网络中多节点故障定位的探测路径选择算法

针对现有故障定位技术不能满足多节点故障定位的要求,尤其当网络中存在大量故障节点时,提出了一种基于主动探测的探测路径选择算法。该算法主要包括用于故障检测的贪婪路径选择算法和用于故障定位的禁忌链路搜索算法。在故障检测阶段,使用贪婪路径选择算法迭代地选择具有最小权重的探测路径覆盖网络中的节点。在故障定位阶段,使用禁忌链路搜索算法多次生成候选路径集以选择最合适的探测路径来解决多节点故障定位问题。在随机网络拓扑和真实网络拓扑上的仿真结果表明,与现有的节点故障定位算法相比,探测路径选择算法具有更高的成功定位率和更低的探测成本。     

Transmission reliability of k minimal paths within time threshold

The quickest path problem involving two attributes, the capacity and the lead time, is to find a single path with minimum transmission time. The capacity of each arc is assumed to be deterministic in this problem. However, in many practical networks such as computer networks, telecommunication networks, and logistics networks, each arc is multistate due to failure, maintenance, etc. Such a network is named a multistate flow network. Hence, both the transmission time to deliver data through a minimal path and the minimum transmission time through a multistate flow network are not fixed. In order to reduce the transmission time, the data can be transmitted through k minimal paths simultaneously. The purpose of this paper is to evaluate the probability that d units of data can be transmitted through k minimal paths within time threshold T. Such a probability is called the transmission reliability. A simple algorithm is proposed to generate all lower boundary points for (d, T), the minimal system states satisfying the demand within time threshold. The transmission reliability can be subsequently computed in terms of such points. Another algorithm is further proposed to find the optimal combination of k minimal paths with highest transmission reliability.     

A label-setting algorithm for finding a quickest path

The quickest path problem is to find a path to send a given amount of data from the source to the destination with minimum transmission time. To find the quickest path, existing algorithms enumerate non-dominated paths with distinct capacity, and then determine a quickest path by comparing their transmission time. In this paper, we propose a label-setting algorithm for finding a quickest path by transforming a network to another network where an important property holds that each subpath of a quickest path is also a quickest path. The proposed algorithm avoids enumerating non-dominated paths whose transmission time is greater than the minimum transmission time. Although the computational complexity of the proposed algorithm is the same as that of existing algorithms, experimental results show that our algorithm is efficient when a network has two or more non-dominated paths.     

An efficient network flow code for finding all minimum cost s–t cutsets

Cutset algorithms have been well documented in the operations research literature. A directed graph is used to model the network, where each node and arc has an associated cost to cut or remove it from the graph. The problem considered in this paper is to determine all minimum cost sets of nodes and/or arcs to cut so that no directed paths exist from a specified source node s to a specified sink node t. By solving the dual maximum flow problem, it is possible to construct a binary relation associated with an optimal maximum flow such that all minimum cost s–t cutsets are identified through the set of closures for this relation. The key to our implementation is the use of graph theoretic techniques to rapidly enumerate this set of closures. Computational results are presented to suggest the efficiency of our approach.Scope and purposeThis paper describes the technical details of a network flow algorithm used to find all minimum cost s–t cutsets in any network topology. The motivation for this work was to provide additional automated analysis capability to a military network targeting system. Specifically, the problem is to identify a minimum cost set of nodes and/or arcs that when removed from the network, will disconnect a selected pair of origin–destination nodes. Algorithms for solving this problem are well understood, with an active research thrust in both the operations research and computer science academic communities in developing more efficient algorithms for larger networks. The main contribution of this paper is in extending these algorithms to quickly find all minimum cost cutset solutions. The implementation described in this paper outperformed conventional methods by several orders of magnitude on networks having thousands of nodes and arcs, with empirical solution times that grew linearly with the network size. The results translate to a real-time cutset analysis capability to support military targeting applications.     

Algorithms for the Determination of Cutsets in a Hypergraph

Given a hypergraph,this paper provides three algorithms for finding all its minimal cutsets,minimal link cutsets and the least cutsets.The result not only set up a new studying field on cutsets of hypergraph,but also lay a foundation of analyzing the performance of multibus systems.The algorithm for determining all the least cutsets in a hypergaph is polynomial complex and more efficient than that in [2].     

On the sum-max bicriterion path problem

In network analysis, there are many applications in which several weights associated with traversing each arc are given, so it is natural to consider multiple-objective path problems. Usually, there is no path which is simultaneously optimal with respect to all objective functions, so it will be necessary to obtain efficient paths of interest, i.e. paths for which there exists no other path that yields an improvement in one of the objective functions without causing a degradation in the others. Methods for determining efficient paths form part of a very extensive literature on multiple-objective optimization, most of them being proposed for objectives defined by sum functions. Our aim is to study the particular case of a bicriterion problem whose objective functions are defined by a sum and a maximum. A very important aspect of this problem is to find a best compromise solution, which is shown to be equivalent to solve the quickest path problem, which has interesting applications in communication and transportation networks.In this paper, we study a special class of bicriterion path problems where the objective functions are defined by a sum and a maximum: The sum-max bicriterion path problem (SMBPP). After reviewing some special kinds of efficient paths, we propose some algorithms to generate these kinds of efficient paths, based on a progressive reduction of the original network. We analyse its relationship with the quickest path problem (QPP), showing that this is equivalent to the weighted problem associated to the SMBPP, which is also solved by a modification of an algorithm proposed for the QPP. A computational study is presented which shows the superiority of the algorithm proposed in this paper over other existing algorithms to generate the entire set E of efficient paths of the SMBPP.     

