Software Configuration Research Articles

Цифровые платформы как инструмент замыкания производственных циклов агрокомплекса Южного федерального округа

The agro-industrial complex is a key element of the model of an economically sovereign national economy, responsible for the country’s food security. At the same time, in the conditions of intensive digitalization of the production and logistics basis of the economy, it cannot be considered as a self-sufficient entity and requires adaptation to main technological trends in its development. The defining trend for the next decade has been the technological reformatting of the infrastructure of any industry based on the convergence of its disparate elements on the basis of a specialized digital platform. The article outlines the author's proposals for ensuring the formation of an integral digital framework of agro-industrial complex platforms of the Southern Federal District (SFD) adapted to the current conditions of technological and sectoral development of the Russian economy in 2022. The needs and opportunities for introducing digital solutions into the activities of companies in the Southern Federal District engaged in agro-industrial complex business have been identified. The economic feasibility of distributing successful digital solutions implemented by large private companies in the Southern Federal District on the scale of their implementation as digital platforms at the regional level is substantiated. The economic feasibility of the technological consolidation of currently disparate participants in the agro-industrial complex chains of the Southern Federal District has also been proven based on the use of hardware and digital solutions available in Russia through the unification and harmonization of their technological processes. Based on the results of the study, the authors developed a system of economic approaches to the formation of the hardware and software configuration of digital platforms in the Southern Federal District, taking into account the current needs of agricultural producers in the Southern Federal District, global trends in the digital transformation of the production basis of the modern economy and the priorities of ensuring the national technological, food and economic security of the country on an individual scale Federal District.

Innovations in Software Configuration: Introducing a Data Comparison Tool Based on the Myers Diff Algorithm

In the realm of software project development, it's not uncommon for changes to emerge at various stages of a project's lifecycle. Such alterations can manifest in virtually every facet of the software development process, from conceptual design decisions down to the minutiae of the source code. To proficiently manage and track these dynamic changes, professionals turn to a specialized toolset known as Software Configuration Management (SCM). One of the standout features that SCM tools bring to the table is the 'diff' capability. This functionality allows developers to identify and review the disparities between two versions of source code. Recognizing the importance and utility of this feature, the primary objective of this research is to create an advanced diff application. This application, by leveraging the Myers Diff algorithm, is meticulously designed to pinpoint and showcase differences in characters between two sets of text-based data. Moreover, it accentuates these differences by visually highlighting the contrasting characters between the two datasets. To ensure the reliability and accuracy of this newly developed tool, we undertook a series of validation tests. We juxtaposed the results from our application against those from a comparable existing tool. Impressively, the discrepancies in results were minimal, with a marginal difference of just 1%. This suggests not only the utility but also the precision of our application in real-world software development scenarios.

Semantic Segmentation Network Slimming and Edge Deployment for Real-Time Forest Fire or Flood Monitoring Systems Using Unmanned Aerial Vehicles

In recent years, there has been a significant increase in the demand for unmanned aerial vehicle (UAV)-based monitoring systems to ensure proper emergency response during natural disasters such as wildfires, hurricanes, floods, and earthquakes. This paper proposes a real-time UAV monitoring system for responding to forest fires or floods. The proposed system consists of a hardware part and a software part. The hardware configuration is an embedded camera board mounted on the UAV, a Qualcomm QCS610 SoC with cores suitable for running deep learning-based algorithms. The software configuration is a deep learning-based semantic segmentation model for detecting fires or floods. To execute the model in real time on edge devices with limited resources, we used a network slimming technique which generates a lightweight model with reduced model size, number of parameters, and computational complexity. The performance of the proposed system was evaluated on the FLAME dataset consisting of forest fire images and the FloodNet dataset consisting of flood images. The experimental results showed that the mIoU of slimmed DeepLabV3+ for FLAME is 88.29%, and the inference speed is 10.92 fps. For FloodNet, the mIoU of the slimmed DeepLabV3+ is 94.15%, and the inference speed is 13.26 fps. These experimental results confirm that the proposed system is appropriate for accurate, low-power, real-time monitoring of forest fires and floods using UAVs.

