In recent years, with the increasingly widespread application of digital technology in various enterprises, the role of databases in various enterprises will also become increasingly important. A lot of useful and cri...
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In recent years, with the increasingly widespread application of digital technology in various enterprises, the role of databases in various enterprises will also become increasingly important. A lot of useful and critical information is often hidden in a large amount of information stored in the database system. How to obtain this information efficiently and accurately has always been a problem, so data mining technology came into being. This paper aims to study the application of association rule algorithm in distributed NewSQL database design. On the basis of analyzing the classical association rule algorithm, the advantages of NewSQL database and the processing process of SQL, the traditional association rule algorithm is improved and a distributed algorithm is designed. NewSQL database, in order to improve the performance of the algorithm for testing, this paper uses multiple computers to form a local area network to simulate the NewSQL database system. The test results show that when the minimum support is low, the execution efficiency of the improved association rule algorithm is slightly better than that of the traditional association rule algorithm
Chatterjee, Gmyr, and Pandurangan [PODC 2020] recently introduced the notion of awake complexity for distributed algorithms, which measures the number of rounds in which a node is awake. In the other rounds, the node ...
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ISBN:
(纸本)9781450391467
Chatterjee, Gmyr, and Pandurangan [PODC 2020] recently introduced the notion of awake complexity for distributed algorithms, which measures the number of rounds in which a node is awake. In the other rounds, the node is sleeping and performs no computation or communication. Measuring the number of awake rounds can be of significance in many settings of distributed computing, e.g., in sensor networks where energy consumption is of concern. In that paper, Chatterjee et al. provide an elegant randomized algorithm for the Maximal Independent Set (MIS) problem that achieves an O(1) node-averaged awake complexity. That is, the average awake time among the nodes is O(1) rounds. However, to achieve that, the algorithm sacrifices the more standard round complexity measure from the well-known O(log n) bound of MIS, due to Luby [STOC'85], to O(log(3.41)n) rounds. Our first contribution is to present a simple randomized distributed MIS algorithm that, with high probability, has O( 1) nodeaveraged awake complexity and O (log n) worst-case round complexity. Our second, and more technical contribution, is to show algorithms with the same O(1) node-averaged awake complexity and O(log n) worst-case round complexity for 1 +epsilon approximation of maximum matching and 2 +epsilon approximation of minimum vertex cover, where.. denotes an arbitrary small positive constant.
This paper considers standard T-interval dynamic networks, where the N nodes in the network proceed in lock-step rounds, and where the topology of the network can change arbitrarily from round to round, as determined ...
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ISBN:
(纸本)9781450391467
This paper considers standard T-interval dynamic networks, where the N nodes in the network proceed in lock-step rounds, and where the topology of the network can change arbitrarily from round to round, as determined by an adversary. The adversary promises that in every T consecutive rounds, the T (potentially different) topologies in those.. rounds contain a common connected subgraph that spans all nodes. Within such a context, we propose novel algorithms for solving some fundamental distributed computing problems such as Count/Consensus/Max. Our algorithms are the first algorithms whose complexities do not contain an Omega(N) term, under constant T values. Previous sublinear algorithms require significantly larger T values.
Among fundamental problems in the context of distributed computing by mobile robots, the Pattern Formation (PF) is certainly the most representative. Given a multi-set F of points in the Euclidean plane and a set R of...
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Among fundamental problems in the context of distributed computing by mobile robots, the Pattern Formation (PF) is certainly the most representative. Given a multi-set F of points in the Euclidean plane and a set R of robots such that vertical bar R vertical bar=vertical bar F vertical bar , PF asks for a distributed algorithm that moves robots so as to reach a configuration similar to F . Similarity means that robots must be disposed as F regardless of translations, rotations, reflections, uniform scalings. In the literature, PF has been approached by assuming asynchronous robots endowed with chirality, i.e. a common handedness. The proposed algorithm along with its correctness proof turned out to be flawed. In this paper, we propose a new algorithm on the basis of a recent methodology studied for approaching problems in the context of distributed computing by mobile robots. According to this methodology, the correctness proof results to be well-structured and less prone to faulty arguments. We then ultimately characterize PF when chirality is assumed.
A decentralized control algorithm for routing flow in complex networks is proposed. The aim is to fairly distribute the supply/demand among the nodes while maintaining maximum flow in the network and satisfying the fl...
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A decentralized control algorithm for routing flow in complex networks is proposed. The aim is to fairly distribute the supply/demand among the nodes while maintaining maximum flow in the network and satisfying the flow limits of the nodes and links. The algorithm is also used for flow re-routing after a failure in (or attack on) a network to avoid a cascading failure while maintaining the maximum flow in the network. The proposed algorithm is based only on the information about the closest neighbours of each node. Consensus rules are used to achieve fairness between the nodes in the network. The performance of the proposed algorithm is demonstrated by simulations.
Flexibilities offered by gas-fired power plants have strongly increased their usage in power systems. Increasing the penetration rate of gas-fired power plants has made it necessary to coordinate gas network expansion...
