Journal articles: 'Distributed systems and algorithms'

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Kindler, Ekkart, and Sibylle Peuker. "Integrating Distributed Algorithms into Distributed Systems." Fundamenta Informaticae 37, no. 3 (1999): 291–309. http://dx.doi.org/10.3233/fi-1999-37306.

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Lee, Hyeon-Seok, and Jae-Jung Yun. "Advanced MPPT Algorithm for Distributed Photovoltaic Systems." Energies 12, no. 18 (September 19, 2019): 3576. http://dx.doi.org/10.3390/en12183576.

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The basic and adaptive maximum power point tracking algorithms have been studied for distributed photovoltaic systems to maximize the energy production of a photovoltaic (PV) module. However, the basic maximum power point tracking algorithms using a fixed step size, such as perturb and observe and incremental conductance, suffer from a trade-off between tracking accuracy and tracking speed. Although the adaptive maximum power point tracking algorithms using a variable step size improve the maximum power point tracking efficiency and dynamic response of the basic algorithms, these algorithms still have the oscillations at the maximum power point, because the variable step size is sensitive to external factors. Therefore, this paper proposes an enhanced maximum power point tracking algorithm that can have fast dynamic response, low oscillations, and high maximum power point tracking efficiency. To achieve these advantages, the proposed maximum power point tracking algorithm uses two methods that can apply the optimal step size to each operating range. In the operating range near the maximum power point, a small fixed step size is used to minimize the oscillations at the maximum power point. In contrast, in the operating range far from the maximum power point, a variable step size proportional to the slope of the power-voltage curve of PV module is used to achieve fast tracking speed under dynamic weather conditions. As a result, the proposed algorithm can achieve higher maximum power point tracking efficiency, faster dynamic response, and lower oscillations than the basic and adaptive algorithms. The theoretical analysis and performance of the proposed algorithm were verified by experimental results. In addition, the comparative experimental results of the proposed algorithm with the other maximum power point tracking algorithms show the superiority of the proposed algorithm.

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Dao-Tran, Minh, Thomas Eiter, Michael Fink, and Thomas Krennwallner. "Distributed Evaluation of Nonmonotonic Multi-context Systems." Journal of Artificial Intelligence Research 52 (April 29, 2015): 543–600. http://dx.doi.org/10.1613/jair.4574.

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Multi-context Systems (MCSs) are a formalism for systems consisting of knowledge bases (possibly heterogeneous and non-monotonic) that are interlinked via bridge rules, where the global system semantics emerges from the local semantics of the knowledge bases (also called contexts) in an equilibrium. While MCSs and related formalisms are inherently targeted for distributed set- tings, no truly distributed algorithms for their evaluation were available. We address this short- coming and present a suite of such algorithms which includes a basic algorithm DMCS, an ad- vanced version DMCSOPT that exploits topology-based optimizations, and a streaming algorithm DMCS-STREAMING that computes equilibria in packages of bounded size. The algorithms be- have quite differently in several respects, as experienced in thorough experimental evaluation of a system prototype. From the experimental results, we derive a guideline for choosing the appropriate algorithm and running mode in particular situations, determined by the parameter settings.

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Challenger, Moharram, Elif Haytaoglu, Gorkem Tokatli, Orhan Dagdeviren, and Kayhan Erciyes. "A Hybrid Distributed Mutual Exclusion Algorithm for Cluster-Based Systems." Mathematical Problems in Engineering 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/703414.

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Distributed mutual exclusion is a fundamental problem which arises in various systems such as grid computing, mobile ad hoc networks (MANETs), and distributed databases. Reducing key metrics like message count per any critical section (CS) and delay between two CS entrances, which is known as synchronization delay, is a great challenge for this problem. Various algorithms use either permission-based or token-based protocols. Token-based algorithms offer better communication costs and synchronization delay. Raymond's and Suzuki-Kasami's algorithms are well-known token-based ones. Raymond's algorithm needs onlyO(log2(N)) messages per CS and Suzuki-Kasami's algorithm needs just one message delivery time between two CS entrances. Nevertheless, both algorithms are weak in the other metric, synchronization delay and message complexity correspondingly. In this work, a new hybrid algorithm is proposed which gains from powerful aspects of both algorithms. Raysuz's algorithm (the proposed algorithm) uses a clustered graph and executes Suzuki-Kasami's algorithm intraclusters and Raymond's algorithm interclusters. This leads to have better message complexity than that of pure Suzuki-Kasami's algorithm and better synchronization delay than that of pure Raymond's algorithm, resulting in an overall efficient DMX algorithm pure algorithm.

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Castanon, D., and D. Teneketzis. "Distributed estimation algorithms for nonlinear systems." IEEE Transactions on Automatic Control 30, no. 5 (May 1985): 418–25. http://dx.doi.org/10.1109/tac.1985.1103972.

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Ciobanu, Gabriel. "Distributed algorithms over communicating membrane systems." Biosystems 70, no. 2 (July 2003): 123–33. http://dx.doi.org/10.1016/s0303-2647(03)00035-2.