Constraint-Aware Federated Scheduling for Data Center Workloads

The use of data centers is ubiquitous, as they support multiple technologies across domains for storing, processing, and disseminating data. IoT applications utilize both cloud data centers and edge data centers based on the nature of the workload. Due to the stringent latency requirements of IoT applications, the workloads are run on hardware accelerators such as FPGAs and GPUs for faster execution. The introduction of such hardware alongside existing variations in the hardware and software configurations of the machines in the data center, increases the heterogeneity of the infrastructure. Optimal job performance necessitates the satisfaction of task placement constraints. This is accomplished through constraint-aware scheduling, where tasks are scheduled on worker nodes with appropriate machine configurations. The presence of placement constraints limits the number of suitable resources available to run a task, leading to queuing delays. As federated schedulers have gained prominence for their speed and scalability, we assess the performance of two such schedulers, Megha and Pigeon, within a constraint-aware context. We extend our previous work on Megha by comparing its performance with a constraint-aware version of the state-of-the-art federated scheduler Pigeon, PigeonC. The results of our experiments with synthetic and real-world cluster traces show that Megha reduces the 99th percentile of job response time delays by a factor of 10 when compared to PigeonC. We also describe enhancements made to Megha’s architecture to improve its scheduling efficiency.

Performance evolution of configurable software systems: an empirical study

As a software system evolves, its performance can improve or degrade over time. Performance evolution is especially delicate in configurable software systems, where performance degradation may manifest only for specific configurations, making it especially hard to spot and fix. Problem. Prior work concentrated mainly on performance-bug detection and root-cause analysis of a single version of a system. The big picture of how performance co-evolves with a system and what role configurability plays is largely unclear. Approach. In an empirical study, we investigate the relation between configurability and performance evolution. Specifically, we analyze a total of 190 releases of 12 configurable real-world systems and examine the extent to which performance changes are specific to particular configurations and whether few or many configuration options cause performance changes. We triangulate our findings by analyzing change logs and commit messages of the respective projects to pin down causes of performance changes. Results. We found that almost every release of every subject system exhibits performance changes in some of their configurations. Notably, the majority of performance changes affects only a subset of the configuration space, and most performance changes are triggered by multiple options (up to 6). In a deeper analysis, we found that a considerable number of releases mention performance changes in the change log and commits: performance changes are reported in 45% and 69% of the releases in the change log and the commit messages, respectively, but only a fraction report the involved configuration options.

GPS Receiver Simplification for Low cost Applications and Multipath Mitigation Analysis on SDR based Re configurable Software Receiver

Many modern position-based applications rely heavily on the Global Navigation Satellite System (GNSS). Most applications require precise position data obtained through sophisticated hardware with a high computational capacity in the receiver. Some cost-effective applications may not require precise position data and require less complex signal processing. The use of efficient hardware and signal processing techniques to reduce the overall cost of a GNSS receiver is an active research topic. This paper considers Global Positioning System (GPS) constellation and proposes two factors to reduce the receiver complexity: sampling frequency and the number of tracking channels. A Keysight GNSS signal generator to record GPS signals, a Software Defined Radio board and a software-based GPS receiver are used in the experimentation. The sampling frequencies are 40, 20, 10 and 5 MHz considered, and tracking channels are reduced from 12 to 6 and then 4. The increase of error in the receiver position with 6 and 4 satellites is considerably small, but the number of tracking channels and signal processing requirements are reduced considerably. The GPS signals are affected by many errors; one of the significant sources of error is multipath propagation. Three distinct GPS multipath scenarios are generated for four satellite signal combinations with the GNSS simulator for the receiver performance analysis. Three multipath mitigation techniques, namely Early Minus Late (EML), Narrow correlator (NC) and strobe correlator (SC) methods, are considered because of their simple structure and fewer signal processing requirements. The error reductions of three multipath scenarios are compared, and the SC method performs better in all three multipath scenarios.

A step towards the final frontier: Lessons learned from acceptance testing of the first HPE/Cray EX 3000 system at ORNL

SummaryIn this article, we summarize the deployment of the Air Force Weather (AFW) HPC11 system at Oak Ridge National Laboratory (ORNL) including the process followed to successfully complete acceptance testing of the system. HPC11 is the first HPE/Cray EX 3000 system that has been successfully released to its user community in a federal facility. HPC11 consists of two identical 800‐node supercomputers, Fawbush and Miller, with access to two independent and identical lustre parallel file systems. HPC11 is equipped with Slingshot 10 interconnect technology and relies on the HPE Performance Cluster Manager software for system configuration. ORNL has a clearly defined acceptance testing process used to ensure that every new system deployed can provide the necessary capabilities to support user workloads. We worked closely with HPE and AFW to develop a set of tests that used the United Kingdom's Meteorological Office's Unified Model and 4‐dimensional variational data assimilation. We also included benchmarks and applications from the Oak Ridge Leadership Computing Facility portfolio to fully exercise the HPE/Cray programming environment and evaluate the functionality and performance of the system. Acceptance testing of HPC11 required parallel execution of each element on Fawbush and Miller. In addition, careful coordination was needed to ensure successful acceptance of the newly deployed lustre file systems alongside the compute resources. In this work, we present test results from specific system components and provide an overview of the issues identified, challenges encountered, and the lessons learned along the way.

