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Impact of grid partitioning algorithms on combined distributed AC optimal power flow and parallel dynamic power grid simulation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第25期14卷 6133-6141页

作者： Kyesswa, Michael Murray, Alexander Schmurr, Philipp Cakmak, Hueseyin Kuehnapfel, Uwe Hagenmeyer, Veit Karlsruhe Inst Technol Inst Automat & Appl Informat Karlsruhe Germany

The complexity of most power grid simulation algorithms scales with the network size, which corresponds to the number of buses and branches in the grid. Parallel and distributed computing is one approach that can be used to achieve improved scalability. However, the efficiency of these algorithms requires an optimal grid partitioning strategy. To obtain the requisite power grid partitionings, the authors first apply several graph theory based partitioning algorithms, such as the Karlsruhe fast flow partitioner (KaFFPa), spectral clustering, and METIS. The goal of this study is an examination and evaluation of the impact of grid partitioning on power system problems. To this end, the computational performance of AC optimal power flow (OPF) and dynamic power grid simulation are tested. The partitioned OPF-problem is solved using the augmented Lagrangian based alternating direction inexact Newton method, whose solution is the basis for the initialisation step in the partitioned dynamic simulation problem. The computational performance of the partitioned systems in the implemented parallel and distributed algorithms is tested using various IEEE standard benchmark test networks. KaFFPa not only outperforms other partitioning algorithms for the AC OPF problem, but also for dynamic power grid simulation with respect to computational speed and scalability.

关键词： Newton method optimisation load flow IEEE standards power grids graph theory pattern clustering parallel algorithms power system simulation computational complexity optimal grid partitioning strategy spectral clustering power system problems computational performance partitioned OPF-problem augmented Lagrangian based alternating direction inexact Newton method partitioned dynamic simulation problem partitioned systems parallel algorithm distributed algorithms AC OPF problem grid partitioning algorithms combined distributed AC optimal power flow parallel dynamic power grid simulation distributed computing power grid simulation algorithms power grid partitionings parallel computing graph theory Karlsruhe fast flow partitioner KaFFPa METIS IEEE standard benchmark test networks

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Autonomous algorithms for centralized and distributed interference coordination: a virtual layer-based approach

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2014年第1期2014卷 1-20页

作者： Kasparick, Martin Wunder, Gerhard Fraunhofer Heinrich Hertz Inst D-10587 Berlin Germany Tech Univ Berlin D-10587 Berlin Germany

Interference mitigation techniques are essential for improving the performance of interference limited wireless networks. In this paper, we introduce novel interference mitigation schemes for wireless cellular networks with space division multiple access (SDMA). The schemes are based on a virtual layer that captures and simplifies the complicated interference situation in the network and that is used for power control. We show how optimization in this virtual layer generates gradually adapting power control settings that lead to autonomous interference minimization. Thereby, the granularity of control ranges from controlling frequency sub-band power via controlling the power on a per-beam basis, to a granularity of only enforcing average power constraints per beam. In conjunction with suitable short-term scheduling, our algorithms gradually steer the network towards a higher utility. We use extensive system-level simulations to compare three distributed algorithms and evaluate their applicability for different user mobility assumptions. In particular, it turns out that larger gains can be achieved by imposing average power constraints and allowing opportunistic scheduling instantaneously, rather than controlling the power in a strict way. Furthermore, we introduce a centralized algorithm, which directly solves the underlying optimization and shows fast convergence, as a performance benchmark for the distributed solutions. Moreover, we investigate the deviation from global optimality by comparing to a branch-and-bound-based solution.

关键词： Cellular interference management SDMA distributed algorithms Autonomous inter-cell coordination Power control Network utility maximization

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distributed Synchronous and Asynchronous algorithms for Semidefinite Programming With Diagonal Constraints

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第2期68卷 1007-1022页

作者： Jiang, Xia Zeng, Xianlin Sun, Jian Chen, Jie Beijing Inst Technol Key Lab Intelligent Control & Decis Complex Syst Beijing Peoples R China Beijing Inst Technol Sch Automat Beijing Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 200082 Peoples R China Minist Educ Beijing Inst Technol Beijing Adv Innovat Ctr Intelligent Robots & Syst Key Lab Biomimet Robots & Syst Beijing 100081 Peoples R China

