The AC optimal power flow problem is known to be highly non-convex and scale very poorly with respect to the number of lines and buses. One possible solution is to subdivide and parallelize the problem through distrib...
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ISBN:
(纸本)9781728171005
The AC optimal power flow problem is known to be highly non-convex and scale very poorly with respect to the number of lines and buses. One possible solution is to subdivide and parallelize the problem through distributed optimization. However, the question of how to optimally partition a power grid for use in distributed optimization remains open in the literature. To this end, we compare the graph partitioners KaFFPa and METIS as well as two spectral clustering methods to the standard IEEE 9, 14, 30, 39, 57, 118, 300 bus models. The distributed algorithm ALADIN is used to solve the partitioned OPF problems. For larger grids, KaFFPa yields the best results on average.
We describe a Big Data-practical, SQL-implementable algorithm for efficiently determining connected components for graph data stored in a Massively Parallel Processing (MPP) relational database. The algorithm describe...
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ISBN:
(纸本)9781728129037
We describe a Big Data-practical, SQL-implementable algorithm for efficiently determining connected components for graph data stored in a Massively Parallel Processing (MPP) relational database. The algorithm described is a linear-space, randomised algorithm, always terminating with the correct answer but subject to a stochastic running time, such that for any epsilon > 0 and any input graph G = < V, E > the algorithm terminates after O(log vertical bar V vertical bar) SQL queries with probability of at least 1 - epsilon, which we show empirically to translate to a quasi-linear runtime in practice.
Pattern formation is one of the most fundamental problems in distributed computing, which has recently received much attention. In this paper, we initiate the study of distributed pattern formation in situations when ...
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ISBN:
(数字)9783030624019
ISBN:
(纸本)9783030624002;9783030624019
Pattern formation is one of the most fundamental problems in distributed computing, which has recently received much attention. In this paper, we initiate the study of distributed pattern formation in situations when some robots can be faulty. In particular, we consider the well-established look-compute-move model with oblivious, anonymous robots. We first present lower bounds and show that any deterministic algorithm takes at least two rounds to form simple patterns in the presence of faulty robots. We then present distributed algorithms for our problem which match this bound, for conic sections: in at most two rounds, robots form lines, circles and parabola tolerating f = 2, 3 and 4 faults, respectively. For f = 5, the target patterns are parabola, hyperbola and ellipse. We show that the resulting pattern includes the f faulty robots in the pattern of n robots, where n >= 2f + 1, and that f < n < 2f + 1 robots cannot form such patterns. We conclude by discussing several relaxations and extensions.
State machine replication (SMR) is a established approach to building fault-tolerant services. In search for high SMR throughput, approaches that exploit semantic information in the ordering and execution of commands ...
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ISBN:
(纸本)9781728176260
State machine replication (SMR) is a established approach to building fault-tolerant services. In search for high SMR throughput, approaches that exploit semantic information in the ordering and execution of commands have emerged. Generalized consensus and parallel state machine replication are two representative examples, respectively. Although both approaches have been proved effective in isolation, no study in the literature has considered their integration. In this paper, we investigate the integration of generalized consensus and parallel SMR. We derive algorithms to parallelize the execution of commands based on the ordering of commands provided by consensus. As a prototype, we extended Egalitarian Paxos and conducted many experiments varying conflict rates, command computational costs, and number of cores at replicas. Compared to Egalitarian Paxos, the integrated approach (a) results in important throughput gains, as command independency and computational cost increase, and (b) converges to the same performance with high conflict rates or reduced number of cores.
Graphs are commonly used to model the relationships between various entities. These graphs can be enormously large and thus, scalable graph analysis has been the subject of many research efforts. To enable scalable an...
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ISBN:
(纸本)9781728174457
Graphs are commonly used to model the relationships between various entities. These graphs can be enormously large and thus, scalable graph analysis has been the subject of many research efforts. To enable scalable analytics, many researchers have focused on generating realistic graphs that support controlled experiments for understanding how algorithms perform under changing graph features. Significant progress has been made on scalable graph generation which preserve some important graph properties (e.g., degree distribution, clustering coefficients). In this paper, we study how to sample a graph from the space of graphs with a given shell distribution. Shell distribution is related to the k-core, which is the largest subgraph where each vertex is connected to at least k other vertices. A k-shell is the subset of vertices that are in k-core but not (k + 1)-core, and the shell distribution comprises the sizes of these shells. Core decompositions are widely used to extract information from graphs and to assist other computations. We present a scalable shared and distributed memory graph generator that, given a shell decomposition, generates a random graph that conforms to it. Our extensive experimental results show the efficiency and scalability of our methods. Our algorithm generates 2(33) vertices and 2(37) edges in less than 50 seconds on 384 cores.(1)
We improve the time complexity of the single-source shortest path problem for weighted directed graphs (with non-negative integer weights) in the Broadcast CONGEST model of distributed computing. For polynomially boun...
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ISBN:
(纸本)9781450375825
We improve the time complexity of the single-source shortest path problem for weighted directed graphs (with non-negative integer weights) in the Broadcast CONGEST model of distributed computing. For polynomially bounded edge weights, the state-of-the-art algorithm for this problem requires (O) over tilde (min root nD(1/2), root nD(1/4) +n(3/5) + D}) rounds [Forster and Nanongkai, FOCS 2018], which is quite far from the known lower bound of (O) over tilde(root n + D) rounds [Elkin, STOC 2014];here D is the diameter of the underlying network and n is the number of vertices in it. For the approximate version of this problem, Forster and Nanongkai [FOCS 2018] obtained an upper bound of (O) over tilde (root nD(1/4) + D), and stated that achieving the same bound for the exact case remains a major open problem. In this paper we resolve the above mentioned problem by devising a new randomized algorithm for solving (the exact version of) this problem in (O) over tilde(root nD(1/4) + D) rounds. Our algorithm is based on a novel weight-modifying technique that allows us to compute bounded-hop distance approximation that preserves a certain form of the triangle inequality for the edges in the graph.
