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European Control Conference (ECC)

作者： Yi, Peng Pavel, Lacra Washington Univ Dept Elect & Syst Engn St Louis MO 63130 USA Univ Toronto Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9783952426982

In this paper, we propose an asynchronous distributed algorithm with delayed information for computing a generalized Nash equilibrium over multi-agent systems. We consider a game where all players' local decisions are coupled via a shared affine constraint. We assume each player can only access its local objective function, local constraint, and a local block matrix of the affine constraint. With the help of auxiliary edge variables and edge Laplacian matrix, each player can perform its local iteration in an asynchronous manner, using only local data and possibly delayed neighbour information, without any centralized clock coordination. Therefore, the algorithm fully exploits the local computation resource of each player, and reduces the idle time due to waiting for the "slowest" agent. The algorithm convergence is shown using asynchronous fixed-point iterations. Numerical studies verify its convergence.

关键词： Games distributed algorithms Convergence Couplings Linear programming Symmetric matrices Nash equilibrium

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Hierarchical distributed Voltage Regulation in Networked Autonomous Grids

Hierarchical Distributed Voltage Regulation in Networked Aut...

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American Control Conference

作者： Xinyang Zhou Zhiyuan Liu Wenbo Wang Changhong Zhao Fei Ding Lijun Chen National Renewable Energy Laboratory Golden CO 80401 USA College of Engineering and Applied Science University of Colorado Boulder CO 80309 USA New York University Brooklyn NY 11201 USA National Renewable Energy Laboratory Golden CO 80401

ISBN: (纸本)9781538679012;9781538679265

We propose a novel algorithm to solve optimal power flow (OPF) that aims at dispatching controllable distributed energy resources (DERs) for voltage regulation at minimum cost. The proposed algorithm features unprecedented scalability to large distribution networks by utilizing an information structure based on networked autonomous grids (AGs). Specifically, each AG is a subtree of a large distribution network that has a tree topology. The topology and line parameters of each AG are known only to a regional coordinator (RC) that is responsible for communicating with and dispatching the DERs within this AG. The reduced network, where each AG is treated as a node, is managed by a central coordinator (CC), which knows the topology and line parameters of the reduced network only and communicates with all the RCs. We jointly explore this information structure and the structure of the linearized distribution power flow (LinDistFlow) model to derive a hierarchical, distributed implementation of the primal-dual gradient algorithm that solves the OPF. The proposed implementation significantly reduces the computation burden compared to the centrally coordinated implementation of the primal-dual algorithm. Numerical results on a 4,521-node test feeder show that the proposed hierarchical distributed algorithm can achieve an improvement of more than tenfold in the speed of convergence compared to the centrally coordinated primal-dual algorithm.

关键词： distributed algorithms distributed control distributed power generation gradient methods load flow optimisation power distribution control trees (mathematics) voltage control

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Adaptive Fog Configuration for the Industrial Internet of Things

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2018年第10期14卷 4656-4664页

作者： Chen, Lixing Zhou, Pan Gao, Liang Xu, Jie Univ Miami Dept Elect & Comp Engn Coral Gables FL 33146 USA Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Dept Ind & Mfg Syst Engn Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China

Industrial fog computing deploys various industrial services, such as automatic monitoring/control and imminent failure detection, at the fog nodes (FNs) to improve the performance of industrial systems. Much effort has been made in the literature on the design of fog network architecture and computation offloading. This paper studies an equally important but much less investigated problem of service hosting where FNs are adaptively configured to host services for sensor nodes (SNs), thereby enabling corresponding tasks to be executed by the FNs. The problem of service hosting emerges because of the limited computational and storage resources at FNs, which limit the number of different types of services that can be hosted by an FN at the same time. Considering the variability of service demand in both temporal and spatial dimensions, when, where, and which services to host have to be judiciously decided to maximize the utility of the fog computing network. Our proposed fog configuration strategies are tailored to battery-powered FNs. The limited battery capacity of FNs creates a long-term energy budget constraint that significantly complicates the fog configuration problem as it introduces temporal coupling of decision making across the timeline. To address all these challenges, we propose an online distributed algorithm, called adaptive fog configuration (AFC), based on Lyapunov optimization and parallel Gibbs sampling. AFC jointly optimizes service hosting and task admission decisions, requiring only currently available system information while guaranteeing close-to-optimal performance compared to an oracle algorithm with full future information.

关键词： distributed algorithms energy efficiency fog computing industrial internet of things (IIoT)

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distributed memory building blocks for massive biological sequence analysis

Distributed memory building blocks for massive biological se...

