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An investigation of distributed computing for combinatorial testing

SOFTWARE TESTING VERIFICATION & RELIABILITY 2023年第4期33卷 e1842-e1842页

作者： La Chance, Edmond Halle, Sylvain Univ Quebec Chicoutimi Lab Informat Formelle Chicoutimi PQ Canada

Combinatorial test generation, also called t$$ t $$-way testing, is the process of generating sets of input parameters for a system under test, by considering interactions between values of multiple parameters. In order to decrease total testing time, there is an interest in techniques that generate smaller test suites. In our previous work, we used graph techniques to produce high-quality test suites. However, these techniques require a lot of computing power and memory, which is why this paper investigates distributed computing for t$$ t $$-way testing. We first introduce our distributed graph colouring method, with new algorithms for building the graph and for colouring it. Second, we present our distributed hypergraph vertex covering method and a new heuristic. Third, we show how to build a distributed IPOG algorithm by leveraging either graph colouring or hypergraph vertex covering as vertical growth algorithms. Finally, we test these new methods on a computer cluster and compare them to existing t$$ t $$-way testing tools.

关键词： combinatorial testing distributed computing phi-way testing test case generation

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mkite: A distributed computing platform for high-throughput materials simulations

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COMPUTATIONAL MATERIALS SCIENCE 2023年第1期230卷

作者： Schwalbe-Koda, Daniel Lawrence Livermore Natl Lab 7000 East Ave Livermore CA 94550 USA

Advances in high-throughput simulation (HTS) software enabled computational databases and big data to become common resources in materials science. However, while computational power is increasingly larger, software packages orchestrating complex workflows in heterogeneous environments are scarce. This paper introduces mkite, a Python package for performing HTS in distributed computing environments. The mkite toolkit is built with the client-server pattern, decoupling production databases from client runners. When used in combination with message brokers, mkite enables any available client to perform calculations without prior hardware specification on the server side. Furthermore, the software enables the creation of complex workflows with multiple inputs and branches, facilitating the exploration of combinatorial chemical spaces. Software design principles are discussed in detail, highlighting the usefulness of decoupling simulations and data management tasks to diversify simulation environments. To exemplify how mkite handles simulation workflows of combinatorial systems, case studies on zeolite synthesis and surface catalyst discovery are provided. Finally, key differences with other atomistic simulation workflows are outlined. The mkite suite can enable HTS in distributed computing environments, simplifying workflows with heterogeneous hardware and software, and helping deployment of calculations at scale.

关键词： High-throughput simulations distributed computing Database Workflow management Data sharing

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Q-Learning-Based Task Allocation for High-Speed distributed computing in Heterogeneous Clusters

Q-Learning-Based Task Allocation for High-Speed Distributed ...

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International Conference on Information Networking

作者： Seung Geon Yang Girim Kwon Seung-Chan Lim School of ICT Robotics and Mechanical Engineering Hankyong National University Anseong South Korea School of Electrical and Computer Engineering University of Seoul Seoul South Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

This paper proposes a Q-learning-based task allocation approach for wireless coded distributed computing systems with heterogeneous worker nodes. Task allocation in such systems is challenging due to the heterogeneity in computation and communication capabilities, leading to non-identically and independently distributed processing times across nodes. By modeling the task allocation problem as a Markov decision process and applying Q-learning, the master node learns to allocate tasks effectively, adapting to node heterogeneity and minimizing the average processing time. This approach highlights the potential of reinforcement learning to optimize distributed computing in heterogeneous environments.

关键词： Wireless communication distributed processing Adaptation models Q-learning Markov decision processes Computational modeling Delays Resource management distributed computing Optimization

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On the Performance of Specialized distributed computing Systems Using Multiple Parallel Task Queues

On the Performance of Specialized Distributed Computing Syst...

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Systems of Signals Generating and Processing in the Field of on Board Communications

作者： I. M. Gostev S. V. Bolovtsov Russian Scientific and Technical Society of Radio Engineering Electronics and Communications named after A.S. Popov Moscow Russia Russian Presidential Academy of National Economy and Public Administration (RANEPA) Moscow Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

Three variants of models of specialized distributed computing systems containing one, two and four parallel task queues are considered. Such systems are characterized by continuous non-stationary internal task flows. In these systems, the input tasks are parallelized into an a priori unknown number of data-independent subtasks. And when a certain subtask containing a solution is completed, all other branches of this task are deleted. The aim of the work was to study the behavior of such systems when they are organized with several parallel queues. The issues of changing the efficiency and performance of such systems with an increase in the number of queues are considered. Studies have shown that the use of several queues makes it possible to increase productivity without increasing computing resources.