We consider network aggregative games where each player minimizes a cost function that depends on its own strategy and on a convex combination of the strategies of its neighbors. As a first contribution, we propose a ...
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We consider network aggregative games where each player minimizes a cost function that depends on its own strategy and on a convex combination of the strategies of its neighbors. As a first contribution, we propose a class of distributed algorithms that can be used to steer the strategies of the rational agents to a Nash equilibrium configuration, with guaranteed convergence under different sufficient conditions depending on the cost functions and on the network. A distinctive feature of the proposed class of algorithms is that agents use optimal responses instead of gradient type of strategy updates. As a second contribution, we show that the algorithm suggested for network aggregative games can also be used to recover a Nash equilibrium of average aggregative games (i.e., games where each agent is affected by the average of the strategies of the whole population) in a distributed fashion, that is, without requiring a central coordinator. We apply our theoretical results to multi-dimensional, convex-constrained opinion dynamics and to demand-response schemes for energy management. (C) 2020 Elsevier Ltd. All rights reserved.
We consider the gathering task by a team of m synchronous mobile robots in a graph of n nodes. Each robot has an identifier (ID) and runs its own deterministic algorithm, i.e., there is no centralized coordinator. We ...
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ISBN:
(数字)9783030624019
ISBN:
(纸本)9783030624002;9783030624019
We consider the gathering task by a team of m synchronous mobile robots in a graph of n nodes. Each robot has an identifier (ID) and runs its own deterministic algorithm, i.e., there is no centralized coordinator. We consider a particularly challenging scenario: there are f Byzantine robots in the team that can behave arbitrarily, and even have the ability to change their IDs to any value at any time. There is no way to distinguish these robots from non-faulty robots, other than perhaps observing strange or unexpected behaviour. The goal of the gathering task is to eventually have all non-faulty robots located at the same node in the same round. It is known that no algorithm can solve this task unless there at least f + 1 non-faulty robots in the team. In this paper, we design an algorithm that runs in polynomial time with respect to n and m that matches this bound, i.e., it works in a team that has exactly f + 1 non-faulty robots. In our model, we have equipped the robots with sensors that enable each robot to see the subgraph (including robots) within some distance H of its current node. We prove that the gathering task is solvable if this visibility range H is at least the radius of the graph, and not solvable if H is any fixed constant.
Shah, Shin and Tetali [25] initiated the study of queuing systems on the medium access channel with arbitrary interference graph. The graph models dependencies between the channel members - any two connected by an edg...
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ISBN:
(纸本)9781450369350
Shah, Shin and Tetali [25] initiated the study of queuing systems on the medium access channel with arbitrary interference graph. The graph models dependencies between the channel members - any two connected by an edge are dependent. This problem could be also re-stated as a query system with dependent elements or local scheduling of dependent packets/jobs. In short, if two dependent units, also called stations, want to transmit (or be in the same query, or do jobs in parallel), there is a conflict and none of them succeeds. The problem is particularly challenging, if the units need to make their decisions locally, without any coordination. Prior the paper by Shah, Shin and Tetali [FOCS 2011], the main focus was on the clique graph - even in this simple topology, many problems related to stability remain open. While the solution by Shah, Shin and Tetali [FOCS 2011] is semi-local, as nodes make use of information about interfering neighbors in the graph, we provide the first purely local stable algorithms. In particular, in our algorithms stations make their decision whether to transmit or not based only on looking at their local queues. Based only on this feature, and without using a priori knowledge of topology, we design an algorithm that allows all stations for implicit transferring information to and from all other stations. We use it as a tool for developing universally stable algorithms for the problem of queuing messages - i.e., guaranteeing bounded queues when, on average, less than one independent set is injected per round. The first one is stable in adversarial (worst-case) sense, the second one - in stochastic sense (average-case). We also prove optimality of our algorithm in adversarial sense: no algorithm can be stable against injections with rate rho = 1 (when, on average, one independent set is injected per round). Moreover, for the class of non-adaptive protocols, we show that it is not possible to achieve stability for any injection rate omega (1/lo
This paper presents a distributed optimal power flow (OPF) algorithm for the system-level control of multi-terminal DC (MTDC) distribution grids with distributed energy resources (DER). At each control period, the alg...
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This paper presents a distributed optimal power flow (OPF) algorithm for the system-level control of multi-terminal DC (MTDC) distribution grids with distributed energy resources (DER). At each control period, the algorithm updates the nominal voltage and power set-points of the DER-interfacing converters, which operate according to active network management (ANM) concepts. To achieve this, the OPF problem, in its nodal formulation, includes power dispatch strategies for diverse DER according to their technical characteristics, which change during the system operation. This multi-objective OPF-for-ANM problem is solved by distributed control units (DCUs) according to the distributed algorithm for the alternating direction method of multipliers (ADMM). All DCUs have identical roles in the distributed control structure and thus the distributed OPF-for-ANM algorithm is highly modular. Simulation results in different IEEE standard systems and various scenarios demonstrate that the algorithm is fast and scalable, irrespective of the number and location of integrated DER, as well as the operating condition of the system. The convergence speed of the algorithm is analysed considering the computation and communication time needed for its execution. The online application in a computers cluster demonstrates the fast execution of the developed algorithm in a physically-distributed implementation. Through the proposed OPF-for-ANM algorithm, the system-level control can dispatch fast diverse DER in different coordination approaches in a distributed manner.
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