Software Execution Environment Research Articles

Introduction of the Capsules environment to support further growth of the SBGrid structural biology software collection.

The expansive scientific software ecosystem, characterized by millions of titles across various platforms and formats, poses significant challenges in maintaining reproducibility and provenance in scientific research. The diversity of independently developed applications, evolving versions and heterogeneous components highlights the need for rigorous methodologies to navigate these complexities. In response to these challenges, the SBGrid team builds, installs and configures over 530 specialized software applications for use in the on-premises and cloud-based computing environments of SBGrid Consortium members. To address the intricacies of supporting this diverse application collection, the team has developed the Capsule Software Execution Environment, generally referred to as Capsules. Capsules rely on a collection of programmatically generated bash scripts that work together to isolate the runtime environment of one application from all other applications, thereby providing a transparent cross-platform solution without requiring specialized tools or elevated account privileges for researchers. Capsules facilitate modular, secure software distribution while maintaining a centralized, conflict-free environment. The SBGrid platform, which combines Capsules with the SBGrid collection of structural biology applications, aligns with FAIR goals by enhancing the findability, accessibility, interoperability and reusability of scientific software, ensuring seamless functionality across diverse computing environments. Its adaptability enables application beyond structural biology into other scientific fields.

Intelligent Correction System of Students’ English Pronunciation Errors Based on Speech Recognition Technology

With the increasingly frequent international exchanges, English has become a common language for communication between countries. Under this research background, in order to correct students’ wrong English pronunciation, an intelligent correction system for students’ English pronunciation errors based on speech recognition technology is designed. In order to provide a relatively stable hardware correction platform for voice data information, the sensor equipment is optimised and combined with the processor and intelligent correction circuit. On this basis, the MLP (Multi-layer Perceptron) error correction function is defined; with the help of the known recognition confusion calculation results, the actual input speech error is processed by gain mismatch; and the software execution environment of the system is built. Combined with the related hardware structure, the intelligent correction system of students’ English pronunciation error based on speech recognition technology is successfully applied, and the comparative experiment is designed and the practical application value of the system is highlighted.

Intelligent Monitoring System for Prison Perimeter Based on Cloud Intelligence Technology

In order to realize the security of prison perimeter, a prison perimeter intelligent monitoring system based on cloud intelligence technology is designed. Based on the multisupply chain collaboration framework, the prison network interface submodule and sniffer monitoring structure are connected on demand to complete the hardware execution environment of the prison perimeter intelligent monitoring system. Analyze the specific service mode of the cloud smart technology host, set the necessary cloud smart technology service monitoring protocol, judge the processing information parameters, select the prison perimeter remote communication port number, set the prison perimeter network IP address and password program, complete the construction of the software execution environment of the monitoring system, and intelligently monitor and track the abnormal conditions around the prison; combined with the relevant hardware and software structure, the design of prison perimeter intelligent monitoring system based on cloud intelligence technology is realized. The experimental results show that the DPI index of the designed system has an obvious upward trend, and the processing results are close to the original image results to avoid the impact caused by the change of monitoring scene.

Intelligent Correction System of Students’ English Pronunciation Errors Based on Speech Recognition Technology

With the increasingly frequent international exchanges, English has become a common language for communication between countries. Under this research background, in order to correct students’ wrong English pronunciation, an intelligent correction system for students’ English pronunciation errors based on speech recognition technology is designed. In order to provide a relatively stable hardware correction platform for voice data information, the sensor equipment is optimized and combined with the processor and intelligent correction circuit. On this basis, the MLP (Multilayer Perceptron) error correction function is defined, with the help of the known recognition confusion calculation results, the actual input speech error is processed by gain mismatch, and the software execution environment of the system is built. Combined with the related hardware structure, the intelligent correction system of students’ English pronunciation error based on speech recognition technology is successfully applied, and the comparative experiment is designed the practical application value of the system is highlighted.

BIGwas: Single-command quality control and association testing for multi-cohort and biobank-scale GWAS/PheWAS data.