Retracted: Software Development, Configuration, Monitoring, and Management of Artificial Neural Networks

Retracted: Software Development, Configuration, Monitoring, and Management of Artificial Neural Networks

Design and Research of a Field Bus Control System Laboratory for Metal Mining, Beneficiation and Metallurgy

Fieldbus control systems play a pivotal role in industries such as mining, beneficiation, and metallurgy, facilitating precise process control. However, diverse process conditions and applications often lead to challenges during system implementation. The prevalence of process control projects underscores the need for dedicated control system laboratories to address these problems. Our research delves into the complexities of process control systems, focusing on mainstream brands such as Siemens, Rockwell, and Emerson, involving analysis of network architectures, software, and hardware configurations. Through rigorous testing of real equipment systems, we uncover prevalent issues in practical control system applications. These findings guide the resolution of technical challenges faced in project control, concurrently enhancing the design and debugging prowess of engineering professionals. We also anticipate the trajectory of intelligent manufacturing, embracing collaborative manufacturing aspects in networked environments. This research establishs a robust foundation for the forthcoming generation of control network technologies specific to metal mining, beneficiation, and metallurgy.

Adaptive Petal Reflector: In-Lab Software Configurable Optical Testing System Metrology and Modal Wavefront Reconstruction.

This paper addresses two aspects of the metrology of spherical, petal polymer reflectors which are part of an effort by the European Space Agency (ESA) to develop actively controlled foldable reflectors, enabling larger apertures on CubeSats and small satellites. The first problem is that of measuring the surface figure error of the spherical reflector alone during the development phase, and to assess the quality before assembling the telescope (large stroke, low accuracy). The SCOTS (Software Configurable Optical Testing System) appears to provide a fast and satisfactory solution to this problem. The second problem is the wavefront error reconstruction when the petal reflector is mounted on the telescope, because parts of the petals are obscured by the secondary mirror, in such a way that the petals appear completely disconnected, making the gradient-based metrology impossible. Using the fact that the petals have common mechanical boundary conditions at the central support ring, the problem is solved by using a set of orthogonal modes satisfying the same boundary conditions. The vibration modes are used for this purpose; the modal amplitudes are reconstructed from slope data outside the obstruction, allowing for wavefront error reconstruction over the entire surface.

Software Product Line Maintenance Using Multi-Objective Optimization Techniques

Currently, software development is more associated with families of configurable software than the single implementation of a product. Due to the numerous possible combinations in a software product line, testing these families of software product lines (SPLs) is a difficult undertaking. Moreover, the presence of optional features makes the testing of SPLs impractical. Several features are presented in SPLs, but due to the environment’s time and financial constraints, these features are rendered unfeasible. Thus, testing subsets of configured products is one approach to solving this issue. To reduce the testing effort and obtain better results, alternative methods for testing SPLs are required, such as the combinatorial interaction testing (CIT) technique. Unfortunately, the CIT method produces unscalable solutions for large SPLs with excessive constraints. The CIT method costs more because of feature combinations. The optimization of the various conflicting testing objectives, such as reducing the cost and configuration number, should also be considered. In this article, we proposed a search-based software engineering solution using multi-objective evolutionary algorithms (MOEAs). In particular, the research was applied to different types of MOEA method: the Indicator-Based Evolutionary Algorithm (IBEA), Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), Non-dominant Sorting Genetic Algorithm II (NSGAII), NSGAIII, and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The results of the algorithms were examined in the context of distinct objectives and two quality indicators. The results revealed how the feature model attributes, implementation context, and number of objectives affected the performances of the algorithms.

Simulation of Eddy Current Rail Testing Data for Neural Networks

The present work is part of the AIFRI project (Artificial Intelligence For Rail Inspection), where we and our project partners train a neural network for defect detection and classification. Our goal at BAM is to generate artificial ultrasound and eddy current training data for the A.I. This paper has an exploratory nature, where we focus on the simulation of eddy current signals for head check cracks, one of the most important rail surface defects. The goal of this paper is twofold. On the one hand, we present our general simulation setup. This includes geometric models for head check cracks with features like branching and direction change, a model for the HC10 rail testing probe, and the configuration of the Faraday simulation software. On the other hand, we use the Faraday software to simulate eddy current testing signals with a strong focus on the influence of the damage depth on the signal, while differentiating between different crack geometries. Here, we observe an early saturation effect of the test signal at a damage depth of 2 mm (at a crack angle of 25◦ to the surface). That is about 2 mm earlier than we would expect from measurements at a crack angle of 90◦ . This behavior will be investigated further in a future paper. Finally, we interpolate the simulated signals in a two-step curve fitting process. With these interpolations we may generate eddy current test signals for any damage depth within the simulated range.