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Zhuravel, S., O. Shpur, and Yu Pyrih. "METHOD OF ACHIEVING CONSENSUS IN DISTRIBUTED SERVICE." Information and communication technologies, electronic engineering 2, no. 2 (December 2022): 58–66. http://dx.doi.org/10.23939/ictee2022.02.058.

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This article examines the problems of distributed algorithms and proposes their solution using temporal analysis. There are many things that can go wrong in distributed systems that can cause the system to crash. The solution to this is to build a system that can withstand the problems that arise during its operation. It turns out that having an algorithm capable of reaching consensus is extremely important for systems that want to function properly despite network failures. Although consensus is omitted in performance-oriented systems, they still rely heavily on systems that implement consensus algorithms for them (such as Zookeeper, etc.) to handle the consensus-reduced task, while at the same time having some weaker consistency model. In turn, the algorithms available today have several problems, the solution of which will significantly increase the performance of the algorithms and, as a result, the systems that use them. This article discusses the problems that arise in existing implementations and presents a data analysis technique and model for solving one of the algorithm problem.

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Mishra, Swati, and Sanjaya Kumar Panda. "Efficient Fault Tolerant Algorithms for Internet Distributed Systems." International Journal of Knowledge Discovery in Bioinformatics 7, no. 1 (January 2017): 71–90. http://dx.doi.org/10.4018/ijkdb.2017010106.

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In the current era, Internet is the fastest growing technology. It is a global network of distributed systems that interconnects with each other to share various resources and computation. The complexity of the distributed systems is rapidly increasing, which leads to an increased susceptibility of failure to the participating computers such as clients and servers and their interconnections. Therefore, the main challenge is to address such failures and develop efficient algorithms to tolerate such failures. In this paper, four fault tolerant algorithms, namely server-based fault tolerant (SFT), client-based fault tolerant (CFT), client-server fault tolerant (CSFT) and connection fault tolerant (CoFT) are proposed to deal with the above challenge. The proposed algorithms are evaluated in terms of number of failures (NOF), load factor (LF) and load standard deviation (LSD) and compared with an existing algorithm. The comparison results show the superior performance of the proposed algorithms.

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Logeswaran, Rajasvaran, and Li-Choo Chen. "Load Balancing Algorithms in Distributed Service Architectures for Medical Applications." International Journal of Healthcare Information Systems and Informatics 5, no. 1 (January 2010): 76–90. http://dx.doi.org/10.4018/jhisi.2010110305.

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This paper investigates the performance of two proposed load balancing algorithms for Object-Oriented Distributed Service Architectures (DSA) that are open and flexible, enabling rapid and easy development of new applications on various kinds of software and hardware platforms, catering for telecommunications and distributed medical applications. The proposed algorithms, namely, Node Status Algorithm and Random Sender Initiated Algorithm, have been developed as solutions to the performance problems faced by the DSA. The performance of the proposed algorithms have been tested and compared with baseline load balancing algorithms, namely the Random Algorithm and Shortest Queue Algorithm. Simulation results show that both the proposed algorithms perform better than the baseline algorithms, especially in heavily loaded conditions. This paper discusses the mechanisms of the algorithms and reports on the investigations that have been carried out in comparing the load balancing algorithms implemented on a DSA-based network, which is useful for the distributed computing requirements of the medical field.

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Balanescu, Tudor, Radu Nicolescu, and Huiling Wu. "Asynchronous P Systems." International Journal of Natural Computing Research 2, no. 2 (April 2011): 1–18. http://dx.doi.org/10.4018/jncr.2011040101.

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In this paper, the authors propose a new approach to fully asynchronous P systems, and a matching complexity measure, both inspired from the field of distributed algorithms. The authors validate the proposed approach by implementing several well-known distributed depth-first search (DFS) and breadth-first search (BFS) algorithms. Empirical results show that the proposed P algorithms have shorter descriptions and achieve a performance comparable to the corresponding distributed algorithms.

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Zhao, Sipei. "Analysis of distributed active noise control systems." Journal of the Acoustical Society of America 154, no. 4_supplement (October 1, 2023): A122. http://dx.doi.org/10.1121/10.0022988.

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Active noise control technology has witnessed a great success in the past decades for local noise control in headsets and similar devices. However, many challenges are yet to be tackled for active control of noise over a large spatial region, where multichannel systems are needed. One of the significant issues is the high computational complexity of multichannel adaptive algorithms running on a centralized processor. To overcome this problem, distributed and decentralized adaptation strategies have been proposed to spread the computational burden over multiple processors recently. This paper will review existing distributed and decentralized active noise control systems based on the filtered-reference least mean square algorithms and analyze their convergence behavior under a unified framework. The convergence conditions in the mean and mean-square sense are studied and the transient and stead-state mean square errors are also analyzed. Simulations are performed to verify the proposed framework and analysis. This work will provide insights into current distributed active noise control systema and shed light on developing new algorithm design in the future.

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Lu, You Wei, Zhen Zhen Xu, and Feng Xia. "Prediction-Based Independent Task Scheduling for Heterogeneous Distributed Computing Systems." Advanced Materials Research 457-458 (January 2012): 1039–46. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.1039.

