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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2019年第2期4卷 263-276页

作者： Kong, Fanxin Lu, Xingjian Liu, Xue Univ Penn Dept Comp & Informat Sci Philadelphia PA 19104 USA East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Smart City Collaborat Innovat Ctr Shanghai Peoples R China McGill Univ Sch Comp Sci Montreal PQ H3A 0G4 Canada

Cloud-based video streaming systems such as YouTube and Netflix are usually supported by the content delivery networks and data centers that can consume many megawatts of power. Most existing work independently studies the issues of improving quality of experience (QoE) for viewers and reducing the cost and emissions associated with the enormous energy usage of data centers. By contrast, this paper addresses them both, and jointly optimizes the QoE, the energy cost and emissions by intelligently allocating data center bandwidth among different client groups. Specially, we propose a distributed algorithm to achieve the optimal bandwidth allocation, given the prediction of future workload. The algorithm novelly decomposes the optimization process into separate ones, which are solved iteratively across data centers and clients. Further, the algorithm has robust performance guarantee in terms of the variance of the prediction error. We demonstrate its convergence and robustness by both proofs using theoretical analysis and validation based on trace-driven simulations. The results further show that the proposed algorithm converges very fast and achieves much better QoE-cost balance than existing approaches.

关键词： Cloud-based streaming dynamic streaming data centers energy cost quality of experience bandwidth allocation distributed algorithms

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Optimal Water-Power Flow-Problem: Formulation and distributed Optimal Solution

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2019年第1期6卷 37-47页

作者： Zamzam, Ahmed S. Dall'Anese, Emiliano Zhao, Changhong Taylor, Josh A. Sidiropoulos, Nicholas D. Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Natl Renewable Energy Lab Golden CO 80401 USA Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22904 USA

This paper formalizes an optimal water-power flow (OWPF) problem to optimize the use of controllable assets across power and water systems while accounting for the couplings between the two infrastructures. Tanks and pumps are optimally managed to satisfy water demand while improving power grid operations;for the power network, an ac optimal power-flow formulation is augmented to accommodate the controllability of water pumps. Unfortunately, the physics governing the operation of the two infrastructures and coupling constraints leads to a nonconvex (and, in fact, NP-hard) problem;however, after reformulating OWPF as a nonconvex, quadratically constrained quadratic problem, a feasible point pursuit-successive convex approximation approach is used to identify feasible and optimal solutions. In addition, a distributed solver based on the alternating direction method of multipliers enables water and power operators to pursue individual objectives while respecting the couplings between the two networks. The merits of the proposed approach are demonstrated for the case of a distribution feeder coupled with a municipal water distribution network.

关键词： distributed algorithms optimal power flow optimal water flow power systems successive convex approximation (SCA) water systems

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Hierarchical distributed Model Predictive Control of Interconnected Microgrids

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2019年第1期10卷 407-416页

作者： Hans, C. A. Braun, P. Raisch, J. Gruene, L. Reincke-Collon, C. Tech Univ Berlin Control Syst Grp D-10623 Berlin Germany Univ Bayreuth Math Inst D-95447 Bayreuth Germany Univ Newcastle Sch Elect Engn & Comp Callaghan NSW 2308 Australia Max Planck Inst Dynam Komplexer Tech Syst Control Theory Grp D-39106 Magdeburg Germany Younicos AG D-12489 Berlin Germany

In this work, we propose a hierarchical distributed model predictive control strategy to operate interconnected microgrids (MGs) with the goal of increasing the overall infeed of renewable energy sources. In particular, we investigate how renewable infeed of MGs can be increased by using a transmission network allowing the exchange of energy. To obtain an model predictive control scheme, which is scalable with respect to the number of MGs and preserves their independent structure, we make use of the alternating direction method of multipliers leading to local controllers communicating through a central entity. This entity is in charge of the power lines and ensures that the constraints on the transmission capacities are met. The results are illustrated in a numerical case study.