This article develops distributed synchronous and asynchronous algorithms for the large-scale semidefinite programming with diagonal constraints, which has wide applications in combinatorial optimization, image processing, and community detection. The information of the semidefinite programming is allocated to multiple interconnected agents such that each agent aims to find a solution by communicating to its neighbors. Based on the low-rank property of solutions and the Burer-Monteiro factorization, we transform the original problem into a distributed optimization problem over unit spheres to reduce variable dimensions and ensure positive semidefiniteness without involving semidefinite projections, which are computationally expensive. For the distributed optimization problem, we propose distributed synchronous and asynchronous algorithms, both of which reduce computational burden and storage space compared with existing centralized algorithms. Specifically, the distributed synchronous algorithm almost surely escapes strict saddle points and converges to the set of optimal solutions to the optimization problem. In addition, the proposed distributed asynchronous algorithm allows communication delays and converges to critical points to the optimization problem under mild conditions. By applying the proposed algorithms to image segmentation, we illustrate the efficiency and convergence performance of the two proposed algorithms.

关键词： Optimization Symmetric matrices distributed algorithms Programming Costs Convergence Sun distributed optimization low-rank matrices semidefinite programming (SDP) with diagonal constraints synchronous and asynchronous algorithms

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Understanding complex, real-world systems through asynchronous, distributed decision-making algorithms

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JOURNAL OF SYSTEMS AND SOFTWARE 2001年第2期58卷 153-167页

作者： Ghosh, S Stevens Inst Technol Dept Elect & Comp Engn Secure Network Design Lab Hoboken NJ 07030 USA

Traditionally, the underlying decision-making algorithms for most real-world systems have been centralized. The term, real-world, refers to systems under computer control that relate to everyday life, are beneficial to the society in the large, and are generally large-scale in scope. Examples include AT&T's dynamic non-hierarchical routing (DNHR) for routing telephone calls, the North American advanced train control system (ATCS) for routing railways, the Swiss banking system (SIC), and inventory management algorithms. While centralized algorithms are simple, easy to conceive and implement, they execute sequentially on uniprocessors and are slow. In addition, by their very nature, centralized algorithms are highly susceptible to natural and artificial disasters. Synchronous distributed algorithms constitute a performance improvement over centralized algorithms, and have been used in fault simulation within the discipline of computer-aided design of digital systems and in matrix manipulations. However, their performance is limited due to frequent inherent synchronizations. This paper critically examines the nature of large-scale, real-world systems and observes that, fundamentally, most complex systems are composed of entities - concurrent, independent, and self-contained units of decision-making, that interact with each other, asynchronously. This paper presents a new class of algorithms asynchronous, distributed, decision-making (ADDM) algorithms, to constitute the underlying control of such systems. While ADDM algorithms are closely related to autonomous decentralized systems (ADS) in the principal elements, their characteristics and boundaries are defined rigorously. While ADDM algorithms are difficult to conceive, design, and implement, they constitute the natural and logical choice for systems control, and hold the promise of extracting the maximal parallelism inherent in these systems. In addition, in principle, true asynchronous systems can be described accur

关键词： systems physical processes asynchronous interactions distributed algorithms real-world systems

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Input-Output Performance of Linear-Quadratic Saddle-Point algorithms With Application to distributed Resource Allocation Problems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第5期65卷 2032-2045页

作者： Simpson-Porco, John W. Poolla, Bala Kameshwar Monshizadeh, Nima Dorfler, Florian Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Swiss Fed Inst Technol Zurich Automat Control Lab CH-8092 Zurich Switzerland Univ Groningen Engn & Technol Inst NL-9747 AG Groningen Netherlands

Saddle-point or primal-dual methods have recently attracted renewed interest as a systematic technique to design distributed algorithms, which solve convex optimization problems. When implemented online for streaming data or as dynamic feedback controllers, these algorithms become subject to disturbances and noise;convergence rates provide incomplete performance information, and quantifying input-output performance becomes more important. We analyze the input-output performance of the continuous-time saddle-point method applied to linearly constrained quadratic programs, providing explicit expressions for the saddle-point $\mathcal {H}_2$ norm under a relevant input-output configuration. We then proceed to derive analogous results for regularized and augmented versions of the saddle-point algorithm. We observe some rather peculiar effects-a modest amount of regularization significantly improves the transient performance, while augmentation does not necessarily offer improvement. We then propose a distributed dual version of the algorithm, which overcomes some of the performance limitations imposed by augmentation. Finally, we apply our results to a resource allocation problem to compare the input-output performance of various centralized and distributed saddle-point implementations and show that distributed algorithms may perform as well as their centralized counterparts.