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Independent task scheduling algorithms in distributed computing systems deal with three main conflicting factors including load balance, task execution time and scheduling cost. In this paper, the problem of scheduling tasks arriving at a low rate and with long execution time in heterogeneous computing systems is studied, and a new scheduling algorithm based on prediction is proposed. This algorithm evaluates the utility of task scheduling based on statistics and prediction to solve the influence of heterogeneous computing systems. The experimental results reveal that the proposed algorithm adequately balances the conflicting factors, and thus performs better than some classical algorithms such as MCT and MET when the parameters are well selected.

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Abdoos, Mahboobeh. "Improved Deadlock Prevention Algorithms in Distributed Systems." International Journal of Engineering and Applied Computer Science 02, no. 02 (February 28, 2017): 75–78. http://dx.doi.org/10.24032/ijeacs/0202/05.

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Viktorov, Oleg, and Afif Mghawish. "Fault-tolerant Distributed Systems with Diagnostics Algorithms." Journal of Computer Science, no. 2 (February 1, 2005): 104–5. http://dx.doi.org/10.3844/jcssp.2005.104.105.

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Al-Dabass, David, Abdalla Zreiba, David J. Evans, and Siva Sivayoganathan. "Parameter Estimation Algorithms for Hierarchical Distributed Systems." International Journal of Computer Mathematics 79, no. 1 (January 2002): 65–88. http://dx.doi.org/10.1080/00207160211916.

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Cardellini, V., M. Colajanni, and P. S. Yu. "Request redirection algorithms for distributed web systems." IEEE Transactions on Parallel and Distributed Systems 14, no. 4 (April 2003): 355–68. http://dx.doi.org/10.1109/tpds.2003.1195408.

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Ma, Ying, Sheng Chen, Chengwen Xing, Xiangyuan Bu, and Lajos Hanzo. "Decomposition Optimization Algorithms for Distributed Radar Systems." IEEE Transactions on Signal Processing 64, no. 24 (December 15, 2016): 6443–58. http://dx.doi.org/10.1109/tsp.2016.2602801.

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Bhat, Prashanth B., Viktor K. Prasanna, and C. S. Raghavendra. "Adaptive Communication Algorithms for Distributed Heterogeneous Systems." Journal of Parallel and Distributed Computing 59, no. 2 (November 1999): 252–79. http://dx.doi.org/10.1006/jpdc.1999.1571.

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Yuan, P., M. Moallem, and R. V. Patel. "REAL-TIME SCHEDULING OF DISTRIBUTED MULTI-ROBOT MANIPULATOR SYSTEMS." Transactions of the Canadian Society for Mechanical Engineering 29, no. 2 (June 2005): 179–94. http://dx.doi.org/10.1139/tcsme-2005-0012.

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This paper presents an online task-oriented scheduling method and an off-line scheduling algorithm that can be used for cooperative control of a distributed multi-robot manipulator system. Satisfaction of temporal deadlines and tasks-relative constraints are considered in this work. With the proposed algorithms, both the timing constraints and relative task dependencies can be satisfied when the worst-case execution time is unknown. The total execution time of the assembly tasks can be significantly improved compared with other known scheduling algorithms such as the First-In-First-Out and Round Robin scheduling methods. Experimental results are presented indicating that the proposed algorithm can be used for improving the performance of multi-robot systems in terms of timing and resource constraints.

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Chu, Xiaofei. "A Distributed Online Newton Step Algorithm for Multi-Agent Systems." Mathematical Problems in Engineering 2022 (October 28, 2022): 1–14. http://dx.doi.org/10.1155/2022/1007032.

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Most of the current algorithms for solving distributed online optimization problems are based on the first-order method, which are simple in computation but slow in convergence. Newton’s algorithm with fast convergence speed needs to calculate the Hessian matrix and its inverse, leading to computationally complex. A distributed online optimization algorithm based on Newton’s step is proposed in this paper, which constructs a positive definite matrix by using the first-order information of the objective function to replace the inverse of the Hessian matrix in Newton’s method. The convergence of the algorithm is proved theoretically and the regret bound of the algorithm is obtained. Finally, numerical experiments are used to verify the feasibility and efficiency of the proposed algorithm. The experimental results show that the proposed algorithm has an efficient performance on practical problems, compared to several existing gradient descent algorithms.

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Sapaty, P. S. "Spatial grasp model for dynamic distributed systems." Mathematical machines and systems 3 (2021): 3–21. http://dx.doi.org/10.34121/1028-9763-2021-3-21.

