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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 6279-6290页

作者： Semper, Sebastian Naviliat, Joeel Gedschold, Jonas Doebereiner, Michael Schieler, Steffen Thomae, Reiner S. TU Ilmenau EMS Res Grp D-98693 Ilmenau Germany Fraunhofer Inst Integrated Circuits IIS D-98693 Erlangen Germany

The recent advances in Integrated Sensing and Communications (ICAS) essentially transform mobile radio networks into a diverse, dynamic and heterogeneous sensing network. For the application of localization, the acquired sensing data needs to be processed to estimates of the state vectors of the targets in a timely manner. This paper aims at providing a roadmap for the development of a suitable computing system for ICAS. We propose to embed the signal processing into the concept of edge computing. It provides the necessary theoretical computing framework, since it alleviates the need for communication with a remote cloud. To obtain localization information in such a distributed, asynchronous and heterogeneous scenario, we study how existing maximum likelihood estimation techniques can be transformed into algorithms that can be orchestrated close to the edge. The advantage of these approaches is that they have well studied statistical properties and efficient algorithmic implementations exist. We propose to study to derive a graph that encodes these algorithms' processing by relating individual and isolated computations in terms of the input/output-behavior of so-called compute nodes. This compute graph structure can then be flexibly distributed across multiple devices and even whole processing/sensing units. Moreover, modern computing architectures leverage such graph structures to optimize the efficient use of computing hardware. Additionally, once this graph is constructed we have laid the groundwork for the possibility to exchange certain compute steps by deep learning architectures. For instance, this allows to sidestep some costly iterative part of traditional maximum likelihood estimators, which further contributes to the low-latency of the localization task. Moreover, deep learning methods bear the promise of being more robust to model mismatches in contrast to the conventional model based approaches. As a consequence, we can then study the relation between t

关键词： Sensors Computational modeling Signal processing algorithms Location awareness MIMO radar Accuracy Radar tracking ICAS distributed computing model-based parameter estimation

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distributed computing model for channel bandwidth allocation and optimization using machine learning techniques

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OPTICAL AND QUANTUM ELECTRONICS 2023年第13期55卷 1-25页

作者： Shan, Pingping Zhang, Zheng Nanyang Inst Technol Sch Comp & Software Nanyang 473004 Peoples R China

A distributed computing model for channel bandwidth allocation and optimization can involve multiple components working together to efficiently allocate and optimize the available bandwidth in a distributed system. The efficient allocation of channel bandwidth in the distributed computing model is crucial for optimizing resource utilization and improving system performance. This paper, proposed the Imperialist Competitive Spline Interpolation (ICSI) scheme, which combines computational intelligence and deep learning techniques to address the challenge of channel bandwidth allocation. The ICSI scheme optimizes resource allocation by considering user requirements and resource availability, utilizing polynomial equations and spline interpolation. The Imperialist Competitive Optimization model evaluates and optimizes the available resources in the distributed environment. With the optimized resources spline interpolation is implemented for the computation of the available resources. Extensive simulations and performance analysis demonstrate the effectiveness of the ICSI scheme in terms of resource utilization, throughput, latency, fairness index, and energy efficiency. The ICSI model achieves the minimal waiting time of 3 ms and minimal latency of 6.4 m. Comparative analysis of the Round Robin scheme further confirms the superiority of the ICSI scheme in terms of task scheduling efficiency. The findings of this paper contribute to the advancement of distributed computing models for channel bandwidth allocation, offering a promising solution for optimizing resource allocation and improving system performance in modern computing environments.

关键词： distributed computing Imperialist competitive optimization Spline interpolation Channel resource Resource allocation

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distributed computing Systems and Approaches for Polar Science Research: An Interdisciplinary Systematic Review 24

Distributed Computing Systems and Approaches for Polar Scien...

