Motivated by ABR class of service in ATM networks, we study a continuous time queueing system with a feedback control of the arrival rate of some of the sources. The feedback about the queue length or the total worklo...
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Motivated by ABR class of service in ATM networks, we study a continuous time queueing system with a feedback control of the arrival rate of some of the sources. The feedback about the queue length or the total workload is provided at regular intervals (variations on it, especially the traffic management specification TM 4.0, are also considered). The propagation delays can be nonnegligible. For a general class of feedback algorithms, we obtain the stability of the system in the presence of one or more bottleneck nodes in the virtual circuit. Our system is general enough that it can be useful to study feedback control in other network protocols. We also obtain rates of convergence to the stationary distributions and finiteness of moments. For the single botterneck case, we provide algorithms to compute the stationary distributions and the moments of the sojourn times in different sets of states. We also show analytically (by showing continuity of stationary distributions and moments) that for small propagation delays, we can provide feedback algorithms which have higher mean throughput, lower probability of overflow and lower delay jitter than any open loop policy. Finally these results are supplemented by some computational results.
We extend the applicability of the Exterior Ellipsoid Algorithm for approximating n-dimensional fixed points of directionally nonexpanding functions. Such functions model many practical problems that cannot be formula...
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We extend the applicability of the Exterior Ellipsoid Algorithm for approximating n-dimensional fixed points of directionally nonexpanding functions. Such functions model many practical problems that cannot be formulated in the smaller class of globally nonexpanding functions. The upper bound 2n(2) ln(2/epsilon) on the number of function evaluations for finding epsilon-residual approximations to the fixed points remains the same for the larger class. We also present a modified version of a hybrid bisection-secant method for efficient approximation of univariate fixed point problems in combustion chemistry. (c) 2007 Elsevier Inc. All rights reserved.
Although the problem of data server placement in parallel and distributed systems has been studied extensively, most of the existing work assumes there is no competition between servers. Hence, their goal is to minimi...
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Although the problem of data server placement in parallel and distributed systems has been studied extensively, most of the existing work assumes there is no competition between servers. Hence, their goal is to minimize read, update and storage cost. In this paper, we study the server placement problem in which a new server has to compete with existing servers for user requests. Therefore, in addition to minimizing cost, we also need to maximize the benefit of building a new server. Our major results include three parts. First, for tree-structured systems, we propose an O(vertical bar V vertical bar(3)k) time dynamic programming algorithm to find the optimal placement of k extra servers that maximizes the benefit in a tree with vertical bar V vertical bar nodes. We also propose an O(vertical bar V vertical bar(3)) time dynamic programming algorithm to find the optimal placement of extra servers that maximizes the benefit, without any constraint on the number of extra servers. Second, for general connected graphs, we prove that the server placement problems are NP-complete, and present three greedy heuristic algorithms, called Greedy Add, Greedy Remove and Greedy Add-Remove, to solve them. Third, we show that if the number of requests a server can handle (i.e., server capacity) is bounded, the server placement problem is NP-complete even for tree networks. We then derive a variation of the same set of greedy heuristic algorithms, with consideration of server capacity constraint, to solve the problem. Our experiment results demonstrate that the greedy algorithms achieve good results, when compared with the upper bounds found by a linear programming algorithm. Greedy Add performs best in the unconstrained model, yielding a benefit within 12% difference from the theoretical upper bound in average. For the constrained model, Greedy Remove performs best for smaller network sizes, while Greedy Add-Remove performs best for larger network sizes. On average, the heuristic alg
In this paper linear time sequential and optimal parallel algorithms for testing pattern involvement for all length 4 permutations are described. This is an improvement as the previous best sequential algorithms, for ...
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In this paper linear time sequential and optimal parallel algorithms for testing pattern involvement for all length 4 permutations are described. This is an improvement as the previous best sequential algorithms, for most of these pattern require time. Our parallel algorithms can be implemented in time with processors on the CREW PRAM model, or alternatively in time with processors on a CRCW PRAM PRAM model. Parallel algorithm can also be implemented in constant time with processors on a CRCW PRAM model. The previous best parallel algorithms were available for only some of these patterns and took time with n processors on the CREW PRAM model.