关键词： Optimization Heuristic algorithms Convergence Resource management Standards distributed algorithms Transient analysis distributed algorithms input-output performance primal-dual dynamics resource allocation saddle-point methods system norms

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Exact distributed Load Centrality Computation: algorithms, Convergence, and Applications to Distance Vector Routing

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2020年第7期31卷 1693-1706页

作者： Maccari, Leonardo Ghiro, Lorenzo Guerrieri, Alessio Montresor, Alberto Lo Cigno, Renato Univ Venice I-30123 Venice Italy Univ Trento I-38122 Trento Italy SpazioDati Srl I-56021 Cascina Italy Univ Brescia I-25121 Brescia Italy

Many optimization techniques for networking protocols take advantage of topological information to improve performance. Often, the topological information at the core of these techniques is a centrality metric such as the Betweenness Centrality (BC) index. BC is, in fact, a centrality metric with many well-known successful applications documented in the literature, from resource allocation to routing. To compute BC, however, each node must run a centralized algorithm and needs to have the global topological knowledge;such requirements limit the feasibility of optimization procedures based on BC. To overcome restrictions of this kind, we present a novel distributed algorithm that requires only local information to compute an alternative similar metric, called Load Centrality (LC). We present the new algorithm together with a proof of its convergence and the analysis of its time complexity. The proposed algorithm is general enough to be integrated with any distance vector (DV) routing protocol. In support of this claim, we provide an implementation on top of Babel, a real-world DV protocol. We use this implementation in an emulation framework to show how LC can be exploited to reduce Babel's convergence time upon node failure, without increasing control overhead. As a key step towards the adoption of centrality-based optimization for routing, we study how the algorithm can be incrementally introduced in a network running a DV routing protocol. We show that even when only a small fraction of nodes participate in the protocol, the algorithm accurately ranks nodes according to their centrality.

关键词： Routing protocols Measurement Routing Indexes distributed algorithms Network topology Multi-hop networks mesh networks ad-hoc networks bellman-ford load centrality distributed algorithms failure recovery

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A review of distributed optimization: Problems, models and algorithms

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NEUROCOMPUTING 2022年第0期483卷 446-459页

作者： Zheng, Yanling Liu, Qingshan Southeast Univ Sch Math Nanjing 210096 Peoples R China Southeast Univ Jiangsu Prov Key Lab Networked Collect Intelligen Nanjing 210096 Peoples R China

With the development of big data and artificial intelligence, distributed optimization has emerged as an indispensable tool for solving large-scale problems. In particular, the multi-agent system based on distributed information processing can be elaborately designed for distributed optimization, in which the agents collaboratively minimize a global objective function made up of a sum of local objective cost functions subject to some local and/or global constraints. Inspired by the applications involving resource allocation, machine learning, power systems, sensor networks and cloud computing, a variety of distributed optimization models and algorithms have been investigated and developed. The optimization models include unconstrained and constrained problems in continuous and discontinuous systems with undirected and directed communication topology graphs. The constraints include bounded constraint, separable and inseparable equality and inequality constraints. Meanwhile, in distributed algorithms, every agent executes its local computation and updating on basis of its own data information and that exchanging with its neighboring agents by means of the underlying communication networks, in order to deal with the optimization problems in a distributed way. This paper is designed to provide a comprehensive overview of extant distributed models and algorithms for distributed optimization.(c) 2021 Elsevier B.V. All rights reserved.

