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distributed computing Quantum Unitary Evolution

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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distributed computing in reconfigurable picosatellite networks

Distributed computing in reconfigurable picosatellite networ...

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2nd NASA/ESA Conference on Adaptive Hardware and Systems

作者： Vladimirova, Tanya Wu, Xiaofeng Jallad, Abdul-Halim Bridges, Christopher P. Univ Surrey Dept Elect Engn Surrey Space Ctr Guildford GU2 7XH Surrey England

ISBN: (纸本)9780769528663

This paper presents the results of a research project, which aims to develop enabling technologies for future distributed space architectures based on flexible, reconfigurable, evolvable, and intelligent multi-spacecraft sensing networks. One important goal of the project is to propose a distributed computing platform over wireless inter-satellite links. The paper discusses initial results on the application of distributed computing technologies to future networked constellations of picosatellites.

关键词： distributed computing picosatellite middleware autonomous agents wireless communication

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distributed computing with Heterogeneous Servers

Distributed Computing with Heterogeneous Servers

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IEEE Global Communications Conference (GLOBECOM) on Advanced Technology for 5G Plus

作者： Xu, Jiasheng Fu, Luoyi Wang, Xinbing Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci Shanghai Peoples R China

ISBN: (纸本)9781728182988

distributed computing is known for its high efficiency of processing large amounts of data in parallel, at the expense of communication load between different servers. Coding was introduced to minimize the communication load by exploiting the repetitive computing, thus drawing great attention within the academia. Most existing works assume that all servers are identical in computational capability, which is inconsistent with practical scenarios. In this paper, we investigate a distributed computing system that consists of two types of servers, i.e., fast servers and slow servers. Due to the heterogeneous computational capabilities within the system, the overall computation time will be delayed by the slow servers, which is called the straggling effect. To this end, we develop a novel framework of coding-based distributed computing to alleviate the straggling effect. Specifically, for a given number of fast servers and slow servers with their corresponding computational capabilities, we aim to minimize the overall computation time by assigning different amounts of workloads to different servers. Further, we derive the information-theoretic lower hound of the communication load of the system, which is shown to be within a constant multiplicative gap to the achievable communication load by our scheme.

关键词： distributed computing coded computation heterogeneous servers straggling effect

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distributed computing Building Blocks for Rational Agents 14

Distributed Computing Building Blocks for Rational Agents

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ACM Symposium on Principles of distributed computing (PODC)

作者： Afek, Yehuda Ginzberg, Yehonatan Feibish, Shir Landau Sulamy, Moshe Tel Aviv Univ Blavatnik Sch Comp Sci Tel Aviv Israel

ISBN: (纸本)9781450329446

Following [4] we extend and generalize the game-theoretic model of distributed computing, identifying different utility functions that encompass different potential preferences of players in a distributed system. A good distributed algorithm in the game-theoretic context is one that prohibits the agents (processors with interests) from deviating from the protocol;any deviation would result in the agent losing, i.e., reducing its utility at the end of the algorithm. We distinguish between different utility functions in the context of distributed algorithms, e.g., utilities based on communication preference, solution preference, and output preference. Given these preferences we construct two basic building blocks for game theoretic distributed algorithms, a wake-up building block resilient to any preference and in particular to the communication preference (to which previous wake-up solutions were not resilient), and a knowledge sharing building block that is resilient to any and in particular to solution and output preferences. Using the building blocks we present several new algorithms for consensus, and renaming as well as a modular presentation of the leader election algorithm of [4].

关键词： distributed computing game theory message passing consensus renaming leader election knowledge sharing rational agents

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distributed computing for large-scale graphs: Models, Algorithms, Applications

Distributed computing for large-scale graphs: Models, Algori...

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作者： Alessio Guerrieri University of Trento

学位级别：博士

The last decade has seen an increased attention on large-scale data analysis, caused mainly by the availability of new sources of data and the development of programming model that allowed their analysis. Since many of these sources can be modeled as graphs, many large-scale graph processing frameworks have been developed, from vertex-centric models such as pregel to more complex programming models that allow asynchronous computation, can tackle dynamism in the data and permit the usage of different amount of resources. This thesis presents theoretical and practical results in the area of distributed large- scale graph analysis by giving an overview of the entire pipeline. Data must first be pre-processed to obtain a graph, which is then partitioned into subgraphs of similar size. To analyze this graph the user must choose a system and a programming model that matches her available resources, the type of data and the class of algorithm to execute. Aside from an overview of all these different steps, this research presents three novel approaches to those steps. The first main contribution is dfep, a novel distributed parti- tioning algorithm that divides the edge set into similar sized partition. dfep can obtain partitions with good quality in only a few iterations. The output of dfep can then be used by etsch, a graph processing framework that uses partitions of edges as the focus of its programming model. etsch's programming model is shown to be flexible and can easily reuse sequential classical graph algorithms as part of its workflow. Implementations of etsch in hadoop, spark and akka allow for a comparison of those systems and the discussion of their advantages and disadvantages. The implementation of etsch in akka is by far the fastest and is able to process billion-edges graphs faster that competitors such as gps, blogel and giraph++, while using only a few computing nodes. A final contribution is an application study of graph-centric approaches to word sense