关键词： distributed algorithms interconnected systems microgrids optimal control power system management renewable energy sources

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distributed Balancing of Commodity Networks Under Flow Interval Constraints

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2019年第1期64卷 51-65页

作者： Hadjicostis, Christoforos N. Dominguez-Garcia, Alejandro D. Univ Cyprus Dept Elect & Comp Engn CY-1678 Nicosia Cyprus Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

We consider networks the nodes of which are interconnected via directed edges, each able to admit a flow (or weight) within a certain interval, with nonnegative end points that correspond to lower and upper flow limits. This paper proposes and analyzes a distributed algorithm for obtaining admissible and balanced flows, i. e., flows that are within the given intervals at each edge and are balanced (the total in-flow equals the total out-flow) at each node. The algorithm can also be viewed as a distributed method for obtaining a set of weights that balance a weighted digraph for the case when there are lower and upper limit constraints on the edge weights. The proposed iterative algorithm assumes that communication among pairs of nodes that are interconnected is bidirectional (i. e., the communication topology is captured by the undirected graph that corresponds to the network digraph), and allows the nodes to asymptotically (with geometric rate) reach a set of balanced feasible flows, as long as the (necessary and sufficient) circulation conditions on the given digraph, with the given flow/weight interval constraints on each edge, are satisfied. We also provide a methodology that can be used by the nodes to asymptotically determine, in a distributedmanner, when the circulation conditions are not satisfied (thus, making the problem infeasible). Finally, we provide several examples and simulation studies to highlight the role of various parameters involved in the proposed distributed algorithm.

关键词： Balanced flow digraphs distributed algorithms flow balancing flow interval constraints

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On the Computability of the Ninth Dedekind Number

On the Computability of the Ninth Dedekind Number

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： William Fang Po Hao Chen Department of Computer Science Boston University Boston MA

The Dedekind numbers are an integer sequence that describes the number of n-variable monotone Boolean functions. It is reducible to counting other distinguished mathematical notions: antichains of subsets of n-set and simplicial complexes on an n-vertex set. The terms for $0 \le n\le 8$ are known and $n=9$ has been an open problem for over three decades. In this paper, we discuss the computability of the ninth Dedekind number with recent results involving partially ordered sets by Berman and Kohler [1] and combinatorial approaches with other relevant sequences

关键词： Boolean functions distributed algorithms

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Time-Varying Graph Learning Under Structured Temporal Priors

Time-Varying Graph Learning Under Structured Temporal Priors

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European Signal Processing Conference (EUSIPCO)

作者： Xiang Zhang Qiao Wang School of Information Science and Engineering Southeast University Nanjing China

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

This paper endeavors to learn time-varying graphs by using structured temporal priors that assume underlying relations between arbitrary two graphs in the graph sequence. Different from many existing methods that only describe vari-ations between two consecutive graphs, we propose a structure named temporal graph to characterize the underlying real temporal relations. Under this framework, classic priors like temporal homogeneity are actually special cases of our temporal graph. To address computational issue, we further develop a distributed algorithm based on Alternating Direction Method of Multipliers (ADMM) to solve the induced optimization problem. Numerical experiments on synthetic and real data demonstrate the superiorities of our method.

关键词： Europe Signal processing Convex functions distributed algorithms Optimization

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distributed Demand Peak Reduction With Non-Cooperative Players and Minimal Communication

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IEEE TRANSACTIONS ON SMART GRID 2019年第1期10卷 153-162页

作者： Collins, Lyle D. Middleton, Richard H. Univ Newcastle Callaghan NSW 2308 Australia CSIRO Energy Ctr Mayfield West NSW 2304 Australia

Demand response technology offers the exciting potential to reduce peak energy demand, electricity infrastructure expenditure, and household electricity bills. In this paper, a pricing mechanism that relies on non-cooperative heterogeneous loads knowledgeable of future energy consumption-such as electric vehicles-transferring minimal amounts of information to achieve peak demand response in a distributed fashion, whilst maintaining the privacy of the players. The existence of a Nash equilibrium is proven, as well as convergence conditions proving uniqueness of a Nash equilibrium and the stability of an "Iterated Synchronous Best Response Algorithm." The price of anarchy (PoA) is proven to approach 1 as the number of homogeneous players approaches infinity, indicating there is no advantage to cooperation for a large number of similar players. Finally, simulation results are presented which suggest that the PoA for a system with heterogeneous players is likely to be proportional to the spread of energy consumption constraints.

关键词： Demand-side management distributed algorithms energy consumption load management power system control power system management smart grids transactive energy

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A hierarchical, market-based, non-cooperative gametheoretic approach to projecting flexible demand-side resources: Towards more realistic demand response-integrated, long- term energy planning models 17

A hierarchical, market-based, non-cooperative gametheoretic ...

