High Uptime Research Articles

A High Up-Time and Security Centered Resource Provisioning Model Toward Sustainable Cloud Service Management

A High Up-Time and Security Centered Resource Provisioning Model Toward Sustainable Cloud Service Management

Novel Photocathode Lasers for the Hard- and Soft-X-ray Free Electron Lasers EuXFEL and FLASH

We developed, constructed and commissioned novel photocathode lasers for the X-ray FELs European XFEL and FLASH which allow emittance optimization via beam shaping. Both facilities operate continuously with those lasers since Jan. 2024 with high uptime and excellent performance.

Autonomous selection of the fault classification models for diagnosing microservice applications

Microservices architecture is a new approach for deploying applications and services in the cloud, gaining popularity for constructing large-scale systems that are highly resilient, robust, and adaptable to dynamic customer needs. Given that the number of services in a microservice application can be in the hundreds or thousands, the complex dependencies between them can cause faults to propagate and affect multiple services, ultimately leading to degraded system performance. To run microservices reliably and with high uptime, it is essential to quickly troubleshoot when a fault occurs. Simultaneously, there is a need for unsupervised fault diagnosis methods, as existing supervised methods are usually time and labor-intensive. As a solution to these challenges, this paper proposes the Autonomous Selection of Fault Classification models (ASFC) for Diagnosing Microservice Applications. The method can automatically select optimal models from candidate unsupervised detection models for different faults and cascade them to perform fault diagnosis. Meanwhile, our method can determine the root cause of the fault by conducting a root cause localization of faulty services. Experiments on two widely used benchmark microservices demonstrate that ASFC outperforms the baseline methods. As shown in the experimental results, our method achieves an average macro-F1 of 82.9% and 88.6% on two benchmark microservices, respectively, with an average improvement of 34.4% and 24.7% over baseline methods. Furthermore, the method achieves high accuracy in the localization of the root causes, with an average Avg@5 of 0.857 and 0.525 on two benchmark microservices.

Searching for Short-Timescale Transients in Gamma-ray Telescope Data

Astrophysical sources show variability in their emissions over a range of timescales, with transients such as fast radio bursts (FRBs) and magnetar giant flares (MGFs) showing variability on timescales as short as a few milliseconds. Recent advances in gamma-ray astronomy such as telescopes’ high temporal resolution and relatively high uptime, combined with follow-up programs between different facilities, should allow serendipitous observations of burst-like phenomena. Even so, no very-high-energy gamma-ray counterparts for FRBs have been detected so far, and there is a general lack of software tools suited to search for such phenomena. We present a tool capable of searching gamma-ray telescope data for transient phenomena over arbitrary timescales—it is based on the Gammapy package and recursively scans the given field of view for clusters of events within user-defined time and angular-separation intervals. The generalized implementation allows for its application in many other cases and multiple gamma-ray telescopes. The main features and methodology of the developed tool are presented here, along with an analysis of the open gamma ray telescope data performed using it.

High-bandwidth coherent optical communication over 10.3 km of turbulent air.

We demonstrate 111.8Gb/s coherent optical communication throughput over a 10.3km folded free-space laser range. Folded links are low complexity to establish and provide a high uptime for testing equipment. The communication signals were sourced from an un-modified commercial off-the-shelf transceiver intended for long-haul fiber networks. Wavelength dependence was explored by testing 52 optical C-band channels over the course of an evening. In the future, such high-bandwidth communications will be used in feeder links from satellites in geosynchronous orbit. Optical power measurements of the received signal are compared with atmospheric theory to determine the turbulence strength exhibited and therefore the applicability of the laser range to space-to-ground links. We show that the high-uptime, 10.3km laser range is suitable for testing high-bandwidth space-to-ground optical communication systems intended for links to geosynchronous orbit at 20°-50° elevation.