关键词： distributed optimization distributed algorithms distributed models Multi-agent systems

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Low-Complexity distributed Radio Access Network Slicing: algorithms and Experimental Results

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IEEE-ACM TRANSACTIONS ON NETWORKING 2018年第6期26卷 2815-2828页

作者： D'Oro, Salvatore Restuccia, Francesco Melodia, Tommaso Palazzo, Sergio Dept Elect & Comp Engn Boston MA 02115 USA Univ Catania Dipartimento Ingn Elettr Elettron & Informat I-92125 Catania Italy

Radio access network (RAN) slicing is an effective methodology to dynamically allocate networking resources in 5G networks. One of the main challenges of RAN slicing is that it is provably an NP-Hard problem. For this reason, we design near-optimal low-complexity distributed RAN slicing algorithms. First, we model the slicing problem as a congestion game, and demonstrate that such game admits a unique Nash equilibrium (NE). Then, we evaluate the Price of Anarchy (PoA) of the NE, i.e., the efficiency of the NE as compared with the social optimum, and demonstrate that the PoA is upper-bounded by 3/2. Next, we propose two fully-distributed algorithms that provably converge to the unique NE without revealing privacy-sensitive parameters from the slice tenants. Moreover, we introduce an adaptive pricing mechanism of the wireless resources to improve the network owner's profit. We evaluate the performance of our algorithms through simulations and an experimental testbed deployed on the Amazon EC2 cloud, both based on a real-world dataset of base stations from the OpenCellID project. Results conclude that our algorithms converge to the NE rapidly and achieve near-optimal performance, while our pricing mechanism effectively improves the profit of the network owner.

关键词： Network slicing 5G congestion games game theory distributed algorithms

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Localized and distributed link scheduling algorithms in IoT under rayleigh fading

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COMPUTER NETWORKS 2019年 151卷 232-244页

作者： Yu, Kan Wang, Yinglong Yu, Jiguo Yu, Dongxiao Cheng, Xiuzhen Shan, Zhiguang Shandong Univ Sci & Technol Coll comp sci & Engn Qingdao 266510 Peoples R China Qilu Univ Technol Sch Comp Sci & Techonol Shandong Acad Sci Jinan 250353 Shandong Peoples R China Natl Supercomp Ctr Jinan Shandong Comp Sci Ctr Jinan 250014 Shandong Peoples R China Qufu Normal Univ Sch Informat Sci & Engn Rizhao 2767826 Peoples R China Shandong Univ Sch Comp Sci & Technol Qingdao 266510 Peoples R China State Informat Ctr Informatizat & Ind Dev Dept Beijing 100045 Peoples R China

The Internet of Things (IoT) is rapidly gaining ground in future wireless communications. Transmission reliability and latency are two significant measurements for the utilization of the IoT. In this paper, we aim to improve reliability and latency requirements by solving the link scheduling problem. Under the Rayleigh fading model, a more realistic interference model, we first localize the global interference by ignoring the interference outside some certain distance, and obtain the success probability of a transmission at least 1 - epsilon, where epsilon is an acceptable error probability of a transmission. Based on this key result, we then design two localized and distributed algorithms for one-slot scheduling problem (i.e., how to ensure that the selected links have high transmission reliability or can be scheduled successfully). In addition, we design a localized and distributed algorithm with time complexity of O(Delta('T,r)(max) log n) to resolve latency minimization problem (i.e., minimize the number of time slots until all transmissions were successful), where Delta('T,r)(max) is the maximum number of senders within R-T centered at a receiver in the network, where R-T is transmission range of a node. Theoretical analysis and extensive simulations demonstrate that the proposed algorithms can improve the reliability and latency requirements significantly. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Internet of things Link scheduling Rayleigh fading distributed algorithms

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Performance analysis of distributed deadlock detection algorithms

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2001年第4期13卷 623-636页

作者： Lee, S Kim, JL Inchon Natl Univ Educ Dept Comp Educ Kyeyang Gu Inchon 407753 South Korea Littauer Technol Co Ltd Kangnam Ku Seoul South Korea

This paper presents a probabilistic performance analysis of a deadlock detection algorithm in distributed systems. Although there has been extensive study on deadlock detection algorithms in distributed systems, little attention has been paid to the study of the performance of these algorithms. Most work on performance study has been achieved through simulation but not through an analytic model. Min [14], to the best of our knowledge, made the sole attempt to evaluate the performance of distributed deadlock detection algorithms analytically. Being different from Min's [14], our analytic approach takes the time-dependent behavior of each process into consideration rather than simply taking the mean-value estimation. Furthermore, the relation among the times when deadlocked processes become blocked is studied, which enhances the accuracy of the analysis. We measure performance metrics such as duration of deadlock, the number of algorithm invocations, and the mean waiting time of a blocked process. It is shown that the analytic estimates are nearly consistent with simulation results.

关键词： deadlock detection distributed algorithms distributed systems cycle detection wait-for graph

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