CANLay: A Network Virtualized Testbed for Vehicle Systems – Improving System Integration and Verification Efforts

AbstractIntegration and Verification activities for cyber physical system networks is a complex task requiring multiple iterations of hardware and software configurations and topologies. Such testing is crucial for the system's functionality, safety, and security during operation. A proficient approach to swiftly generate various testing setups is to employ a software‐defined system, vehicle in this case, and encapsulate in‐vehicle networking messages in a switched packet network, like Ethernet. This paper introduces CANLay, the networking foundation of a software‐defined vehicle that serves as a virtualized test bed for in‐vehicle network examination. CANLay carries controller area network data and sensor signals originating from vehicle control units and simulator systems. It functions as a communication medium between a vehicle simulator and real‐world electronic control units situated on distinct network segments. CANLay's efficacy is demonstrated for heavy vehicle network performance testing in a versatile digital engineering environment, facilitating streamlined system integration and verification efforts.

Design of a Low-Cost and Configurable Radar for Border Security

Radars are usually very expensive, and have a limited frequency range, limited instrumented range, fixed Radar type (either pulsed or Frequency Modulated Continuous Wave (FMCW)), fixed waveform, and no target simulation. This article addresses all these limitations through the development of a Low-Cost Configurable Software Defined Radio (SDR) Radar for Border Safeguarding. The Radar was used to detect humans, cars, animals, and airborne targets but this article only addresses the simulation mode of the Radar and the detection of cars. It can be stated here that the major novelty of this work is the presentation of the complete design flow of such an SDR-based radar.

HIGHLY RECONFIGURABLE SOFT-CPU BASED PERIPHERAL MODULES DESIGN

Research motivation. When developing microcontrollers, manufacturers try to include as many different types of peripherals as possible in order to increase the marketing attractiveness of their products. On the one hand, with a large assortment of various peripheral modules, it is very difficult to implement several devices of the same type in the microcontroller: manufacturers are mainly limited to 1-2 instances, in rare cases 4 modules of the same type are included. On the other hand, most software projects do not use all the peripherals of modern microcontrollers and many devices are left unused, while there may be a shortage of other types of modules. Another problem that has become especially noticeable for microcontrollers used in the field of IoT is the cryptographic protection of data that is transmitted through built-in information exchange interfaces. The main efforts of researchers and developers of cryptographic data protection methods were aimed at reducing energy-intensive operations, memory access iterations and speeding up encryption processes while maintaining a high level of cryptographic protection and enabling efficient data distribution within IoT devices networks. Research results. This paper presents an alternative approach to the manufacture of peripheral modules as part of microcontrollers. The authors propose to use a configurable software processor module based on the MIPS architecture with a reduced instruction set and limited capabilities. Conclusions. This approach would make it possible to dynamically change the functionality of peripheral modules in accordance with the requirements of the developed software solution, which in turn will increase the efficiency of the microcontroller chips capabilities utilization. In addition, the transfer of data stream encryption functions to the reconfigurable core of the peripheral module will provide fast and transparent cryptographic protection, as well as allow offloading the microcontroller core and increasing the energy efficiency of chips while reducing their production cost.

Toward an Analysis Method of Software Evolution Triggered by Place Change in Mobile Computing

Software dynamic evolution is a hot topic in software engineering. Traditional evolution is caused by the change in user requirements and running environments. However, in mobile computing systems, there is a new problem that place change leads to the change in software configuration, deployment, and function, which is called the problem of software evolution triggered by place change. Facing this new problem, traditional evolution methods fail to formally describe their evolution process and analyze their evolution performance. Therefore, a new method needs to be developed to ensure the reliability of software evolution triggered by place change. We first address various situations regarding the change of the software function caused by place change in physical space, which are described by a Bigraph model. Then, the triggering conditions for software evolution are defined, and a reaction system corresponding to software evolution is discussed. A theory about software function change caused by place change is developed, including a novel algorithm for identifying software evolution rules that involve place information and the control process for the whole software evolution (CPSE) in a mobile computing environment. Finally, some real case studies illustrate the effectiveness and correctness of our proposed method. Extensive experiments show that our algorithm CPSE outperforms Quiescence and version consistency (VC)-concurrent versions (CV) algorithms in timeliness and average disruption.