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24th IEEE/ACM International Symposium on Cluster, Cloud, and Internet computing (CCGrid)

作者： Kathiravelu, Pradeeban Birmingham, Ryan Univ Alaska Anchorage Anchorage AK 99508 USA Emory Univ Atlanta GA 30322 USA

ISBN: (纸本)9798350377521;9798350377514

Polar science is an umbrella term for several research disciplines practiced in Earth's polar regions and other planets. Polar science research includes studies performed on land (such as geology and archeology on the circumpolar tundra), air (atmospheric research), sky (planetary observations due to dark, clear, and clean skies during the polar nights), and ocean (marine studies in the Arctic and the Southern Oceans). Many polar science research works look into overlapping complementary studies. distributed computing is a discipline of computer science that involves computations across multiple distinct physically separated computing resources, such as computer clusters, clouds, and the edge. distributed computing has enabled efficient, scalable, and elastic execution of complex computational problems on utility hardware without expecting access to expensive infrastructure or supercomputers. In this paper, we screen 770 for the interdisciplinary research of distributed computing used in or developed for polar science. After systematically removing the irrelevant studies, we assess the full text of 72 papers to understand the distributed systems research landscape for polar science. We then specifically study 22 polar science research works that develop or heavily utilize distributed computing frameworks or principles in detail. Our study finds distributed execution frameworks instrumental for polar science due to the complex and real-time computational needs, coupled with the remote location requiring efficient network bandwidth availability and usage to access remote resources.

关键词： Computer Networks distributed computing distributed Systems Environmental Systems Polar Science

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distributed computing Quantum Unitary Evolution

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LOBACHEVSKII JOURNAL OF MATHEMATICS 2024年第7期45卷 3121-3129页

作者： Miao, Hui-hui Ozhigov, Yu. I. Lomonosov Moscow State Univ Fac Computat Math & Cybernet Moscow 119991 Russia Russian Acad Sci Valiev Inst Phys & Technol Moscow 117218 Russia

A distributed computing approach to solve the curse of dimensionality, caused by the complex quantum system modeling, is discussed. With the help of Cannon's algorithm, the distributed computing transformation of numerical method for simulating quantum unitary evolution is achieved. Based on the Tavis-Cummings model, a large number of atoms are added into the optical cavity to obtain a high-dimensional quantum closed system, implemented on the supercomputer platform. The comparison of time cost and speedup of different distributed computing strategies is discussed.

关键词： curse of dimensionality distributed computing quantum electrodynamics artificial atom

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Dynamic access task scheduling of LEO constellation based on space-based distributed computing

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Journal of Systems Engineering and Electronics 2024年第4期35卷 842-854页

作者： LIU Wei JIN Yifeng ZHANG Lei GAO Zihe TAO Ying Institute of Telecommunication and Navigation Satellites China Academy of Space TechnologyBeijing 100094China Innovation Center of Satellite Communication System China National Space AdministrationBeijing 100094China

A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this *** allocation is a combinatorial optimization process under a series of complex constraints,which is important for enhancing the matching between resources and requirements.A complex algorithm is not available because that the LEO on-board resources is *** proposed genetic algorithm(GA)based on two-dimen-sional individual model and uncorrelated single paternal inheri-tance method is designed to support distributed computation to enhance the feasibility of on-board application.A distributed system composed of eight embedded devices is built to verify the algorithm.A typical scenario is built in the system to evalu-ate the resource allocation process,algorithm mathematical model,trigger strategy,and distributed computation *** to the simulation and measurement results,the proposed algorithm can provide an allocation result for more than 1500 tasks in 14 s and the success rate is more than 91%in a typical *** response time is decreased by 40%com-pared with the conditional GA.