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作者： Pan, Tony C. Georgia Institute of Technology

学位级别：博士

K-mer indices and de Bruijn graphs are important data structures in bioinformatics with multiple applications ranging from foundational tasks such as error correction, alignment, and genome assembly, to knowledge discovery tasks including repeat detection and SNP identification. While advances in next generation sequencing technologies have dramatically reduced the cost and improved latency and throughput, few bioinformatics tools can efficiently process the data sets at the current generation rate of 1.8 terabases every 3 days. The volume and velocity with which sequencing data is generated necessitate efficient algorithms and implementation of k-mer indices and de Bruijn graphs, two central components in bioinformatic applications. Existing applications that utilize k-mer counting and de Bruijn graphs, however, tend to provide embedded, specialized implementations. The research presented here represents efforts toward the creation of the first reusable, flexible, and extensible distributed memory parallel libraries for k-mer indexing and de Bruijn graphs. These libraries are intended for simplifying the development of bioinformatics applications for distributed memory environments. For each library, our goals are to create a set of API that are simple to use, and provide optimized implementations based on efficient parallel algorithms. We designed algorithms that minimize communication volume and latency, and developed implementations with better cache utilization and SIMD vectorization. We developed Kmerind, a k-mer counting and indexing library based on distributed memory hash table and distributed sorted arrays, that provide efficient insert, find, count, and erase operations. For de Bruijn graphs, we developed Bruno by leveraging Kmerind functionalities to support parallel de Bruijn graph construction, chain compaction, error removal, and graph traversal and element query. Our performance evaluations showed that Kmerind is scalable and high performance. Kmerin

关键词： High performance computing Bioinformatics K-mer index K-mer counting De bruijn graph Next generation sequencing Parallel algorithms distributed memory distributed algorithms SIMD vectorization Cache friendly algorithms MPI

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Fast and Efficient distributed Computation of Hamiltonian Cycles in Random Graphs 38

Fast and Efficient Distributed Computation of Hamiltonian Cy...

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38th IEEE International Conference on distributed Computing Systems (ICDCS)

作者： Chatterjee, Soumyottam Fathi, Reza Pandurangan, Gopal Nguyen Dinh Pham Univ Houston Dept Comp Sci Houston TX 77204 USA

ISBN: (纸本)9781538668719

We present fast and efficient randomized distributed algorithms to find Hamiltonian cycles in random graphs. In particular, we present a randomized distributed algorithm for the G(n,p) random graph model, with number of nodes n and p = cln n/n(delta) (for any constant 0 < delta <= 1 and for a suitably large constant c > 0), that finds a Hamiltonian cycle with high probability in O(n(delta)) rounds.(1) Our algorithm works in the (synchronous) CONGEST model (i.e., only 0(log n) sized messages are communicated per edge per round) and its computational cost per node is sublinear (in n) per round and is fully-distributed (each node uses only o(n) memory and all nodes' computations are essentially balanced). Our algorithm improves over the previous best known result in terms of both the running time as well as the edge sparsity of the graphs where it can succeed;in particular, the denser the random graph, the smaller is the running time.

关键词： Computational modeling distributed algorithms distributed computing Memory management Parallel algorithms Bandwidth Upper bound

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distributed collaborative control strategy for integrated energy microgrid based on multi-agent system

Distributed collaborative control strategy for integrated en...

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International Conference on Electrical Machines and Systems (ICEMS)

作者： Changbin Hu Shiwei Li Shanna Luo Rui Ma Hui Fan College of Electrical and Control Engineering North China University of Technology Beijing China State Grid Hebei Electric Power Company Shijiazhuang China

ISBN: (纸本)9781728133997

Economic dispatch problem (EDP) in integrated energy microgrid is a basic optimization problem of power system. With the abundance of various forms of energy in the micro grid, this paper proposes a distributed cooperative algorithm based on the consistency framework to solve the EDP of the comprehensive energy economy model. This method makes use of the principle that under the optimal output power value, all micro sources can achieve the economic optimum with the same rate of cost increment. Therefore, the cost slight increase rate of each micro source is selected as the consistency variable, and the local power supply and demand mismatch value is taken as the feedback variable to meet the constraint conditions. Using multi-parameter perturbation theory and graph theory, the convergence of the algorithm is proved. The algorithm is a completely distributed algorithm with no leader or virtual command node. Simulation results show the effectiveness of this algorithm. At the same time, the simulation results directly illustrate the variation law of convergence speed.

关键词： distributed algorithm Multi-agent system Integrated energy Microgrid Consistency Multi-agent systems MICRO-GRID distributed algorithms consistency Consistency (Measurement) constraint condition algorithms Electronic Data Processing control strategy Energy sources

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Simplifying Hands-On Teaching of distributed algorithms with SPLAY

Simplifying Hands-On Teaching of Distributed Algorithms with...

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26th IEEE International Parallel and distributed Processing Symposium (IPDPS) / Workshop on High Performance Data Intensive Computing

作者： Riviere, Etienne Univ Neuchatel Inst Informat CH-2000 Neuchatel Switzerland

ISBN: (纸本)9780769546766

Teaching distributed algorithms using a learning-by-doing approach is usually associated with a slow and cumbersome learning process for students. In order to test and evaluate even simple protocols, students need to learn how to set up and operate a testbed, and to write scripts for deploying, running their program and finally retrieving some logs for parsing and observation of the results. Moreover, the amount of code required in languages such as C# or Java for implementing even a simple protocol is often one order of magnitude more than the length of the protocol pseudo-code description as discussed in class or in research papers. Nevertheless, teaching distributed algorithms and protocols using real deployed code on real conditions is highly desirable and can not always be replaced by the use of simulators. We present in this paper our experience of using SPLAY, a distributed systems evaluation framework that greatly simplifies the work of both instructors and students for hands-on learning of distributed systems. SPLAY simplifies the writing, deployment and observation of distributed algorithms and the management of test environments, narrowing the complexity gap between pseudo-code descriptions and executable implementations. In addition, SPLAY's features and the focus kept on the algorithms and their evaluation, allow students to evaluate their protocols in a variety of conditions, by controlling the experiments and their running conditions, or by allowing running them on multiple testbeds at no additional costs and with minimal administration complexity.