Placement of SDN controllers based on network setup cost and latency of control packets

Software-defined networking (SDN) was developed to simplify the management of computer networks. In the SDN architecture, the control layer is completely decoupled from the data layer. Moreover, network software configuration is also possible. To setup an SDN, you need a device called controller, which is not available in traditional networks. In the control layer, there are several controllers that control the data layer switches. The type, number and place of these controllers directly affect the cost and average latency of control packets. Since achieving the lowest setup cost and the minimum latency of the control packets (between switches and controllers, and among controllers) is of great importance at the same time, in this paper, a binary linear programming model is proposed to achieve the trade-off between them in wide area networks(WAN). The model determines the type, place and minimum number of required controllers by receiving the place of the switches, the amount of processing capacity of the controllers and the setup cost of each controller. Accordingly, it reduces the setup cost, average latency between switches and controllers as well as average latency among controllers in comparison with previous methods. The results of the study show that the model has the lowest setup cost and the minimum average latency of control packets (switch-to-controller and controller-to-controller) in controllers placement compared to the previous methods.

A Software Vulnerability Management Framework for the Minimization of System Attack Surface and Risk

Current Internet of Things (IoT) systems comprise multiple software systems that are deployed to provide users with the required functionalities. System architects create system blueprints and draw specifications for the software artefacts that are needed; subsequently, either custom-made software is developed according to these specifications and/or ready-made COTS/open source software may be identified and customized to realize the overall system goals. All deployed software however may entail vulnerabilities, either due to insecure coding practices or owing to misconfigurations and unexpected interactions. Moreover, software artefacts may implement a much broader set of functionalities than may be strictly necessary for the system at hand, in order to serve a wider range of needs, and failure to appropriately configure the deployed software to include only the required modules results in the further increase of the system attack surface and the associated risk. In this paper, we present a software vulnerability management framework which facilitates (a) the configuration of software to include only the necessary features, (b) the execution of security-related tests and the compilation of platform-wide software vulnerability lists, and (c) the prioritization of vulnerability addressing, considering the impact of each vulnerability, the associated technical debt for its remediation, and the available security budget. The proposed framework can be used as an aid in IoT platform implementation by software architects, developers, and security experts.

Design and Test of Control System of RF Ion Source Test Platform Based on Industrial PLC

Combined with the experimental operation requirements of the radio frequency ion source test platform, a set of the control system is designed with S7-1200 series PLC which is based on TIA Portal and WinCC configuration software. It realizes the monitoring and management of the RF ion source performance test experiment. In order to visualize the operation of the test platform, a fully graphical monitoring interface is designed. And the system has functions such as timing control, real-time status monitoring, parameter setting, data acquisition and recording, historical data query, and alarm recording. After experimental testing, the system meets the control requirements of the ion source performance testing platform and is of great significance for the fault diagnosis and safe operation of the test platform system.

Do Performance Aspirations Matter for Guiding Software Configuration Tuning? An Empirical Investigation under Dual Performance Objectives

Configurable software systems can be tuned for better performance. Leveraging on some Pareto optimizers, recent work has shifted from tuning for a single, time-related performance objective to two intrinsically different objectives that assess distinct performance aspects of the system, each with varying aspirations to be satisfied, e.g., “ the latency is less than 10s ” while “ the memory usage is no more than 1GB ”. Before we design better optimizers, a crucial engineering decision to make therein is how to handle the performance requirements with clear aspirations in the tuning process. For this, the community takes two alternative optimization models: either quantifying and incorporating the aspirations into the search objectives that guide the tuning, or not considering the aspirations during the search but purely using them in the later decision-making process only. However, despite being a crucial decision that determines how an optimizer can be designed and tailored, there is a rather limited understanding of which optimization model should be chosen under what particular circumstance, and why. In this article, we seek to close this gap. Firstly, we do that through a review of over 426 articles in the literature and 14 real-world requirements datasets, from which we summarize four performance requirement patterns that quantify the aspirations in the configuration tuning. Drawing on these, we then conduct a comprehensive empirical study that covers 15 combinations of the state-of-the-art performance requirement patterns, four types of aspiration space, three Pareto optimizers, and eight real-world systems/environments, leading to 1,296 cases of investigation. Our findings reveal that (1) the realism of aspirations is the key factor that determines whether they should be used to guide the tuning; (2) the given patterns and the position of the realistic aspirations in the objective landscape are less important for the choice, but they do matter to the extents of improvement; (3) the available tuning budget can also influence the choice for unrealistic aspirations but it is insignificant under realistic ones. To promote open science practice, we make our code and dataset publicly available at: https://github.com/ideas-labo/aspiration-study .