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232
Compact routing schemes
 in SPAA ’01: Proceedings of the thirteenth annual ACM symposium on Parallel algorithms and architectures
"... We describe several compact routing schemes for general weighted undirected networks. Our schemes are simple and easy to implement. The routing tables stored at the nodes of the network are all very small. The headers attached to the routed messages, including the name of the destination, are extrem ..."
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Cited by 237 (5 self)
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We describe several compact routing schemes for general weighted undirected networks. Our schemes are simple and easy to implement. The routing tables stored at the nodes of the network are all very small. The headers attached to the routed messages, including the name of the destination, are extremely short. The routing decision at each node takes constant time. Yet, the stretch of these routing schemes, i.e., the worst ratio between the cost of the path on which a packet is routed and the cost of the cheapest path from source to destination, is a small constant. Our schemes achieve a nearoptimal tradeoff between the size of the routing tables used and the resulting stretch. More specifically, we obtain: 1. A routing scheme that uses only ~ O(n 1=2) bits of memory at each node of an nnode network that has stretch 3. The space is optimal, up to logarithmic factors, in the sense that
Fractional cascading: I. A data structuring technique
 Algorithmica
, 1986
"... Abstract. In computational geometry many search problems and range queries can be solved by performing an iterative search for the same key in separate ordered lists. In this paper we show that, if these ordered lists can be put in a onetoone correspondence with the nodes of a graph of degree d so ..."
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Cited by 174 (6 self)
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Abstract. In computational geometry many search problems and range queries can be solved by performing an iterative search for the same key in separate ordered lists. In this paper we show that, if these ordered lists can be put in a onetoone correspondence with the nodes of a graph of degree d so that the iterative search always proceeds along edges of that graph, then we can do much better than the obvious sequence of binary searches. Without expanding the storage by more than a constant factor, we can build a datastructure, called a fractional cascading structure, in which all original searches after the first can be carried out at only log d extra cost per search. Several results related to the dynamization of this structure are also presented. A companion paper gives numerous applications of this technique to geometric problems.
HighSpeed Policybased Packet Forwarding Using Efficient Multidimensional Range Matching
 In ACM SIGCOMM
, 1998
"... The ability to provide differentiated services to users with widely varying requirements is becoming increasingly important, and Internet Service Providers would like to provide these differentiated services using the same shared network infrastructure. The key mechanism, that enables differentiatio ..."
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Cited by 172 (0 self)
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The ability to provide differentiated services to users with widely varying requirements is becoming increasingly important, and Internet Service Providers would like to provide these differentiated services using the same shared network infrastructure. The key mechanism, that enables differentiation in a connectionless network, is the packet classification function that parses the headers of the packets, and after determining their context, classifies them based on administrative policies or realtime reservation decisions. Packet classification, however, is a complex operation that can become the bottleneck in routers that try to support gigabit link capacities. Hence, many proposals for differentiated services only require classification at lower speed edge routers and also avoid classification based on multiple fields in the packet header even if it might be advantageous to service providers. In this paper, we present new packet classification schemes that, with a worstcase and tr...
A FASTER STRONGLY POLYNOMIAL MINIMUM COST FLOW ALGORITHM
, 1991
"... In this paper, we present a new strongly polynomial time algorithm for the minimum cost flow problem, based on a refinement of the EdmondsKarp scaling technique. Our algorithm solves the uncapacitated minimum cost flow problem as a sequence of O(n log n) shortest path problems on networks with n no ..."
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Cited by 161 (11 self)
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In this paper, we present a new strongly polynomial time algorithm for the minimum cost flow problem, based on a refinement of the EdmondsKarp scaling technique. Our algorithm solves the uncapacitated minimum cost flow problem as a sequence of O(n log n) shortest path problems on networks with n nodes and m arcs and runs in O(n log n (m + n log n)) time. Using a standard transformation, thjis approach yields an O(m log n (m + n log n)) algorithm for the capacitated minimum cost flow problem. This algorithm improves the best previous strongly polynomial time algorithm, due to Z. Galil and E. Tardos, by a factor of n 2 /m. Our algorithm for the capacitated minimum cost flow problem is even more efficient if the number of arcs with finite upper bounds, say n', is much less than m. In this case, the running time of the algorithm is O((m ' + n)log n(m + n log n)).