关键词： Productivity Computational modeling System performance Planning distributed computing

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DISCOVER: A Cyberinfrastructure Testbed for distributed computing and Networking in Rural and Remote Environments

DISCOVER: A Cyberinfrastructure Testbed for Distributed Comp...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Alireza Ebrahimi Connor Gouin Sayed Pedram Haeri Boroujeni Juan Carlos Tique Rangel Tolunay Seyfi Truong X. Nghiem Abolfazl Razi Morgan Vigil-Hayes Paul L. Heinrich Fatemeh Afghah Department of Electrical and Computer Engineering Clemson University Clemson USA School of Computing Clemson University Clemson USA School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff Arizona USA Department of Electrical and Computer Engineering University of Central Florida Florida USA

ISBN: (数字)9798331538323

ISBN: (纸本)9798331538330

The distributed Sensing and computing Over Sparse Environments (DISCOVER) testbed is a pioneering cyberinfrastructure initiative designed to advance research in distributed computing and networking tailored to rural, remote, and sparsely populated regions. Supported by the National Science Foundation (NSF), DISCOVER integrates a network of configurable Internet-of-Things (IoT) nodes—including stationary sensors, drones, and terrestrial rovers—across three key sites: Northern Arizona University (NAU), Clemson University, and Navajo Technical University (NTU). This collaboration offers a unique platform to explore innovative algorithms and methodologies addressing the technical challenges of under-served areas, with an emphasis on environmental and civil disaster response. The testbed enables a wide range of experiments, such as regional-scale data collection, heterogeneous networked services, distributed artificial intelligence (AI), distributed multi-robot control, and communication-aware software for resource-constrained networks. An online portal enhances accessibility, allowing researchers to request resources, upload experimental code, and retrieve data, with pre-integrated deep learning models for applications like human posture detection, object detection, and wildfire detection. This paper outlines the testbed’s architecture, operational sites, supported experiment types, ongoing research efforts, and its educational and outreach impacts, highlighting its role in fostering scientific innovation.

关键词： Wireless communication Wireless sensor networks Technological innovation Wildfires Software algorithms US Government Sensors distributed computing Artificial intelligence Drones

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Research on Big Data distributed computing Model and Algorithm Optimization for Complex Network Analysis

Research on Big Data Distributed Computing Model and Algorit...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), International Conference on

作者： Aiting Sun Liaoning Vocational College of light industry Liaoning China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

With the advent of the era of big data, the traditional stand-alone computing model can no longer meet the needs of complex network analysis, and the distributed computing model provides a new solution. This paper first introduces the importance of complex network analysis and the challenges brought by big data, then discusses the application of distributed computing model in complex network analysis in detail, and focuses on the distributed computing algorithm based on MapReduce. Then, this paper puts forward an optimization strategy of distributed community detection algorithm based on modularity optimization. This strategy performs community detection tasks in parallel through MapReduce framework, and optimizes community division through iterative updating to maximize modularity. The experimental results show that the optimized algorithm has a significant improvement in computing time, accuracy and resource consumption. The distributed computing model based on MapReduce framework and its optimization strategy have obvious advantages in dealing with large-scale complex networks. It can not only improve the calculation efficiency and accuracy, but also reduce the resource consumption, which provides an efficient and feasible solution for complex network analysis.

关键词： Analytical models Accuracy Computational modeling Memory management Complex networks Big Data Data models distributed computing Detection algorithms Optimization

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Guest Editorial: Special Section on New Trends in Parallel and distributed computing for Human Sensible Applications

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computing 2021年第4期9卷 1640-1641页

作者： D'Agostino, Daniele Leporati, Francesco Torquati, Massimo Xue, Jingling CNR Inst Elect Informat Engn & Telecommun Paris France Univ Pavia Elect Biomed & Comp Dept Mechatron Ind Informat & Embedded Syst & Digital Pavia Italy Univ Pisa Comp Sci Dept Pisa Italy UNSW Sydney Sch Comp Sci & Engn Sydney NSW Australia

The papers in this special section focus on collecting high-quality scientific contributions from the research community working in the fields of parallel and distributed computing, data analytics algorithms and big data frameworks, application specific processing. Specifically, the main focus is on emerging new computing trends that affect the concrete human life, the so-called "Human Sensible Applications". Problems in parallel computing related to implement precision medicine and novel therapeutical targets, real-time architectures for biomedical IoT, computational biology and chemical compounds simulations, realistic modelling of human body organs, bioimaging processing, but even emerging computing systems for Human Sustainability, including weather and climate changes monitoring/prediction, resources management, safety, disaster prediction and prevention, belong to this scenario.