Development of cloud selection supporting model for green information and communication technology services

Purpose The purpose of the study is to develop a cloud supporting model for green computing. In today's contemporary world, information technology (IT) plays a significant role. Because of the rapid growth of the IT business and the high level of greenhouse gas emissions, salient data centers are increasingly considering green IT techniques to reduce their environmental impacts. Both developing and underdeveloped countries are widely adopting green infrastructure and services over the cloud because of its cost-effectiveness, scalability and guaranteed high uptime. Several studies have investigated the fact that cloud computing provides beyond green information and communication technology (ICT) services and solutions. Therefore, anything offered over clouds also needs to be green to reduce the adverse influence on the environment. Design/methodology/approach This paper examines the rationale for the use of green ICT in higher education and finds crucial success variables for the implementation of green ICT on the basis of an analysis of chosen educational organizations and interviews with key academic experts from the Universities of Ethiopia, in general, and BuleHora University, in particular. Findings Finally, this paper described the design and development of a green cloud selection supporting model for green ICTs in higher educational institutions that helps cloud service customers choose the most green cloud-based ICT products as well as services. Originality/value This study may be a significant source of new information for green ICT design and implementation in higher education institutions to preserve the environment and its impact on human life.

Achieving Diesel Powertrain Ownership Parity in Battery Electric Heavy Duty Commercial Vehicles Using a Rapid Recurrent Recharging Architecture

<div class="section abstract"><div class="htmlview paragraph">Battery electric vehicles (BEV) in heavy duty (HD) commercial freight transport face challenging technoeconomic barriers to adoption. Specifically, beyond safety and compliance, fleet and operational logistics require both high up-time and parity with diesel system productivity/Total Cost of Ownership (TCO) to enable strong adoption of electrified powertrains. At present, relatively high energy storage prices coupled with the increased weight of BEV systems limit the practicality of HD commercial freight transport to shorter range applications, where smaller batteries will suffice for the mission energy requirements (single operational shift). This paper presents an approach to extend the feasibility of BEV HD trucking for a broad range of applications. The concept is based on the leveraging rapid and recurrent recharging of a BEV HD truck that may either already make frequent stops due to shipment drop-offs/reloading or be required to make frequent stops along with longer missions for recharging. While the challenges of the latter are well appreciated, the concept proposed explores making minimal impact to overall mission time by targeting high C-rate charging while optimizing the frequency (miles) through which these events must occur. The concept optimizes battery size and chemistry, such that the expected life (years and total energy throughput) is balanced with the depth of discharge between recharging events, thus making complete use of the energy available through the life of the battery system. The solution is constrained to minimize the impact on payload capacity. The paper analyzes critical levers in achieving diesel price parity (based on a simplified vehicle TCO), achieved through different purchase options (including lease versus buy) and operational models (half-life swap out). Finally, the paper identifies the application design domain where these solutions are viable with limited impact on fleet operations.</div></div>

Tutorial on systems with antifragility to downtime

An antifragile system of software and stakeholders, including designers, developers, and operators, learn from incidents how to avoid outages and maintain high uptime. This tutorial article reviews how to design and operate such socio-technical systems with antifragility to downtime. It documents the importance of four design principles and two operational principles by exploring the polar opposite anti-principles and the interplay between the principles and the anti-principles. The design principles mandate a software design of separate and isolatable processes with sufficient diversity and redundancy. The processes should communicate asynchronously over an external network. The operational principles imply that the software development teams should repeatedly inject artificial failures into the production system to understand its behavior and detect and mitigate vulnerabilities as the system and its environment change.