关键词： graph analysis big-data distributed computing

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Energy-Efficient distributed Edge computing to Assist Dense Internet of Things

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FUTURE INTERNET 2025年第1期17卷 37-37页

作者： Algarni, Sumaiah Abd El-Samie, Fathi E. Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Univ Liverpool Dept Elect Engn & Elect Liverpool L69 7ZX England Menoufia Univ Fac Elect Engn Dept Elect & Elect Commun Menoufia 32952 Egypt

The Internet of Things (IoT) represents a rapidly growing field, where billions of intelligent devices are interconnected through the Internet, enabling the seamless sharing of data and resources. These smart devices are typically employed to sense various environmental characteristics, including temperature, motion of objects, and occupancy, and transfer their values to the nearest access points for further analysis. The exponential growth in sensor availability and deployment, powered by recent advances in sensor fabrication, has greatly increased the complexity of IoT network architecture. As the market for these sensors grows, so does the problem of ensuring that IoT networks meet high requirements for network availability, dependability, flexibility, and scalability. Unlike traditional networks, IoT systems must be able to handle massive amounts of data generated by various and frequently-used resource-constrained devices, while ensuring efficient and dependable communication. This puts high constraints on the design of IoT, mainly in terms of the required network availability, reliability, flexibility, and scalability. To this end, this work considers deploying a recent technology of distributed edge computing to enable IoT applications over dense networks with the announced requirements. The proposed network depends on distributed edge computing at two levels: multiple access edge computing and fog computing. The proposed structure increases network scalability, availability, reliability, and scalability. The network model and the energy model of the distributed nodes are introduced. An energy-offloading method is considered to manage IoT data over the network energy, efficiently. The developed network was evaluated using a developed IoT testbed. Heterogeneous evaluation scenarios and metrics were considered. The proposed model achieved a higher energy efficiency by 19%, resource utilization by 54%, latency efficiency by 86%, and reduced network congestion by

关键词： distributed computing IoT fog computing resource utilization multiple access edge computing reliability scalability dense networks

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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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distributed computing and Data Processing Technology under the Power Data Middleware Architecture 3

Distributed Computing and Data Processing Technology under t...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Zou, Yuelin Shen, Jia Yin, Rui Guo, Jiangtao Guo, Yi State Grid Xinjiang Electric Power Co. Ltd. Information and Communication Company Urumqi830002 China State Grid Xinjiang Electric Power Co. Ltd. Urumqi830002 China State Grid Info-Telecom Great Power Science and Technology Co. Ltd Fuzhou350001 China

ISBN: (纸本)9798350341546

With the establishment and development of integrated monitoring platforms and communication information platforms for intelligent substations, the data volume of the power system is showing explosive growth. However, existing data centers have poor processing capabilities for massive amounts of data, making it difficult to meet the multiple service quality requirements of users. How to effectively manage massive and complex data, fully utilize the value of data, improve service quality, reduce operational costs, and achieve rapid response to users is an urgent problem to be solved. Therefore, based on the actual needs of power enterprises, this article proposed a cloud computing power data middleware architecture based on distributed technology, which effectively managed, cleaned, converted, and evaluated power data sources through cloud computing platforms. This article mainly analyzed the shortcomings of traditional power data processing platforms and the feasibility of cloud computing based power data platforms. Finally, through load balancing bias comparison and pressure testing experiments under different task numbers, it was proven that cloud computing systems outperformed traditional power data processing systems in terms of utility value, completion time, and load balancing. Moreover, in the pressure testing experiment, under the condition of 10 concurrent users and 673 system request transactions, 91.3% of the interface response time in the cloud computing system was within 50ms. Therefore, cloud computing systems have certain advantages in processing power data. © 2023 IEEE.

关键词： Cloud computing Data Center System Data Processing distributed computing Electricity Data

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Simulation of Multi-Option Pricing on distributed computing

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CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES 2012年第2期86卷 93-112页

作者： Lee, J. E. Kim, S. J. Seoul Natl Univ Interdisciplinary Program Computat Sci & Technol Seoul South Korea Seoul Natl Univ Dept Aerosp Engn Seoul South Korea

As the option trading nowadays has become popular, it is important to simulate efficiently large amounts of option pricings. The purpose of this paper is to show valuations of large amount of options, using network distribute computing resources. We valuated 108 options simultaneously on the self-made cluster computer system which is very inexpensive, compared to the supercomputer or the GPU adopting system. For the numerical valuations of options, we developed the option pricing software to solve the Black-Scholes partial differential equation by the finite element method. This yielded accurate values of options and the Greeks with reasonable computational times. This was executed on the single node and then extended on the cluster computer system. We can infer our research for large amounts options on the distributed computing will be a highly attractive alternative to devising hedging strategies or developing new pricing models.

关键词： option pricing model Black-Scholes equation finite element method distributed computing cluster computer system

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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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