关键词： Special issues and sections Parallel processing distributed computing Computer architecture Human factors Real-time systems Computational modeling Predictive models

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Enhancing Security in VANETs using distributed computing: A Review

Enhancing Security in VANETs using Distributed Computing: A ...

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Vamsi Krishna Pavuluri Abinash Borah Anirudh Paranjothi Mohammad S. Khan Department of Computer Science Oklahoma State University Stillwater Oklahoma USA Department of Computing East Tennessee State University Johnson City Tennessee USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Vehicular Ad-Hoc Networks (VANETs) are a cornerstone of intelligent transportation systems, enabling vehicles to communicate with each other and roadside infrastructure to enhance traffic safety and efficiency. Securing VANETs remains a critical challenge due to the dynamic and decentralized nature of vehicular communications. This paper reviews and analyzes various distributed technologies-blockchain, fog computing, and federated learning-deployed for enhancing VANET security. Each technology's efficacy in addressing some of the security issues is assessed, revealing its strengths in improving data integrity and trustworthiness. However, challenges such as scalability limitations, high computational overheads, and susceptibility to single point of failure persist. Future research directions are proposed to bolster fault tolerance, load balancing, and scalability, aiming to advance the resilience and efficiency of distributed VANET security solutions.

关键词： Fault tolerance Reviews Scalability Fault tolerant systems Vehicular ad hoc networks Transportation Load management Security distributed computing Vehicle dynamics

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Integrating Artificial Intelligence with Cloud Platforms to Optimize Performance, Scalability, and Reliability in distributed computing Systems

Integrating Artificial Intelligence with Cloud Platforms to ...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： B.L. Vennala Devanolla Suresh P. Samiya M. Suresh B. Vamsi Department of Computer Science & Technology Madanapalle Institute of Technology & Science Madanapalle Department of Computer Science & Technology Madanapalle Institute Of Technology & Science Andhra Pradesh Department of Computer Science & Technology Madanapalle Institute of Technology & Science

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

This non-stop advancement of distributed computing systems due to the necessity of achieving big data processing for real-time data processing and high work complexity requires better solutions for enhancing high-performance, scalability, and reliability. This study focuses on the interaction between AI and Cloud computing, to improve the performance of distributed Systems. It is the hope of this paper that with the use of AI-adaptive algorithms and machine learning, resource distribution can be optimized, system constraints can be forecasted with + precision, and summaries can be made fault resilient across various computational platforms. The focus techniques are reinforcement learning for dynamic resource management, genetic algorithms for load distribution, and federated learning for enhanced data confidentiality and distributed model training. Further, workload prediction is done using time-series based or machine-learning based predictive Analytics, and deep learning-based anomaly detection for health monitoring and autoencoder for fault diagnosis. Container orchestration and micro services architecture has been implemented in the proposed framework for achieving flexibility and scalability in multi-cloud and hybrid cloud systems. The experimental results show the effectiveness of the proposed implementation to enhance the system performance, scalability and reliability and thus signaling a new dawn for AI based solutions in distributed computing systems.

关键词： Cloud computing Federated learning Scalability System performance Heuristic algorithms Reinforcement learning Real-time systems Resource management distributed computing Genetic algorithms

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Algebraic Geometric Rook Codes for Coded distributed computing

Algebraic Geometric Rook Codes for Coded Distributed Computi...

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IEEE (first ITW conference of the year) Information Theory Workshop (ITW)

作者： Gretchen L. Matthews Pedro Soto Department of Mathematics Virginia Tech Blacksburg Virginia USA

ISBN: (数字)9798350348934

ISBN: (纸本)9798350348941

We extend coded distributed computing over finite fields to allow the number of workers to be larger than the field size. We give codes that work for fully general matrix multiplication and show that in this case we serendipitously have that all functions can be computed in a distributed fault-tolerant fashion over finite fields. This generalizes previous results on the topic. We prove that the associated codes achieve a recovery threshold similar to the ones for characteristic zero fields but now with a factor that is proportional to the genus of the underlying function field. In particular, we have that the recovery threshold of these codes is proportional to the classical complexity of matrix multiplication by a factor of at most the genus.

关键词： Fault tolerance Codes Conferences Fault tolerant systems Complexity theory Galois fields distributed computing

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