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More complex distributed and intelligent systems which relate to economy, ecology, communi-cations, security and defense, and cover both terrestrial and celestial environments are being developed. Their efficient management, especially in dynamic and unpredictable situations, needs serious investigations and development in scientific and technological areas. Their tradi-tional representations as parts operating by certain algorithms and exchanging messages are be-coming inadequate as such systems need much stronger integration to operate as holistic organ-isms pursuing global and often varying goals. This paper is focused on a completely different paradigm for organization and management of large dynamic and distributed systems. This par-adigm extends and transforms the notion of an algorithm for the description of knowledge pro-cessing logic. Moreover, it allows it to exist, propagate and operate as an integral whole in any distributed spaces which may constantly change their volumes and structures. Taking into con-sideration some organizational features related to dangerous viruses, as well as recent pandem-ics, this ubiquitous Spatial Grasp (SG) model is presented in the paper at philosophical and im-plementation levels, together with the introduction of special spatial charts for its exhibition and studies, which extend traditional algorithmic flowcharts towards working directly in dis-tributed spaces. Utilization of this model for the creation of resultant Spatial Grasp Technology and its basic Spatial Grasp Language, already described in details in numerous publications, is briefed as well. Elementary examples of dealing with distributed networks, collective human-robotic behavior, removal of space debris by a constellation of cleaning satellites and simulat-ing the spread of virus and vaccination against it explain SG advantages over traditional system organizations.

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FLATEBO, MITCHELL, and AJOY KUMAR DATTA. "DISTRIBUTED DEADLOCK DETECTION ALGORITHMS." Parallel Processing Letters 02, no. 01 (March 1992): 21–30. http://dx.doi.org/10.1142/s0129626492000143.

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A distributed system consists of a set of loosely connected state machines which do not share a global memory. The global state of the system depends on the state of each process in the system. The set of global states can be split up into two categories, legal and illegal. This paper deals with methods of detecting deadlocks in distributed systems. One way that has been used to detect deadlocks is by sending probes around the system. If a process thinks that it may be deadlocked, it initiates a probe. If the probe is received by the initiator, the initiator declares deadlock. This paper uses the idea of states of processes In order to detect the deadlock. The algorithm runs continually and does not have to be initiated. This paper presents deadlock detection algorithms for single and multiple outstanding requests. A method for deadlock resolution is also discussed. The algorithms detect all deadlocks and do not detect false deadlocks.

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CHO, KILSEOK, ALAN D. GEORGE, RAJ SUBRAMANIYAN, and KEONWOOK KIM. "PARALLEL ALGORITHMS FOR ADAPTIVE MATCHED-FIELD PROCESSING ON DISTRIBUTED ARRAY SYSTEMS." Journal of Computational Acoustics 12, no. 02 (June 2004): 149–74. http://dx.doi.org/10.1142/s0218396x04002274.

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Matched-field processing (MFP) localizes sources more accurately than plane-wave beamforming by employing full-wave acoustic propagation models for the cluttered ocean environment. The minimum variance distortionless response MFP (MVDR–MFP) algorithm incorporates the MVDR technique into the MFP algorithm to enhance beamforming performance. Such an adaptive MFP algorithm involves intensive computational and memory requirements due to its complex acoustic model and environmental adaptation. The real-time implementation of adaptive MFP algorithms for large surveillance areas presents a serious computational challenge where high-performance embedded computing and parallel processing may be required to meet real-time constraints. In this paper, three parallel algorithms based on domain decomposition techniques are presented for the MVDR–MFP algorithm on distributed array systems. The parallel performance factors in terms of execution times, communication times, parallel efficiencies, and memory capacities are examined on three potential distributed systems including two types of digital signal processor arrays and a cluster of personal computers. The performance results demonstrate that these parallel algorithms provide a feasible solution for real-time, scalable, and cost-effective adaptive beamforming on embedded, distributed array systems.

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Ruan, Yali, Yingting Luo, and Yunmin Zhu. "Globally Optimal Distributed Kalman Filtering for Multisensor Systems with Unknown Inputs." Sensors 18, no. 9 (September 6, 2018): 2976. http://dx.doi.org/10.3390/s18092976.

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In this paper, the state estimation for dynamic system with unknown inputs modeled as an autoregressive AR (1) process is considered. We propose an optimal algorithm in mean square error sense by using difference method to eliminate the unknown inputs. Moreover, we consider the state estimation for multisensor dynamic systems with unknown inputs. It is proved that the distributed fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurement; therefore, it achieves the best performance. The computation complexity of the traditional augmented state algorithm increases with the augmented state dimension. While, the new algorithm shows good performance with much less computations compared to that of the traditional augmented state algorithms. Moreover, numerical examples show that the performances of the traditional algorithms greatly depend on the initial value of the unknown inputs, if the estimation of initial value of the unknown input is largely biased, the performances of the traditional algorithms become quite worse. However, the new algorithm still works well because it is independent of the initial value of the unknown input.

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Toroev, A. S., and A. B. Sizonenko. "ANALYSIS OF THE EFFECTIVENESS OF CONSENSUS-BUILDING ALGORITHMS IN DISTRIBUTED DATA PROCESSING SYSTEMS BASED ON BLOCKCHAIN TECHNOLOGY." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 197 (November 2020): 13–22. http://dx.doi.org/10.14489/vkit.2020.11.pp.013-022.