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17th International Conference on the European Energy Market (EEM)

作者： Mohseni, Soheil Brent, Alan C. Kelly, Scott Victoria Univ Wellington Fac Engn Sch Engn & Comp Sci Sustainable Energy Syst Wellington 6140 New Zealand Univ Technol Sydney Inst Sustainable Futures Sydney NSW 2007 Australia

ISBN: (纸本)9781728169194

This paper presents a non- cooperative, zero-sum, symmetric game approach to projecting the flexible demand-side resources to improve the reliability and robustness of long-term strategic energy planning models. A specifically devised distributed algorithm is put forward to determine the unique, pure-strategy Nash equilibrium of a non-cooperative game formulated for the delivery of aggregator-mediated demand response resources, at which no player can yield a higher payoff by deviating from its best-response strategy. The simulation for the operation of renewable energy systems is run over the course of a year. Furthermore, using a metaheuristic-based solution algorithm proposed for the optimal capacity planning of microgrids, the model was satisfactorily applied to the energy infrastructure planning of a test-case system conceptualized for the town of Ohakune, in New Zealand. The simulation results suggest that not only does the proposed market-directed, incentive-based, strategic demand- side management planning framework help elicit further contributions from the customers, which, in turn, reduces the reserve capacity requirements to meet the peak demand, it also guarantees the highest possible payoff for all players of the game. According to the simulation results, the test-case system's life-cycle cost can be reduced by up to similar to 29% compared to the case, where no demand response is considered.

关键词： Demand-side management distributed algorithms Load management Strategic planning Transactive energy

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ROME: All Overlays Lead to Aggregation, but Some Are Faster than Others

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ACM TRANSACTIONS ON COMPUTER SYSTEMS 2021年第1-4期39卷 4-4页

作者： Bloecher, Marcel Coppa, Emilio Kleber, Pascal Eugster, Patrick Culhane, William Ardekani, Masoud Saeida Tech Univ Darmstadt Hochschulstr 10 D-64289 Darmstadt Germany Sapienza Univ Rome Via Ariosto 25 Rome Italy Univ Svizzera Italiana Via La Santa 1 CH-6962 Lugano Switzerland Purdue Univ 305 N Univ St W Lafayette IN 47907 USA Imperial Coll London South Kensington Campus London SW7 2AZ England

Aggregation is common in data analytics and crucial to distilling information from large datasets, but current data analytics frameworks do not fully exploit the potential for optimization in such phases. The lack of optimization is particularly notable in current "online" approaches that store data in main memory across nodes, shifting the bottleneck away from disk I/O toward network and compute resources, thus increasing the relative performance impact of distributed aggregation phases. We present ROME, an aggregation system for use within data analytics frameworks or in isolation. ROME uses a set of novel heuristics based primarily on basic knowledge of aggregation functions combined with deployment constraints to efficiently aggregate results from computations performed on individual data subsets across nodes (e.g., merging sorted lists resulting from top-k). The user can either provide minimal information that allows our heuristics to be applied directly, or ROME can autodetect the relevant information at little cost. We integrated ROME as a subsystem into the Spark and Flink data analytics frameworks. We use real-world data to experimentally demonstrate speedups up to 3x over single-level aggregation overlays, up to 21% over other multi-level overlays, and 50% for iterative algorithms like gradient descent at 100 iterations.

关键词： Big data aggregation overlay distributed algorithms

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A survey on active noise control in the past decade-Part I: Linear systems

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SIGNAL PROCESSING 2021年 183卷 108039-108039页

作者： Lu, Lu Yin, Kai-Li de Lamare, Rodrigo C. Zheng, Zongsheng Yu, Yi Yang, Xiaomin Chen, Badong Sichuan Univ Sch Elect & Informat Engn Chengdu 610065 Sichuan Peoples R China Sichuan Univ Sch Comp Sci Chengdu 610065 Sichuan Peoples R China Pontificia Univ Catolica Rio de Janeiro CETUC BR-22451900 Rio de Janeiro Brazil Sichuan Univ Sch Elect Engn Chengdu 610065 Peoples R China Southwest Univ Sci & Technol Sch Informat Engn Robot Technol Used Special Environm Key Lab Sichu Mianyang 621010 Sichuan Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China

Active noise control (ANC) is an effective way for reducing the noise level in electroacoustic or electromechanical systems. Since its first introduction in 1936, this approach has been greatly developed. This paper focuses on discussing the development of ANC techniques over the past decade. Linear ANC algorithms, including the celebrated filtered-x least-mean-square (FxLMS)-based algorithms and distributed ANC algorithms, are investigated and evaluated. Nonlinear ANC (NLANC) techniques, such as functional link artificial neural network (FLANN)-based algorithms, are pursued in Part II. Furthermore, some novel methods and applications of ANC emerging in the past decade are summarized. Finally, future research challenges regarding the ANC technique are discussed. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Active noise control Adaptive filtering FxLMS-based algorithms distributed algorithms

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