关键词： distributed systems distributed algorithms education deployment evaluation learning-by-doing

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Asynchronous distributed Method of Multipliers for Constrained Nonconvex Optimization 16

Asynchronous Distributed Method of Multipliers for Constrain...

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European Control Conference (ECC)

作者： Farina, Francesco Garulli, Andrea Giannitrapani, Antonio Notarstefano, Giuseppe Univ Siena Dipartimento Ingn Informaz & Sci Matemat Siena Italy Univ Salento Dept Engn Lecce Italy

ISBN: (纸本)9783952426982

This paper addresses a class of constrained optimization problems over networks in which local cost functions and constraints can be nonconvex. We propose an asynchronous distributed optimization algorithm, relying on the centralized Method of Multipliers, in which each node wakes up in an uncoordinated fashion and performs either a descent step on a local Augmented Lagrangian or an ascent step on the local multiplier vector. These two phases are regulated by a distributed logic-AND, which allows nodes to understand when the descent on the (whole) Augmented Lagrangian is sufficiently small. We show that this distributed algorithm is equivalent to a block coordinate descent algorithm for the minimization of the Augmented Lagrangian followed by an update of the whole multiplier vector. Thus, the proposed algorithm inherits the convergence properties of the Method of Multipliers.

关键词： Optimization Peer-to-peer computing distributed algorithms Minimization Nickel Silicon Europe

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Load-Optimal Local Fast Rerouting for Dense Networks

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IEEE-ACM TRANSACTIONS ON NETWORKING 2018年第6期26卷 2583-2597页

作者： Borokhovich, Michael Pignolet, Yvonne-Anne Schmid, Stefan Tredan, Gilles AT&T Labs Florham Pk NJ 07932 USA ABB Corp Res CH-8037 Baden Switzerland Univ Vienna Fac Comp Sci A-1010 Vienna Austria CNRS Lab Architecture & Anal Syst F-1000 Toulouse France

Reliable and highly available computer networks must implement resilient fast rerouting mechanisms: upon a link or node failure, an alternative route is determined quickly, without involving the network control plane. Designing such fast failover mechanisms capable of dealing with multiple concurrent failures, however, is challenging, as failover rules need to be installed proactively, i.e., ahead of time, without knowledge of the actual failures happening at runtime. Indeed, only little is known today about the design of resilient routing algorithms. This paper introduces a general framework to reason about and design local failover algorithms that minimize the resulting load after failover on dense networks, beyond destination-based routing. We show that due to the inherent locality of the failover decisions at runtime, the problem is fundamentally related to the field of distributed algorithms without coordination. We derive an intriguing lower bound on the inherent network load overhead any local fast failover scheme that will introduce in the worst case, even though globally seen, much more balanced traffic allocations exist. We then present different randomized and deterministic failover algorithms and analyze their overhead load. In particular, we build upon the theory of combinatorial designs and develop a novel deterministic failover mechanism based on symmetric block design theory, which tolerates a maximal number of link failures while ensuring low loads.

关键词： Computer network reliability distributed algorithms

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COLLABORATIVE CACHING AND MATCHING FOR D2D CONTENT SHARING

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IEEE WIRELESS COMMUNICATIONS 2018年第3期25卷 43-49页

作者： Wu, Dan Zhou, Liang Cai, Yueming Qian, Yi Army Engn Univ Zhengzhou Henan Peoples R China Nanjing Univ Posts & Telecommun Nanjing Jiangsu Peoples R China Univ Nebraska Dept Elect & Comp Engn Lincoln NE USA

D2D content sharing is recognized as an effective response to the prevalence of multimedia and local services, especially for 5G edge networks. We construct a content sharing framework based on cache-assisted D2D communications, which consists of the content placement and delivery phases. When considering the random content location and the limited storage and transmission capability of devices, which content should be cached and how to optimally match potential providers to requesters of contents are of importance. As such, we extend the concept of cacheability, and accordingly propose a distributed collaborative cache management scheme. It involves a caching decision and update model based on the submodularity over matroid constraints, and a best effort distributed algorithm framework. Moreover, we extend the sharing mode category, and propose the sharing mode selection scheme by constructing a general social welfare maximization with efficient incentive mechanisms. By leveraging the total unimodularity, it can be simplified to its linear relaxation without loss of optimization.

关键词： Device-to-device communication Collaboration Receivers Interference 5G mobile communication Internet distributed algorithms

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