mitigation and the improved system availability.

Modelling a general standby system and evaluation of its performance

Redundancy or standby is a technique that has been widely applied to improving system reliability and availability in system design. In this paper, a general method for modelling standby system is ...

On the concept of e-maintenance: Review and current research

The importance of the maintenance 1 1 The maintenance terminology used in this paper is extracted from the European standard EN 13306:2001 de/fr/en European Standard on Maintenance Terminology. function has increased because of its role in keeping and improving system availability and safety, as well as product quality. To support this role, the development of the communication and information technologies has allowed the emergence of the concept of e-maintenance. Within the era of e-manufacturing and e-business, e-maintenance provides the opportunity for a new maintenance generation. As we will discuss later in this paper, e-maintenance integrates existing telemaintenance principles, with Web services and modern e-collaboration principles. Collaboration allows to share and exchange not only information but also knowledge and (e)-intelligence. By means of a collaborative environment, pertinent knowledge and intelligence become available and usable at the right place and time, in order to facilitate reaching the best maintenance decisions. This paper outlines the basic ideas within the e-maintenance concept and then provides an overview 2 2 Last year, within a current research project called “Modelling policies for the improvement of production systems dependability” (Project number DPI 2004-01843) and sponsored by the Spanish government, we have launched a call for paper on e-maintenance to better understand the nature and the impact of the e-maintenance phenomenon. The results were presented in different conferences and some of them are published within a special issue on e-maintenance for Computers in Industry, Guest Editors B. Iung and A. Crespo Marquez [1]. of the current research and challenges in this emerging field. An underlying objective is to identify the industrial/academic actors involved in the technological, organizational or management issues related to the development of e-maintenance. Today, this heterogeneous community has to be federated in order to bring up e-maintenance as a new scientific discipline.