关键词： beam resource allocation distributed computing low Earth obbit(LEO)constellation spacecraft access task scheduling

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DataLab as a Service: distributed computing Framework for Multi-Interactive Analysis Environments

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IEEE ACCESS 2025年 13卷 22566-22577页

作者： Hoz, Aida Palacio Heredia Canales, Andres Cimadevilla Alvarez, Ezequiel Obregon Ruiz, Marta Lopez Garcia, Alvaro Inst Fis Cantabria IFCA CSIC UC Santander 39005 Spain

During the current data era, data analysis across multiple disciplines has become a critical task for researchers to obtain meaningful insights and solve complex problems that are immeasurable using traditional technologies. Big Data has led to the development of state-of-the-art technologies that have revolutionized the process of experimentation. These innovations span from automating the setup of the infrastructure required for data analysis to providing user-friendly interfaces that simplify coding and result visualization. However, managing and scaling these resources for large-scale data processing remains a challenge. In this work, we introduce a novel framework called Datalab as a Service which integrates cutting-edge and open-source technologies to offer an online platform designed for both resource providers and researchers. The platform enables users to easily and automatically deploy interactive environments tailored for data analysis, thereby streamlining the process of managing computational resources. Through DLaaS, users gain access to cloud-based infrastructures and distributed computing resources, which are essential for performing compute-intensive tasks on massive datasets. The framework ensures scalability, resource management and optimization, and high availability, all within an accessible and user-friendly platform. Furthermore, this paper presents several use cases where researchers have successfully utilized DLaaS resources, demonstrating its practical applications in real-world scenarios.

关键词： Cloud computing Microservice architectures Ecosystems Computer architecture Servers Scalability Libraries Complexity theory Codes Sparks distributed computing Kubernetes interactive environments cloud computing Jupyter notebooks

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A Novel Multi-hop Query Answering System Based on a Large Knowledge Graph and distributed computing

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2025年第3期24卷 1-28页

作者： Hong, Trung Phan Do, Phuc Hoa Sen Univ Ho Chi Minh City Vietnam Univ Informat Technol UIT VNU HCM Informat Technol Ho Chi Minh City Vietnam

An automated query answering system (QAS) is a very useful application in organizations. Therefore, there is a lot of research to build, develop and improve it. In this article, we present a method to build a multi-hop query answering system (MQAS) based on a large knowledge graph (KG), Bidirectional Encoder Represenprovides knowledge for MQAS. BERT is used to transform questions into vectors. KD-Tree helps find the right answers quickly. On the other hand, we also propose a solution for distributed indexing of large vector spaces by building a distributed indexing structure called distributed KD-Tree (DKD-Tree). In addition, we also present experiments and evaluation results to demonstrate the effectiveness of our solution.

关键词： Phrases Multi-hop query answering large knowledge graph BERT KD-Tree distributed computing

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Nicaea: A Byzantine Fault Tolerant Consensus Under Unpredictable Message Delivery Failures for Parallel and distributed computing

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IEEE TRANSACTIONS ON COMPUTERS 2025年第3期74卷 915-928页

作者： Jing, Guanlin Zou, Yifei Xu, Minghui Zhang, Yanqiang Yu, Dongxiao Shan, Zhiguang Cheng, Xiuzhen Ranjan, Rajiv Shandong Univ Inst Intelligent Comp Sch Comp Sci & Technol Qingdao 266237 Peoples R China State Informat Ctr Dept Informatizat & Ind Dev Beijing 100804 Peoples R China Newcastle Univ Sch Comp Newcastle Upon Tyne NE1 7RU England

Byzantine fault-tolerant (BFT) consensus is a critical problem in parallel and distributed computing systems, particularly with potential adversaries. Most prior work on BFT consensus assumes reliable message delivery and tolerates arbitrary failures of up to n3 nodes out of n total nodes. However, many systems face unpredictable message delivery failures. This paper investigates the impact of unpredictable message delivery failures on the BFT consensus problem. We propose Nicaea, a novel protocol enabling consensus among loyal nodes when the number of Byzantine nodes is below a new threshold, given by: (2-rho)(1-rho)2n-2-1 /(2-rho)(1-rho)2n-2+1n, where rho denotes the message failure rate. Theoretical proofs and experimental results validate Nicaea's Byzantine resilience. Our findings reveal a fundamental trade-off: as message delivery instability increases, a system's tolerance to Byzantine failures decreases. The well-known n3 threshold under reliable message delivery is a special case of our generalized threshold when rho = 0. To the best of our knowledge, this work presents the first quantitative characterization of unpredictable message delivery failures' impact on Byzantine fault tolerance in parallel and distributed computing.