Early Prediction of Quality Issues in Automotive Modern Industry

Many industries today are struggling with early the identification of quality issues, given the shortening of product design cycles and the desire to decrease production costs, coupled with the customer requirement for high uptime. The vehicle industry is no exception, as breakdowns often lead to on-road stops and delays in delivery missions. In this paper we consider quality issues to be an unexpected increase in failure rates of a particular component; those are particularly problematic for the original equipment manufacturers (OEMs) since they lead to unplanned costs and can significantly affect brand value. We propose a new approach towards the early detection of quality issues using machine learning (ML) to forecast the failures of a given component across the large population of units. In this study, we combine the usage information of vehicles with the records of their failures. The former is continuously collected, as the usage statistics are transmitted over telematics connections. The latter is based on invoice and warranty information collected in the workshops. We compare two different ML approaches: the first is an auto-regression model of the failure ratios for vehicles based on past information, while the second is the aggregation of individual vehicle failure predictions based on their individual usage. We present experimental evaluations on the real data captured from heavy-duty trucks demonstrating how these two formulations have complementary strengths and weaknesses; in particular, they can outperform each other given different volumes of the data. The classification approach surpasses the regressor model whenever enough data is available, i.e., once the vehicles are in-service for a longer time. On the other hand, the regression shows better predictive performance with a smaller amount of data, i.e., for vehicles that have been deployed recently.

Assessing the JEOL CRYO ARM 300 for high-throughput automated single-particle cryo-EM in a multiuser environment.

Single-particle cryo-EM has become an indispensable structural biology method. It requires regular access to high-resolution electron cryogenic microscopes. To fully utilize the capacity of the expensive high-resolution instruments, the time used for data acquisition and the rate of data collection have to be maximized. This in turn requires high stability and high uptime of the instrument. One of the first 300 kV JEOL CRYO ARM 300 microscopes has been installed at the cryo-EM facility BECM at VIB-VUB, Brussels, where the microscope is used for continuous data collection on multiple projects. Here, the suitability and performance of the microscope is assessed for high-throughput single-particle data collection. In particular, the properties of the illumination system, the stage stability and ice contamination rates are reported. The microscope was benchmarked using mouse heavy-chain apoferritin which was reconstructed to a resolution of 1.9 Å. Finally, uptime and throughput statistics of the instrument accumulated during the first six months of the facility operation in user access mode are reported.

JILA SrI optical lattice clock with uncertainty of

We report on an improved systematic evaluation of the JILA SrI optical lattice clock, achieving a nearly identical uncertainty compared to the previous strontium record set by the JILA SrII optical lattice clock at . This improves upon the previous evaluation of the JILA SrI optical lattice clock in 2013, and we achieve a more than twenty-fold reduction in systematic uncertainty to . A seven-fold improvement in clock stability, reaching for an averaging time in seconds, allows the clock to average to its systematic uncertainty in under 10 min. We improve the systematic uncertainty budget in several important ways. This includes a novel scheme for taming blackbody radiation-induced frequency shifts through active stabilization and characterization of the thermal environment, inclusion of higher-order terms in the lattice light shift, and updated atomic coefficients. Along with careful control of other systematic effects, we achieve low temporal drift of systematic offsets and high uptime of the clock. We additionally present an improved evaluation of the second order Zeeman coefficient that is applicable to all Sr optical lattice clocks. These improvements in performance have enabled several important studies including frequency ratio measurements through the boulder area clock optical network (BACON), a high precision comparison with the JILA 3D lattice clock, a demonstration of a new all-optical time scale combining SrI and a cryogenic silicon cavity, and a high sensitivity search for ultralight scalar dark matter.

How Reliable Is Your Uninterruptible Power System?: Eliminating Single Points of Failure

Industrial Uninterruptible Power Systems (UPSS) on floating offshore oil and gas platforms supply power to critical loads and require extremely high reliability and uptime. UPS configuration or topology can have a major impact on reliability and uptime, and although several UPS topologies are available on the market, space constraints can limit the options. It is important to understand topology and how the components that make up a UPS operate and impact reliability. We detail a specific UPS topology chosen for a floating production platform with high reliability requirements and space constraints. We also identify particular reliability concerns associated with that topology and outline different solutions, with their associated advantages and disadvantages.