We consider the problem of sorting n integers when the elements are drawn from the restricted domain [1...n]. A new deterministic parallel algorithm for sorting n integers is obtained. Its running time is O(log n log(...
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We consider the problem of sorting n integers when the elements are drawn from the restricted domain [1...n]. A new deterministic parallel algorithm for sorting n integers is obtained. Its running time is O(log n log(n/log n)) using n/log n processors on EREW (exclusive read exclusive write) PRAM (parallel random access machine). Also, our algorithm was modified to become optimal when we use rootn processors. This algorithm belongs to class EP (Efficient, Polynomial fast).
This paper takes a close look at the important commonalities and subtle differences between the well-established field of supervised learning and the much younger one of cooptimization. It explains the relationships b...
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This paper takes a close look at the important commonalities and subtle differences between the well-established field of supervised learning and the much younger one of cooptimization. It explains the relationships between the problems, algorithms and views on cost and performance of the two fields, all throughout providing a two-way dictionary for the respective terminologies used to describe these concepts. The intent is to facilitate advancement of both fields through transfer and cross-pollination of ideas, techniques and results. As a proof of concept, a theoretical study is presented on the connection between existence / lack of free lunch in the two fields, showcasing a few ideas for improving computational complexity of certain supervised learning approaches.
Multivariable trial functions that depend on random parameters are maximized by crude global search. Analytical and numerical investigations of error distributions confirm recent conclusions that in practice random se...
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Multivariable trial functions that depend on random parameters are maximized by crude global search. Analytical and numerical investigations of error distributions confirm recent conclusions that in practice random searching points perform better than rectangular lattices, and that quasi-random searching points are even more efficient.
In standard sensor network applications, sensors generate raw data that have to be sent to a sink node. In order to save energy, special intermediate storage nodes can be exploited in order to compress data before for...
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In standard sensor network applications, sensors generate raw data that have to be sent to a sink node. In order to save energy, special intermediate storage nodes can be exploited in order to compress data before forwarding them to the sink. We consider the problem of locating k storage nodes in order to minimize the energy consumed for converging data to the sink. This is known as the minimum k-storage problem. We show that in directed graphs (and in particular in Directed Acyclic Graphs) the problem does not admit an algorithm with a constant approximation ratio, unless P = NP. If the topology is restricted to trees where the arcs are directed towards the sink (typical scenario in sensor networks), the problem is solvable in polynomial time. We give a dynamic programming algorithm that requires O (min{kn(2), k(2)P}) time, where n and P are the number of nodes and the path length of the tree [7], respectively. We improve over a previous algorithm which requires O (kn(2)(max{k, d})(d-1)) time, where d is the maximum out-degree of the tree [8]. (C) 2015 Elsevier B.V. All rights reserved.
作者:
TEMPO, RCENS-CNR
Politecnico di Torino 10129 Torino Corso Duca degli Abruzzi 24 Italy
We study parametric identification of uncertain systems in a deterministic setting. We assume that the problem data and the linearly parameterized system model are given, In the presence of a priori information and no...
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We study parametric identification of uncertain systems in a deterministic setting. We assume that the problem data and the linearly parameterized system model are given, In the presence of a priori information and norm-bounded noise, we design optimal worst-case algorithms. In particular, we study the interplay between identification tools and nonstandard techniques used in approximation theory. The obtained estimators, called smoothing algorithms, as well as the identification errors are computed by means of the singular-value decomposition of the system model. Finally, the proposed algorithms are tested on real data referring to the tuning of A/D converters.
It is shown that the AKS sorting network can indeed be laid out in area A equals O(n**2), while maintaining an O(log n) computation time, thereby establishing its optimality in the VLSI model of computation.
It is shown that the AKS sorting network can indeed be laid out in area A equals O(n**2), while maintaining an O(log n) computation time, thereby establishing its optimality in the VLSI model of computation.
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