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The authors of the article consider consensus-building algorithms that are central to the life cycle of transactional exchange between nodes of a DDPS (Distributed Data Processing System) based on blockchain technology. The term under consideration is formally defined. It is proved that when developing these algorithms, it is necessary to choose the main indicators and methods for calculating them, which can be used to evaluate their effectiveness. The process of false forking is described in detail, and the probability of its occurrence is calculated in order to assess the security of data stored in the distributed ledger. When analyzing existing consensus-building algorithms based on statistical data, it is shown that the Byzantine Fault Tolerance Delegated Proof-of-Stake (BFT-DPoS) algorithm has a higher level of efficiency not only in terms of security, but also in terms of performance compared to other algorithms. There is a lack of comprehensive evaluation of candidates for winning Registrar nodes that generate (mining) blocks and distribute them to verifier nodes. It is concluded that the elimination of the above-mentioned drawback would provide a more correct construction of the distributed ledger, since most of the existing algorithms are focused on applicability only in cryptocurrency systems and are not universal. To solve this problem, we propose the need to synthesize the BFT-DPoS and Proof-of-Importance algorithms in order to make it applicable to any type of DDPS using distributed ledger technology (blockchain).

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Toroev, A. S., and A. B. Sizonenko. "ANALYSIS OF THE EFFECTIVENESS OF CONSENSUS-BUILDING ALGORITHMS IN DISTRIBUTED DATA PROCESSING SYSTEMS BASED ON BLOCKCHAIN TECHNOLOGY." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 197 (November 2020): 13–22. http://dx.doi.org/10.14489/vkit.2020.11.pp.013-022.

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The authors of the article consider consensus-building algorithms that are central to the life cycle of transactional exchange between nodes of a DDPS (Distributed Data Processing System) based on blockchain technology. The term under consideration is formally defined. It is proved that when developing these algorithms, it is necessary to choose the main indicators and methods for calculating them, which can be used to evaluate their effectiveness. The process of false forking is described in detail, and the probability of its occurrence is calculated in order to assess the security of data stored in the distributed ledger. When analyzing existing consensus-building algorithms based on statistical data, it is shown that the Byzantine Fault Tolerance Delegated Proof-of-Stake (BFT-DPoS) algorithm has a higher level of efficiency not only in terms of security, but also in terms of performance compared to other algorithms. There is a lack of comprehensive evaluation of candidates for winning Registrar nodes that generate (mining) blocks and distribute them to verifier nodes. It is concluded that the elimination of the above-mentioned drawback would provide a more correct construction of the distributed ledger, since most of the existing algorithms are focused on applicability only in cryptocurrency systems and are not universal. To solve this problem, we propose the need to synthesize the BFT-DPoS and Proof-of-Importance algorithms in order to make it applicable to any type of DDPS using distributed ledger technology (blockchain).

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Wu, Ningchao, Xingchuan Bao, Dayang Wang, Song Jiang, Manjun Zhang, and Jing Zou. "Task Offloading in Real-Time Distributed Energy Power Systems." Electronics 13, no. 14 (July 12, 2024): 2747. http://dx.doi.org/10.3390/electronics13142747.

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The distributed energy power system needs to provide sufficient and flexible computing power on demand to meet the increasing digitization and intelligence requirements of the smart grid. However, the current distribution of the computing power and loads in the energy system is unbalanced, with data center loads continuously increasing, while there is a large amount of idle computing power at the edge. Meanwhile, there are a large number of real-time computing tasks in the distributed energy power system, which have strict requirements on execution deadlines and require reasonable scheduling of multi-level heterogeneous computing power to meet real-time computing demands. Based on the aforementioned background and issues, this paper studies the real-time service scheduling problem in a multi-level heterogeneous computing network of distributed energy power systems. Specifically, we consider the divisibility of tasks in the model. This paper presents a hierarchical real-time task-scheduling framework specifically designed for distributed energy power systems. The framework utilizes an orchestrating agent (OA) as the execution environment for the scheduling module. Building on this, we propose a hierarchical selection algorithm for choosing the appropriate network layer for real-time tasks. Further, we develop two scheduling algorithms based on greedy strategy and genetic algorithm, respectively, to effectively schedule tasks. Experiments show that the proposed algorithms have a superior success rate in scheduling compared to other current algorithms.

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Natan, Avraham, Roni Stern, and Meir Kalech. "Distributed Spectrum-Based Fault Localization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 5 (June 26, 2023): 6491–98. http://dx.doi.org/10.1609/aaai.v37i5.25798.

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Spectrum-Based Fault Localization (SFL) is a popular approach for diagnosing faulty systems. SFL algorithms are inherently centralized, where observations are collected and analyzed by a single diagnoser. Applying SFL to diagnose distributed systems is challenging, especially when communication is costly and there are privacy concerns. We propose two SFL-based algorithms that are designed for distributed systems: one for diagnosing a single faulty component and one for diagnosing multiple faults. We analyze these algorithms theoretically and empirically. Our analysis shows that the distributed SFL algorithms we developed output identical diagnoses to centralized SFL while preserving privacy.