The design and implementation of the beam diagnostics control system for HIMM

The Heavy Ion Medical Machine (HIMM) is a compact synchrotron based facility built by the Institute of Modern Physics (IMP) in China. This facility delivers carbon ion beams with kinetic energies up to 400 MeV/u for clinical applications. The beam diagnostics system for HIMM is based on 18 different types of monitors and is used to measure all relevant beam parameters from the ion source to the irradiation therapy terminals which are critical for the commissioning of the accelerator as well as during the regular operation to ensure high up-time of the machine. The control system for all the monitors is designed, developed and implemented globally based on a 3-tier architecture with the aim to achieve a clear separation between front-end devices and their controllers. This paper presents the architecture, the design principle and the implementation of this control system. The beam parameters measured based on the detectors′ motion control and data acquisition control system are analyzed in detail. Furthermore, this beam diagnostics control system (BDCS) will be transplanted to the next heavy ion medical machine in Lanzhou city on account of the successful application in Wuwei city, the precise beam parameters measurement, and long term operation with high stability and reliability.

Applying Event Stream Processing to Network Online Failure Prediction

Predicting failures on networks and systems is critical in order to maintain high uptime rates. Online failure prediction (OFP) techniques use machine learning and predictive analytics to generate failure models that can be applied to computer network data. These techniques can be provisioned on state-of-the-art stream processing systems, such as Spark Streaming, in order to cope with the scalability challenges from the base data. A big challenge with OFP is selecting the right information to process, as well as the appropriate features in order to achieve high accuracy in predicting failures on complex, interconnected systems. In this article we describe an OFP system built over Apache Spark that takes a repository of network management events, trains a Random Forest model, and uses this model to predict the appearance of future events in near real time. We show through our experiments the usefulness of network management events for accurate predictions, and the advantages of the proposed system in terms of predictive quality, cost, and ease of deployment.

Design and development of an automated storage organiser

Automated Storage and Retrieval systems are widely used in warehouse management due to their high uptime reliability and effective management. In India most of the warehouses and supermarkets refill their goods manually to avoid the high cost of automation. This situation can be avoided by implementing a storage and retrieval organiser which acts according to the user. In this project an Automated Storage Organiser (ASO) with 3 degree of freedom (DOF) has been built to handle light weight goods of about 1kg. The storage organiser detects the product on arrival and stores it automatically, retrieval is done by using a graphical user interface developed using LabVIEW software. The model has been developed using CATIA software and fabricated by necessary actuators and sensors. The main processing unit is Atmel ATmega 326. Virtual control module has been developed by using LabVIEW graphical programming software in computer so that user can directly control and monitor the movement of the system. Arduino programming has been used to develop a closed loop control algorithm for accuracy in position and speed control

Dedicated Protection for Survivable Virtual Network Embedding

Network virtualization is enabling infrastructure providers (InPs) to offer new services to service providers (SPs). InPs are usually bound by service level agreements to ensure various levels of resource availability for different SPs’ virtual networks (VNs). They provision redundant backup resources while embedding an SP’s VN request to conform to the SLAs during physical failures in the infrastructure. An extreme backup resource provisioning is to reserve a mutually exclusive backup of each element in an SP’s VN request. Such dedicated protection scheme can enable an InP to ensure fast VN recovery, thus, providing high uptime guarantee to the SPs. In this paper, we study the 1 + 1-Protected Virtual Network Embedding (1 + 1-ProViNE) problem. We propose Dedicated Protection for Virtual Network Embedding ( DRONE ), a suite of solutions to the 1 + 1-ProViNE problem. DRONE includes an integer linear programming formulation for optimal solution ( OPT-DRONE ) and a heuristic ( FAST-DRONE ) to tackle the computational complexity of the optimal solution. Trace driven simulations show that FAST-DRONE allocates only 14.3% extra backup resources on average compared to the optimal solution, while executing 200–1200 times faster. Simulation results also show that FAST-DRONE can accept four times more VN requests on average compared to the state-of-the-art solution for providing dedicated protection to VNs.