BackgroundGenome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) involving 1 million GWAS samples from dozens of population-based biobanks present a considerable computational challenge and are carried out by large scientific groups under great expenditure of time and personnel. Automating these processes requires highly efficient and scalable methods and software, but so far there is no workflow solution to easily process 1 million GWAS samples.ResultsHere we present BIGwas, a portable, fully automated quality control and association testing pipeline for large-scale binary and quantitative trait GWAS data provided by biobank resources. By using Nextflow workflow and Singularity software container technology, BIGwas performs resource-efficient and reproducible analyses on a local computer or any high-performance compute (HPC) system with just 1 command, with no need to manually install a software execution environment or various software packages. For a single-command GWAS analysis with 974,818 individuals and 92 million genetic markers, BIGwas takes ∼16 days on a small HPC system with only 7 compute nodes to perform a complete GWAS QC and association analysis protocol. Our dynamic parallelization approach enables shorter runtimes for large HPCs.ConclusionsResearchers without extensive bioinformatics knowledge and with few computer resources can use BIGwas to perform multi-cohort GWAS with 1 million GWAS samples and, if desired, use it to build their own (genome-wide) PheWAS resource. BIGwas is freely available for download from http://github.com/ikmb/gwas-qc and http://github.com/ikmb/gwas-assoc.

Multi level Recommendation System of College Online Learning Resources Based on Multi Intelligence Algorithm

In order to accurately grasp the actual search habits of online learning objects and establish a more reasonable multi-level recommendation scheme, a multi-level recommendation system of online learning resources in Colleges and Universities Based on multi intelligent algorithm is designed. With the help of the given topology, the multi-level recommendation behavior in the system is analyzed. Then combined with various types of Resource Recommendation application units, the hardware execution environment of the system is built. On this basis, a multiple intelligent neural network model is established. Combined with multi intelligent recommendation technology, the actual Resource Recommendation behavior related to learning objects is determined, and the software execution environment of the system is built. Combined with the structure of relevant hardware equipment, the design of multi-level recommendation system for online learning resources in colleges and universities based on multiple intelligent algorithms is completed. The experimental results show that the system designed in this paper records a larger number of learning objects, but the recommended waiting time is relatively shorter. The system can accurately grasp the actual search habits of online learning objects and establish a more reasonable multi-level recommendation implementation scheme.

Algorithms for the design of 5G networks with VNF-based Reusable Functional Blocks

We face the problem of designing a 5G network composed of Virtual Network Function (VNF)-based entities, called Reusable Functional Blocks (RFBs). RFBs provide a high level of flexibility and scalability, which are recognized as core functions for the deployment of the forthcoming 5G technology. Moreover, the RFBs can be run on different HardWare (HW) and SoftWare (SW) execution environments located in 5G nodes, in line with the current trend of network softwarization. After overviewing the considered RFB-based 5G network architecture, we formulate the problem of minimizing the total costs of a 5G network composed of RFBs and physical 5G nodes. Since the presented problem is NP-Hard, we derive two algorithms, called SFDA and 5G-PCDA, to tackle it. We then consider a set of scenarios located in the city of San Francisco, where the positions of the users and the set of candidate sites to host 5G nodes have been derived from the WeFi app. Our results clearly show the trade-offs that emerge between (i) the total costs incurred by the installation of the 5G equipment, (ii) the percentage of users that are served, and (iii) the minimum downlink traffic provided to the users.

Lifetime Extension of Software Execution Subject to Aging

Software aging is a phenomenon of progressive degradation of software execution environment caused by software faults. In this paper, we propose software life-extension as an operational countermeasure against software aging and present the mathematical foundations of software life-extension by means of stochastic modeling. A semi-Markov process is used to capture the behavior of a system with software life-extension and to analyze the system's availability and completion times of jobs running on it. The semi-Markov process can correctly model the time-based life-extension and allows us to derive the optimal trigger for starting life-extension in terms of system availability and mean job completion time. We also present an effective combination of software life-extension and software rejuvenation that can maximize the system availability compared with a system using either rejuvenation or software life-extension.

AlgoRun: a Docker-based packaging system for platform-agnostic implemented algorithms.