Should Tables Be Sorted?
, 1979
"... We examine optimality questions in the following information retrieval problem: Given a set S of n keys, store them so that queries of the form "Is X \in S?" can be answered quickly. It is shown that, in a rather general model including all the commonlyused schemes, rMn+qi P ro bes to the ..."
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Cited by 160 (0 self)
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We examine optimality questions in the following information retrieval problem: Given a set S of n keys, store them so that queries of the form "Is X \in S?" can be answered quickly. It is shown that, in a rather general model including all the commonlyused schemes, rMn+qi P ro bes to the table are needed in the worst case, provided the key space is sufficiently large. The effects of smaller key space and arbitrary encoding are also explored.
Tradeoffs for Packet Classification
"... We present an algorithmic framework for solving the packet classification problem that allows various access time vs. memory tradeoffs. It reduces the multidimensional packet classification problem to solving a few instances of the onedimensional IP lookup problem. It gives the best known lookup ..."
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Cited by 136 (1 self)
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We present an algorithmic framework for solving the packet classification problem that allows various access time vs. memory tradeoffs. It reduces the multidimensional packet classification problem to solving a few instances of the onedimensional IP lookup problem. It gives the best known lookup performance with moderately large memory space. Furthermore, it efficiently supports a reasonable number of additions and deletions to the rulesets without degrading the lookup performance. We perform a thorough experimental study of the tradeoffs for the twodimensional packet classification problem on rulesets derived from datasets collected from AT&T WorldNet, an Internet Service Provider.
Faster algorithms for the shortest path problem
, 1990
"... Efficient implementations of Dijkstra's shortest path algorithm are investigated. A new data structure, called the radix heap, is proposed for use in this algorithm. On a network with n vertices, mn edges, and nonnegative integer arc costs bounded by C, a onelevel form of radix heap gives a t ..."
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Cited by 133 (13 self)
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Efficient implementations of Dijkstra's shortest path algorithm are investigated. A new data structure, called the radix heap, is proposed for use in this algorithm. On a network with n vertices, mn edges, and nonnegative integer arc costs bounded by C, a onelevel form of radix heap gives a time bound for Dijkstra's algorithm of O(m + n log C). A twolevel form of radix heap gives a bound of O(m + n log C/log log C). A combination of a radix heap and a previously known data structure called a Fibonacci heap gives a bound of O(m + n /log C). The best previously known bounds are O(m + n log n) using Fibonacci heaps alone and O(m log log C) using the priority queue structure of Van Emde Boas et al. [17].
Four results on randomized incremental constructions
 Comput. Geom. Theory Appl
, 1993
"... Raimund Seidel§ We prove four results on randomized incremental constructions (RIes): • an analysis of the expected behavior under insertion and deletions, • a fully dynamic data structure for convex hull mamtenance in arbitrary dimensions, • a tail estimate for the space complexity of RIes, • a low ..."
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Cited by 116 (19 self)
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Raimund Seidel§ We prove four results on randomized incremental constructions (RIes): • an analysis of the expected behavior under insertion and deletions, • a fully dynamic data structure for convex hull mamtenance in arbitrary dimensions, • a tail estimate for the space complexity of RIes, • a lower bound on the complexity of agame related to RIes. 1
Simulation of networks of spiking neurons: A review of tools and strategies
 Journal of Computational Neuroscience
, 2007
"... We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on ..."
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Cited by 106 (29 self)
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We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including HodgkinHuxley type, integrateandfire models, interacting with currentbased or conductancebased synapses, using clockdriven or eventdriven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given
A Data Structure for Manipulating Priority Queues
, 1978
"... A data structure is described which can be used for representing a collection of priority queues. The primitive operations are insertion, deletion, union, update, and search for an item of earliest priority. ..."
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Cited by 106 (1 self)
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A data structure is described which can be used for representing a collection of priority queues. The primitive operations are insertion, deletion, union, update, and search for an item of earliest priority.