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Flexibilities offered by gas-fired power plants have strongly increased their usage in power systems. Increasing the penetration rate of gas-fired power plants has made it necessary to coordinate gas network expansion planning with electricity network expansion planning. In this paper, a decentralized model for expansion of gas and electricity networks is introduced that coordinates investment in these networks while taking into account their privacy. Alternating direction method of multipliers is used to decompose the centralized gas and electricity expansion problem into two separate expansion problems. The proposed method aims to minimize the investment and operation cost of interconnected gas and electricity networks during the planning period. The resultant problems are mixed-integer nonlinear programs, which are then linearized and transformed into mixed-integer linear programming models to facilitate the optimality conditions. distributed optimization provides a cyber-physical system in which gas and electricity networks are managed separately while a coordination link is provided through the cloud computing layer. However, coordinated operation through a cloud layer keeps the control-loop subject to cyber-attacks. In this paper, the standard form of the proposed decentralized model is modified to secure it against cyber-attacks. Finally, the effectiveness and applicability of the proposed method are tested and verified over a real case study that has a high penetration level of gas consuming generation units. The proposed cyber secure model not only converges faster than a standard model (ie, computationally more efficient than conventional methods) but also keeps optimality of found solution in a secure fashion. The satisfactory performance of the proposed model is also demonstrated against centralized, leader-follower, and independent expansion planning models.
In this paper, a novel distributed algorithm is proposed to recover two-dimensional graph signals. This work exploits a Cartesian product graph as a graph model to present complex datasets. Furthermore, incorporating ...
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In this paper, a novel distributed algorithm is proposed to recover two-dimensional graph signals. This work exploits a Cartesian product graph as a graph model to present complex datasets. Furthermore, incorporating the piecewise smooth assumption, a LASSO optimization formulation is tailored to the recovery problem. To solve the optimization formulation in a distributed manner, the Cartesian product graph is decomposed into a family of overlapping subgraphs, and then a series of localized subproblems resided on the subgraphs are introduced. The subproblems are solved via an alternating direction method of multipliers (ADMM). Experiments on three real-world datasets demonstrate the excellent performance of the proposed algorithm.(c) 2022 Elsevier Inc. All rights reserved.
The Gathering task for k robots disposed on the n vertices of a graph G requires robots to move toward a common vertex from where they do not move anymore. When dealing with very weak robots in terms of capabilities, ...
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The Gathering task for k robots disposed on the n vertices of a graph G requires robots to move toward a common vertex from where they do not move anymore. When dealing with very weak robots in terms of capabilities, considering synchronous or asynchronous settings may heavily affect the feasibility of the problem. In fact, even though dealing with asynchronous robots in general requires more sophisticated strategies with respect to the synchronous counterpart, sometimes it comes out that asynchronous robots simply cannot solve the problem whereas synchronous robots can. We study general properties of graphs that can be exploited in order to accomplish the gathering task in the synchronous setting, obtaining an interesting and innovative sufficient condition for the feasibility of the gathering task in graphs, regardless the topology. Furthermore, we consider dense and symmetric graphs like complete and complete bipartite graphs where in general the topology does not allow to distinguish vertices where to finalize the gathering. In such topologies, we fully characterize the solvability of the gathering task in the synchronous setting by suitably combining some strategies arising from the general approach with specific techniques dictated by the considered topologies. From the lower bound point of view in terms of number of synchronous time units required to accomplish the gathering task, in general nothing better than Omega(D-G), with D-G being the diameter of the input graph G, can be provided. For both complete and complete bipartite graphs, we prove a lower bound of Omega(log(phi) k), with phi being the well-known golden ratio. Combined with the provided algorithms, this reveals to be asymptotically tight for complete graphs while for complete bipartite graphs an additive factor of delta(k) is achieved, with delta(k) being the function that returns the number of divisors of the integer k. (C) 2020 Elsevier B.V. All rights reserved.
By utilizing tools from the game theory, we develop a novel multiperiod-multicompany demand response framework considering the interactions between companies (sellers of energy) and their consumers (buyers of energy)....
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By utilizing tools from the game theory, we develop a novel multiperiod-multicompany demand response framework considering the interactions between companies (sellers of energy) and their consumers (buyers of energy). We model the interactions in terms of a Stackelberg game, where companies set their prices and consumers respond by choosing their demands. We show that the underlying game has a unique equilibrium at which the companies maximize their revenues, while the consumers maximize their utilities subject to their local constraints. Closed-form expressions are provided for the optimal strategies of all players. Based on these solutions, a power-allocation game has been formulated, which is shown to admit a unique pure-strategy Nash equilibrium, for which closed-form expressions are also provided. This equilibrium is found under the assumption that companies can freely allocate their power across the time horizon, but we also demonstrate that it is possible to relax this assumption. We further provide a fast distributed algorithm for the computation of all optimal strategies using only local information. We also study the effect of variations in the number of periods (subdivisions of the time horizon) and the number of consumers. As a consequence, we are able to find an appropriate company-to-consumer ratio when the number of consumers participating in demand response exceeds some threshold. Furthermore, we show, both analytically and numerically, that the multiperiod scheme provides incentives for energy consumers to participate in demand response compared with the single-period framework studied by Maharjan et al. In our framework, we provide a condition for the minimum budget consumers need and carry out case studies using real-life data to demonstrate the benefits of the approach, which show potential savings of up to 30% and equilibrium prices that have low volatility.
We present a distributed Nash equilibrium seeking method based on the Bregman forward-backward splitting, which allows us to have a mirror mapping instead of the standard projection as the backward operator. Our main ...
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We present a distributed Nash equilibrium seeking method based on the Bregman forward-backward splitting, which allows us to have a mirror mapping instead of the standard projection as the backward operator. Our main technical contribution is to show convergence to a Nash equilibrium when the game has cocoercive pseudogradient mapping. Furthermore, when the feasible sets of the agents are simplices, a suitable choice of a Legendre function results in an exponentiated pseudogradient method, which, in our numerical experience, performs out the standard projected pseudogradient and dual averaging meth-ods.(c) 2022 The Author(s). Published by Elsevier Ltd on behalf of European Control Association. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )
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