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Satybaldiyeva, A., A. Ismailova, R. Moldasheva, A. Mukhanova, and K. Kadirkulov. "ABSTRACT DATA TYPES FOR KNOWLEDGE REPRESENTATION AND SPECIFICATION OF MULTI-AGENT SYSTEMS." PHYSICO-MATHEMATICAL SERIES 2, no. 336 (April 15, 2021): 48–55. http://dx.doi.org/10.32014/2021.2518-1726.20.

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Distributed system is a group of decentralized interacting executers. Distributed algorithm is the communication protocol for a distributed system that transforms the group into a team to solve some task. Multiagent system is a distributed system that consists of autonomous reactive agents, i.e. executers which internal states can be characterized in terms Believes (B), Desires (D), and Intentions (I). Multiagent algorithm is a distributed algorithm for a multiagent system. The article discusses the basic concepts of agents and multi-agent systems. Also, two problems of multi-agent algorithms for representing knowledge in the context of Social Software Engineering are considered. A number of new multi-agent algorithms are presented, and their correctness is proved. The main characteristics of agents are provided, such as autonomy, proactivity, social ability, and reactivity; also, agents can have such additional characteristics as persistence, reasonability, performance, mobility, personality, and rationality. A number of new multi-agent algorithms are presented, and their correctness is proved. Two statements have been proved for solving RAM and MRP problems. This time we address a social issue of agent anonymity and privacy in these algo-rithms.

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Liu, Shuxin, Haijun Jiang, Liwei Zhang, and Xuehui Mei. "Distributed Adaptive Optimization for Generalized Linear Multiagent Systems." Discrete Dynamics in Nature and Society 2019 (August 1, 2019): 1–10. http://dx.doi.org/10.1155/2019/9181093.

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In this paper, the edge-based and node-based adaptive algorithms are established, respectively, to solve the distribution convex optimization problem. The algorithms are based on multiagent systems with general linear dynamics; each agent uses only local information and cooperatively reaches the minimizer. Compared with existing results, a damping term in the adaptive law is introduced for the adaptive algorithms, which makes the algorithms more robust. Under some sufficient conditions, all agents asymptotically converge to the consensus value which minimizes the cost function. An example is provided for the effectiveness of the proposed algorithms.

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Vasileva, Svetlana, and Aleksandar Milev. "Simulation Studies of Distributed Two-phase Locking in Distributed Database Management Systems." Information Technologies and Control 13, no. 1-2 (June 1, 2015): 46–55. http://dx.doi.org/10.1515/itc-2016-0010.

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Abstract This paper considers algorithms simulating the implementation of distributed two-phase locking (2PL) protocols in distributed database systems and simulation results. It describes specifically the simulations of two-version 2PL and 2PL with integrated timestamp ordering mechanism. Integrated modelling algorithms for deadlock avoiding are suggested in the paper: twoversion architecture of database and timestamp ordering strategy “wait-die”. The results of the simulations of these two variants of the 2PL method at different scales of the networks for data transmission and at different intensities of inflow transactions are also presented. Modelling algorithms are developed by means of the system for simulation modelling GPSS World Personal Version.

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Duda, Zdzisław. "Hierarchical filtration for distributed linear multisensor systems." Archives of Control Sciences 22, no. 4 (December 1, 2012): 507–18. http://dx.doi.org/10.2478/v10170-011-0038-7.

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In the paper two filtration algorithms for distributed multisensor system are presented. The first one is derived for a linear dynamical system composed of local subsystems described by local state equations. Local estimates are sent to a central station to be fused and formed an optimal global estimate. The second algorithm is derived for a system observed by local nodes that determine estimates of the whole system using local information and periodically aggregated information from other nodes. Periodically local estimates are sent to the central station to be fused. Owing to this a reduced communication can be achieved

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Barenboim, Leonid, Michael Elkin, and Uri Goldenberg. "Locally-iterative Distributed (Δ + 1)-coloring and Applications." Journal of the ACM 69, no. 1 (February 28, 2022): 1–26. http://dx.doi.org/10.1145/3486625.