There is a growing need in bioinformatics for easy-to-use software implementations of algorithms that are usable across platforms. At the same time, reproducibility of computational results is critical and often a challenge due to source code changes over time and dependencies. The approach introduced in this paper addresses both of these needs with AlgoRun, a dedicated packaging system for implemented algorithms, using Docker technology. Implemented algorithms, packaged with AlgoRun, can be executed through a user-friendly interface directly from a web browser or via a standardized RESTful web API to allow easy integration into more complex workflows. The packaged algorithm includes the entire software execution environment, thereby eliminating the common problem of software dependencies and the irreproducibility of computations over time. AlgoRun-packaged algorithms can be published on http://algorun.org, a centralized searchable directory to find existing AlgoRun-packaged algorithms. AlgoRun is available at http://algorun.org and the source code under GPL license is available at https://github.com/algorun laubenbacher@uchc.edu Supplementary data are available at Bioinformatics online.

A Dynamic Control Mechanism of Interrupt Stack Overflow on Real‐Time Embedded Monitor (REMON)

SUMMARYFor embedded systems, it is important to detect changes in the real world and continuing processing properly. The changes are represented by external interrupts, and proper processes are implemented by nested interrupt service routines (hereafter ISRs). Here, a necessity of mutual exclusion arises. In a software execution environment without any real time OS (hereafter RTOS), a traditional mutual exclusion approach was to disable/enable interrupts in a CPU‐specific manner. However, this method typically degrades the real time performance because it defers execution of the mutual‐exclusion‐free part of the system. Considering this situation, we have been studying a real‐time embedded monitor (REMON) which provides a novel mutual exclusion method that can maintain real‐time performance without RTOS. For in‐service embedded systems, one major runtime fault is ISR stack overflow (SOF). It is extremely difficult to test all conditions where ISRs are called from various external conditions. Note that the ISR stack holds not only data but also program instruction addresses, and consequently a SOF may cause a fatal system error. In summary, ISR SOF is a significant issue, but it has not previously been addressed by REMON. This paper proposes two safety extension methods for embedded systems using REMON. The first method detects ISR overflow and safely stops the system before triggering a system‐down or a malfunction. The second method reallocates the ISR stack and resumes system execution automatically.

The Software Reliability Growth Models for Software Life-Cycle Based on NHPP

This paper considers the differences in the software execution environments in the testing phase and the operational phase to determine the optimal release time and warranty period of software systems. We formulate equations for the total expected software cost until the end of the software life cycle based on the NHPP. In addition, we derive the optimal release time that minimizes the total expected software cost for an imperfect debugging software reliability model. Finally, we analyze the sensitivity of the optimal testing and maintenance design related to variation of the cost model parameters based on the fault data observed in the actual testing process, and discuss the quantitative properties of the proposed model.

CARE Resource Broker: A framework for scheduling and supporting virtual resource management

Scheduling application in grid is a complex task that often fails due to nonavailability of resources and the required execution environment in the resources. The CARE Resource Broker (CRB) proposed in this paper addresses such scheduling scenarios using the concepts of virtualization. The virtualization technology offers effective resource management mechanisms such as isolated, secure job scheduling, and utilization of computing resources to the possible extent. However, lack of protocols and services to support virtualization technology in high level grid architecture does not allow management of virtual machines and virtual clusters in grid environment. CRB proposes and implements necessary protocols and services to support creation and management of virtual resources in the physical hosts. Unlike the conventional grid schedulers, CRB addresses several cases of failure of application scheduling such as nonavailability of enough computing nodes in a cluster, and, nonavailability of software execution environment in any of the grid resources. It deploys the required number of virtual machines in potential computing resources to meet the application requirements, and creates virtual clusters dynamically; configure with the required software execution environment for facilitating application execution. Thus, CRB improves the overall throughput by scheduling more applications than conventional grid schedulers and increases the utilization of underutilized computing resources in grid.