A new method for compensation and rematch of cavity failure in the C-ADS linac**Supported by China ADS Project (XDA03020600) and Natural Science Foundation of China (11575216)

For proton linear accelerators used in applications such as accelerator-driven systems, due to the nature of the operation, it is essential for the beam failure rate to be several orders of magnitude lower than usual performance of similar accelerators. A fault-tolerant mechanism should be mandatorily imposed in order to maintain short recovery time, high uptime and extremely low frequency of beam loss. This paper proposes an innovative and challenging way for compensation and rematch of cavity failure using fast electronic devices and Field Programmable Gate Arrays (FPGAs) instead of embedded computers to complete the computation of beam dynamics. A method of building an equivalent model for the FPGA, with optimization using a genetic algorithm, is shown. Results based on the model and algorithm are compared with TRACEWIN simulation to show the precision and correctness of the mechanism.

Focus on microscopy and photonics

Focus on microscopy and photonics

Two instruments based on differential optical absorption spectroscopy (DOAS) to measure accurate ammonia concentrations in the atmosphere

Abstract. We present two Differential Optical Absorption Spectroscopy (DOAS) instruments built at RIVM: the RIVM DOAS and the miniDOAS. Both instruments provide virtually interference-free measurements of NH3 concentrations in the atmosphere, since they measure over an open path, without suffering from inlet problems or interference problems by ammonium aerosols dissociating on tubes or filters. They measure concentrations up to at least 200 μg m−3, have a fast response, low maintenance demands, and a high up-time. The RIVM DOAS has a high accuracy of typically 0.15 μg m−3 for ammonia for 5-min averages and over a total light path of 100 m. The miniDOAS has been developed for application in measurement networks such as the Dutch National Air Quality Monitoring Network (LML). Compared to the RIVM DOAS it has a similar accuracy, but is significantly reduced in size, costs, and handling complexity. The RIVM DOAS and miniDOAS results showed excellent agreement (R2 = 0.996) during a field measurement campaign in Vredepeel, the Netherlands. This measurement site is located in an agricultural area and is characterized by highly variable, but on average high ammonia concentrations in the air. The RIVM-DOAS and miniDOAS results were compared to the results of the AMOR instrument, a continuous-flow wet denuder system, which is currently used in the LML. Averaged over longer time spans of typically a day, the (mini)DOAS and AMOR results agree reasonably well, although an offset of the AMOR values compared to the (mini)DOAS results exists. On short time scales, the (mini)DOAS shows a faster response and does not show the memory effects due to inlet tubing and transport of absorption fluids encountered by the AMOR. Due to its high accuracy, high uptime, low maintenance and its open path, the (mini)DOAS shows a good potential for flux measurements by using two (or more) systems in a gradient set-up and applying the aerodynamic gradient technique.

Tension monitoring in a belt-driven automated material handling system

The extension of the useful life of manufacturing equipment is one of the keys to sustainable manufacturing. The high uptime requirements of the semiconductor industry result in the need for conservative preventive maintenance schemes, which leads to replacement of key components before the end of their useful life. This paper presents the results of research toward a more intelligent condition based maintenance scheme for belt monitoring in a belt driven automated material handling system. An experimental study of belt dynamics showed that transverse belt vibrations were sensitive to changes in belt length, belt tension, belt misalignment, and excitation location. Based on these findings, a novel, contact based device was designed to consistently excite belt vibrations in the material handling system with greatly reduced variations in belt length and initial condition location. On average, standard deviations of tension estimates using the device were 65% lower than that of a trained technician performing the current standard technique on three different robots. This design was then further adapted to facilitate a non-contact belt excitation and monitoring approach that did not require bringing the material handling system offline in order to obtain sensor readings. Such a procedure greatly eased and accelerated the monitoring process.