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We consider graph coloring and related problems in the distributed message-passing model. Locally-iterative algorithms are especially important in this setting. These are algorithms in which each vertex decides about its next color only as a function of the current colors in its 1-hop-neighborhood . In STOC’93 Szegedy and Vishwanathan showed that any locally-iterative Δ + 1-coloring algorithm requires Ω (Δ log Δ + log * n ) rounds, unless there exists “a very special type of coloring that can be very efficiently reduced” [ 44 ]. No such special coloring has been found since then. This led researchers to believe that Szegedy-Vishwanathan barrier is an inherent limitation for locally-iterative algorithms and to explore other approaches to the coloring problem [ 2 , 3 , 19 , 32 ]. The latter gave rise to faster algorithms, but their heavy machinery that is of non-locally-iterative nature made them far less suitable to various settings. In this article, we obtain the aforementioned special type of coloring. Specifically, we devise a locally-iterative Δ + 1-coloring algorithm with running time O (Δ + log * n ), i.e., below Szegedy-Vishwanathan barrier. This demonstrates that this barrier is not an inherent limitation for locally-iterative algorithms. As a result, we also achieve significant improvements for dynamic, self-stabilizing, and bandwidth-restricted settings. This includes the following results: We obtain self-stabilizing distributed algorithms for Δ + 1-vertex-coloring, (2Δ - 1)-edge-coloring, maximal independent set, and maximal matching with O (Δ + log * n ) time. This significantly improves previously known results that have O(n) or larger running times [ 23 ]. We devise a (2Δ - 1)-edge-coloring algorithm in the CONGEST model with O (Δ + log * n ) time and O (Δ)-edge-coloring in the Bit-Round model with O (Δ + log n ) time. The factors of log * n and log n are unavoidable in the CONGEST and Bit-Round models, respectively. Previously known algorithms had superlinear dependency on Δ for (2Δ - 1)-edge-coloring in these models. We obtain an arbdefective coloring algorithm with running time O (√ Δ + log * n ). Such a coloring is not necessarily proper, but has certain helpful properties. We employ it to compute a proper (1 + ε)Δ-coloring within O (√ Δ + log * n ) time and Δ + 1-coloring within O (√ Δ log Δ log * Δ + log * n ) time. This improves the recent state-of-the-art bounds of Barenboim from PODC’15 [ 2 ] and Fraigniaud et al. from FOCS’16 [ 19 ] by polylogarithmic factors. Our algorithms are applicable to the SET-LOCAL model [ 25 ] (also known as the weak LOCAL model). In this model a relatively strong lower bound of Ω (Δ 1/3 ) is known for Δ + 1-coloring. However, most of the coloring algorithms do not work in this model. (In Reference [ 25 ] only Linial’s O (Δ 2 )-time algorithm and Kuhn-Wattenhofer O (Δ log Δ)-time algorithms are shown to work in it.) We obtain the first linear-in-Δ Δ + 1-coloring algorithms that work also in this model.

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Stanković, Maja, and Dragan Antić. "DISTRIBUTED CONSENSUS-BASED CALIBRATION OF NETWORKED CONTROL SYSTEMS." Facta Universitatis, Series: Automatic Control and Robotics 18, no. 2 (January 27, 2020): 095. http://dx.doi.org/10.22190/fuacr1902095s.

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In this paper a new algorithm for distributed blind macro-calibration of Networked Control Systems is presented. It is assumed that the measured signal is stochastic and unknown. The algorithm is in the form of recursions of gradient type for estimation of the correction parameters for sensor gains and offsets. The recursion for gain correction is autonomous, derived from the measurement increments. The recursion for offset correction is based on differences between local measurements and utilizes the results of gain correction. It is proved that the algorithm provides asymptotic convergence to consensus in the sense that the corrected gains and offsets are equal for all sensors. It is demonstrated that the adopted structure of the algorithm enables obtaining high convergence rate, superior to the algorithms existing in the literature. Simulation results are provided illustrating the proposed algorithm properties.

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Al Maghayreh, Eslam, Iyad Abu Doush, and Faisal Alkhateeb. "Detecting Distributed Predicates Using Genetic Algorithms." International Journal of Intelligent Information Technologies 9, no. 1 (January 2013): 56–70. http://dx.doi.org/10.4018/jiit.2013010104.

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Detection of distributed predicates is one of the techniques that have been used in the literature to improve the dependability of distributed programs. This technique (sometimes referred to as runtime verification) is used to verify that a given run of a distributed program satisfies certain properties (specified as predicates). In general, the detection of a distributed predicate can incur significant overhead due to the existence of multiple processes running concurrently. Several techniques have been introduced in the literature to efficiently detect distributed predicates. However, most of these techniques work efficiently for certain classes of predicates, like conjunctive predicates. In this paper, the authors have presented a technique based on genetic algorithms to efficiently detect distributed predicates under the possibly modality. The authors have used JGAP (Java Genetic Algorithms Package) to implement the algorithm and conducted several experiments to demonstrate its effectiveness.

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Rajendra Purohit, K R Chowdhary, S D Purohit. "Analysis of Distributed Algorithms for Big-Data." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (October 16, 2023): 671–79. http://dx.doi.org/10.52783/tjjpt.v44.i4.906.

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The parallel and distributed processing are becoming de facto industry standard, and a large part of the current research is targeted on how to make computing scalable and distributed, dynamically, without allocating the resources on permanent basis. The present article focuses on the study and performance of distributed and parallel algorithms their file systems, to achieve scalability at local level (OpenMP platform), and at global level where computing and file systems are distributed. Various applications, algorithms, file systems have been used to demonstrate the areas and their performance studies have been presented. The systems and applications chosen here are of open-source nature, due to their wider applicability.

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Lei, Xing Ming, Chang Feng Xing, Ling Wu, and Yun Feng Wen. "Multiple-Depot Vehicle Routing Problems as a Distributed Constraint Optimization Problem." Applied Mechanics and Materials 66-68 (July 2011): 1033–38. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.1033.