Opinion

The Nature of the Problem: Since the inception of personal computing in the early 80’s, the evolution of computing resources has created a myriad of software applications, programming languages, operating systems, platforms, and execution environment sensitive applications. Many of the developed hardware and software system components have come and gone before they had any longevity allowing return on investments in the technology by the market place. In some instances, the changeover in technology has met or exceeded Moore’s Law in the swiftness with which the fleeting technology has appeared only to be replaced. This has created a tremendous obsolescence in software, hardware, and people’s skills. The effect on software development has been an endless requirement for additional training and retraining of personnel on the use of new tools and software environments (as shown in Figure1). The long term effect is a dearth in efficiency of people’s time, and capital investments and the ability to use time, people, and investments to solve new and interesting problems left unchallenged.

Schedulable garbage collection in CLI virtual execution system

Virtual software execution environment, known as Virtual Machine (VM), has been gaining popularity through Java Virtual Machine (JVM) and Common Language Infrastructure (CLI). Given their advantage...

가상 소프트웨어 실행 환경을 제공하는 주문형 소프트웨어 스트리밍 시스템 설계 및 성능평가

소프트웨어 스트리밍은 프로그램 설치 및 실행을 위하여 서버로부터 프로그램 전송이 진행중인 동안에도 컴퓨터 상에서 미설치 소프트웨어의 실행이 즉각적으로 이루어지도록 하는 기능이다. 본 논문에서는 네트워크를 통하여 컴포넌트들의 자동 설치 기능들을 제공하며 프로그램 및 데이터 파일을 스트리밍하고 실행해주는 Software On-Demand(SOD)스트리밍 시스템을 제안한다. 제안된 시스템의 효용성을 입증하기 위하여 리녹스 상에서 즉각적인 소프트웨어 실행 환경과 함께 사용자가 소프트웨어 다운로드와 인스톨 작업에서 완전하게 벗어날 수 있도록 하는 SOD 시스템을 설계 및 구현하였다. 구현된 SOD 시스템은 복잡하고 실패하기 쉬운 설치 작업으로부터 사용자의 수고를 경감시키며 사용자가 UI 윈도우 또는 웹 브라우저를 통하여 look-and-click 의 대화식 조작에 의해 여러 제품들을 쉽게 사용할 수 있도록 해주기 때문에 소프트웨어 개발자는 SOD 시스템에 기반한 가상 실행환경을 통하여 소프트웨어 제품을 광고하고, 전파할 새롭고 강력한 수단을 지원받게 된다. 또한 본 논문에서는 리녹스 상에서 두 가지 SOD 스트리밍 실험 환경을 구축한 후 성능평가 실험 결과에 대한 분석을 통해 향후에 SOD 시스템에 적용할수 있는 두 가지의 성능 개선 방법 AIA(Application Initation Accelerator), SPP(Statistical Predictor Prefetching)를 제안한다. Software streaming allows the execution of stream-enabled software on desktop or portable computing devices like PC, PDA, laptop, cellular phone, etc., even while the transmission/streaming from the server may still be in progress. In this paper, we present an efficient streaming system called Software On-Demand(SOD) streaming system to transmit stream-enabled applications in addition to automatic installation of program registry, environment variables, configuration files, and related components. In particular, we design and implement a SOD system in Linux to provide the user with the instant look-and-click software execution environment such that software download and installation are internally proceeded in a completely user-transparent way. Therefore, the SOD system relieves the user from the tricky, failure-prone installation business. In addition, the software developer now obtains a new, powerful means to advertise and propagate their software products since the user can use software packages via user-friendly UI window or web browser by look-and-click interactive operation. In the paper, we also make a couple of SOD streaming experiments using a spectrum of popular softwares. Based on the analysis of the experiment results, we also propose two performance improvement schemes.

Optimal testing/maintenance design in a software development project

In this paper, we consider the problem of determining the optimal testing period and the optimal maintenance limit for software. Software debugging in the testing phase and the operation stage is described by a software reliability model based on a nonhomogeneous Poisson process and models the difference between the software execution environments in both stages as the operational profile. Furthermore, the time when continued maintenance by the project team ends is defined as the planned maintenance limit. Each of the optimal testing period (release time) and the optimal planned maintenance limit that minimize the total expected software cost is determined. Finally, we present an algorithm that determines the joint optimal policies for both, and perform the optimal testing/maintenance design through numerical examples. © 2006 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 89(8): 1–9, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20224