关键词： Protocols Reliability Fault tolerant systems Fault tolerance distributed computing Consensus protocol Computational modeling Electronic mail Consensus algorithm Computers Byzantine fault-tolerance message delivery failure parallel and distributed computing

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Real Relative Encoding Genetic Algorithm for Workflow Scheduling in Heterogeneous distributed computing Systems

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2025年第1期36卷 1-14页

作者： Jiang, Junqiang Sun, Zhifang Lu, Ruiqi Pan, Li Peng, Zebo Hunan Inst Sci & Technol Sch Informat Sci & Engn Yueyang 414006 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Key Lab Embedded & Cyber Phys Syst Hunan Prov Changsha 410082 Peoples R China Linkoping Univ Dept Comp & Informat Sci IDA Embedded Syst Lab ESLAB SE-58183 Linkoping Sweden

This paper introduces a novel Real Relative encoding Genetic Algorithm (R(2)GA) to tackle the workflow scheduling problem in heterogeneous distributed computing systems (HDCS). R(2)GA employs a unique encoding mechanism, using real numbers to represent the relative positions of tasks in the schedulable task set. Decoding is performed by interpreting these real numbers in relation to the directed acyclic graph (DAG) of the workflow. This approach ensures that any sequence of randomly generated real numbers, produced by cross-over and mutation operations, can always be decoded into a valid solution, as the precedence constraints between tasks are explicitly defined by the DAG. The proposed encoding and decoding mechanism simplifies genetic operations and facilitates efficient exploration of the solution space. This inherent flexibility also allows R(2)GA to be easily adapted to various optimization scenarios in workflow scheduling within HDCS. Additionally, R(2)GA overcomes several issues associated with traditional genetic algorithms (GAs) and existing real-number encoding GAs, such as the generation of chromosomes that violate task precedence constraints and the strict limitations on gene value ranges. Experimental results show that R(2)GA consistently delivers superior performance in terms of solution quality and efficiency compared to existing techniques.

关键词： Genetic algorithms Encoding Scheduling Processor scheduling Biological cells Metaheuristics Quality of service Heuristic algorithms Genetic operators distributed computing Candidate task set directed acyclic graph (DAG) genetic algorithm real encoding workflow scheduling

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A Family of Binary Locally Repairable Codes for Coded distributed computing

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第1期72卷 50-62页

作者： Qharabagh, Muhammad Fetrat Ardakani, Masoud Univ Waterloo David R Cheriton Sch Comp Sci Waterloo ON N2L 3G1 Canada Univ Alberta Dept Elect & Comp Engn Edmonton T6G 2R3 AB Canada

One of the main bottlenecks in distributed computing systems is the stragglers' problem. Error correction codes have been proposed to alleviate this problem at the cost of coding complexity for the master node. In this work, we aim to reduce this coding complexity and propose a novel family of binary locally repairable codes (BLRC) to encode the distributed tasks in a linear matrix-vector multiplication problem. In comparison to the widely used maximum distance separable (MDS) codes, our proposed codes (i) eliminate the costly multiplication operations from the encoding and decoding processes, (ii) allow for low-complexity recovery within the local groups. We analyze the complexity of our proposed codes and through simulations show that compared to MDS codes, our codes reduce the overall encoding plus computation plus decoding time by more than 35% in many practical scenarios.

关键词： Codes Encoding Task analysis Decoding Complexity theory distributed computing Delay effects Coded distributed computing stragglers error-correction codes binary codes locally repairable codes minimum distance separable codes computational delay

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