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The Distributed Constraint Optimization Problem (DCOP) is able to model a variety of distributed reasoning tasks that arise in multi-agent systems, and widely applying to distribute programming, scheduling and resource allocation etc. In order to solve the multi-depot vehicle routing problem (MDVRP) in distributed manner, this article show how DCOP can be used to model the MDVRP, and using existing various DCOP algorithm solve it base on Frodo simulation platform. We have evaluated the performances of various DCOP algorithms on an existing MDVRP.

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ERSHOV, NIKOLAY. "DEVELOPMENT AND RESEARCH OF DISTRIBUTED CONTROL ALGORITHMS FOR SWARM INTELLIGENCE SYSTEMS." Computational Nanotechnology 9, no. 2 (June 28, 2022): 21–34. http://dx.doi.org/10.33693/2313-223x-2022-9-2-21-34.

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The subject of this paper is the development and research of distributed algorithms for organizing collective behavior in swarm robotic systems in order to solve various applied problems with these systems. Using the example of solving the problem of collective cleaning of a given area, several swarm algorithms based on classical swarm models are constructed and studied: random walk model, Reynolds model, bacterial search algorithm, stochastic gradient method. The results of numerical experiments comparing the efficiency of the proposed methods are presented

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van Veen, B. D., O. Leblond, V. P. Mani, and D. J. Sebald. "Distributed adaptive algorithms for large dimensional MIMO systems." IEEE Transactions on Signal Processing 48, no. 4 (April 2000): 1076–85. http://dx.doi.org/10.1109/78.827541.

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Ali, Ammar Alhaj, Pavel Varacha, Said Krayem, Petr Zacek, and Andrzej Urbanek. "Distributed data mining systems: techniques, approaches and algorithms." MATEC Web of Conferences 210 (2018): 04038. http://dx.doi.org/10.1051/matecconf/201821004038.

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Nowadays, we are living in the midst of a data explosion and seeing a massive growth in databases so with the wide availability of huge amounts of data; necessarily we are become in need for turning this data into useful information and knowledge, where Data mining uncovers interesting patterns and relationships hidden in a large volume of raw data and big data is a new term used to identify the datasets that are of large size and have grater complexity. The knowledge gained from data can be used for applications such as market analysis, customer retention and production control. Data mining is a massive computing task that deals with huge amount of stored data in a centralized or distributed system to extract useful information or knowledge. In this paper, we will discuss Distributed Data Mining systems, approaches, Techniques and algorithms to deal with distributed data to discover knowledge from distributed data in an effective and efficient way.

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Griswold, Victor Jon. "Core algorithms for autonomous monitoring of distributed systems." ACM SIGPLAN Notices 26, no. 12 (December 1991): 36–45. http://dx.doi.org/10.1145/127695.122762.

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HIROYASU, Tomoyuki, and Mitsunori MIKI. "Parallel Distributed Genetic Algorithms on PC Cluster Systems." Proceedings of OPTIS 2000.4 (2000): 305–10. http://dx.doi.org/10.1299/jsmeoptis.2000.4.305.

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Reed, Benjamin, and Darrell D. E. Long. "Analysis of caching algorithms for distributed file systems." ACM SIGOPS Operating Systems Review 30, no. 3 (July 1996): 12–21. http://dx.doi.org/10.1145/230908.230913.

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Allerton, D. J., and H. Jia. "Distributed data fusion algorithms for inertial network systems." IET Radar, Sonar & Navigation 2, no. 1 (February 1, 2008): 51–62. http://dx.doi.org/10.1049/iet-rsn:20060159.

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Feijó, B., P. C. R. Gomes, S. Scheer, and J. Bento. "Online algorithms supporting emergence in distributed CAD systems." Advances in Engineering Software 32, no. 10-11 (October 2001): 779–87. http://dx.doi.org/10.1016/s0965-9978(01)00029-1.

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Leslie, Robert, and Sati McKenzie. "Evaluation of loadsharing algorithms for heterogeneous distributed systems." Computer Communications 22, no. 4 (March 1999): 376–89. http://dx.doi.org/10.1016/s0140-3664(98)00262-x.

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Wang, Aijuan, Tao Dong, and Xiaofeng Liao. "Distributed optimal consensus algorithms in multi-agent systems." Neurocomputing 339 (April 2019): 26–35. http://dx.doi.org/10.1016/j.neucom.2019.01.044.

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Gravvanis, George A., and Hamid R. Arabnia. "Special section: Parallel and distributed algorithms and systems." Future Generation Computer Systems 22, no. 1-2 (January 2006): 32–33. http://dx.doi.org/10.1016/j.future.2004.11.013.

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Folino, Gianluigi, and Carlo Mastroianni. "Special section: Bio-inspired algorithms for distributed systems." Future Generation Computer Systems 26, no. 6 (June 2010): 835–37. http://dx.doi.org/10.1016/j.future.2010.03.002.

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Zou, Yao, Ziyang Meng, and Yiguang Hong. "Adaptive distributed optimization algorithms for Euler–Lagrange systems." Automatica 119 (September 2020): 109060. http://dx.doi.org/10.1016/j.automatica.2020.109060.

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Journal articles on the topic 'Distributed systems and algorithms'

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