A method to evaluate the routing policy with two minimal paths within time threshold

Two attributes, the capacity and the lead time, are involved in the quickest path problem which finds a path with the minimum transmission time. The capacity of each edge is assumed to be deterministic in this problem. However, in many real-life networks such as computer, telecommunication, logistics networks, etc., each edge should be multistate due to failure, maintenance, etc. Such a network is named a multistate network. Hence, the minimum transmission time through a multistate network is not fixed. We evaluate the system reliability that a specified amount of data can be sent through a pair of minimal paths simultaneously within the time threshold. A solution procedure is first proposed to calculate it. In order to boost the system reliability, the network administrator decides the routing policy in advance to indicate the first and the second priority pairs of minimal paths. The second one will be responsible for the transmission duty if the first one fails. According to the routing policy, the system reliability can be subsequently computed. The case to transmit data through more than two minimal paths can be extended easily.     

On the effective tool path planning algorithms for sculptured surface manufacture

This paper describes the cutter path planning and cutter interference (gouging) analysis algorithms developed to generate optimal tool path for manufacturing sculptured surfaces on three axes CNC machine tools. Cutter path planning algorithm approximates the parametric curves on three dimensional surfaces by a sequence of straight line segments and generates optimal tool paths by minimizing the number of interpolation points while keeping the path deviations within the specified tolerances. Cutter interference analysis algorithm checks for the self intersection of an offset surface and determines the self-intersection curve. The tool path is then planned over the cutter contact (CC) surface after removing the CC data that lies inside the self-intersection curve. Finally, the effectiveness of these algorithms is demonstrated by implementing them in CAD/CAM system.     

Counterexample Generation in Probabilistic Model Checking

Providing evidence for the refutation of a property is an essential, if not the most important, feature of model checking. This paper considers algorithms for counterexample generation for probabilistic CTL formulae in discrete-time Markov chains. Finding the strongest evidence (i.e., the most probable path) violating a (bounded) until-formula is shown to be reducible to a single-source (hop-constrained) shortest path problem. Counterexamples of smallest size that deviate most from the required probability bound can be obtained by applying (small amendments to) k-shortest (hop-constrained) paths algorithms. These results can be extended to Markov chains with rewards, to LTL model checking, and are useful for Markov decision processes. Experimental results show that typically the size of a counterexample is excessive. To obtain much more compact representations, we present a simple algorithm to generate (minimal) regular expressions that can act as counterexamples. The feasibility of our approach is illustrated by means of two communication protocols: leader election in an anonymous ring network and the Crowds protocol.     

Selection of near-minimum time geometric paths for robotic manipulators

A number of trajectory planning algorithms exist for calculating the joint positions, velocities, and torques which will drive a robotic manipulator along a given geometric path in minimum time. However, the time depends upon the geometric path, so the traversal time of the path should be considered again for geometric planning. There are algorithms available for finding minimum distance paths, but even when obstacle avoidance is not an issue, minimum (Cartesian) distance is not necessarily equivalent to minimum time. In this paper, we have derived a lower bound on the time required to move a manipulator from one point to another, and determined the form of the path which minimizes this lower bound. As numerical examples, we have applied the path solution to the first three joints of the Bendix PACS arm and the Stanford arm. These examples do indeed demonstrate that the derived approximate solutions usually require less time than Cartesian straight-line (minimum-distance) paths and joint-interpolated paths.     

Adaptive deadlock- and livelock-free routing with all minimal pathsin torus networks

This paper consists of two parts. In the first part, two new algorithms for deadlock- and livelock-free wormhole routing in the torus network are presented. The first algorithm, called Channels, is for the n-dimensional torus network. This technique is fully-adaptive minimal, that is, all paths with a minimal number of hops from source to destination are available for routing, and needs only five virtual channels per bidirectional link, the lowest channel requirement known in the literature for fully-adaptive minimal worm-hole routing. In addition, this result also yields the lowest buffer requirement known in the literature for packet-switched fully-adaptive minimal routing. The second algorithm, called 4-Classes, is for the bidimensional torus network. This technique is fully-adaptive minimal and requires only eight virtual channels per bidirectional link. Also, it allows for a highly parallel implementation of its associated routing node. In the second part of this paper, four worm-hole routing techniques for the two-dimensional torus are experimentally evaluated using a dynamic message injection model and different traffic patterns and message lengths     

全光双向网络中的波长转换

在许多光学路由中,对于给定一组通讯路的集合,必须对有公共边的路安排相同的波长.为了充分利用光学的带宽,目的是安排尽量少的波长数.但有时候也考虑使用波长转换器. 如果一个顶点安装转换器,任何经过这个顶点的路都可以改变其波长.因此在某些顶点安装波长转换器后可以将波长的数目减少到一个拥塞界,因此,Wilfong和Winkler定义了一个顶点集 S,在S上安装转换器后,任何路集都可以分配数目等于拥塞界的波长,这样的集合S被称为充分集.研究在双向网络中的最小充分集问题,并把他转化为最小顶点覆盖问题.对此问题给出几个算法.     

Reliability evaluation for a manufacturing network with multiple production lines

This paper focuses on performance evaluation of a manufacturing system with multiple production lines based on the network-analysis perspective. Due to failure, partial failure, or maintenance, the capacity of each machine is stochastic (i.e., multi-state). Hence, the manufacturing system can be constructed as a stochastic-flow network, named manufacturing network herein. This paper intends to measure the probability that the manufacturing network can satisfy customers’ orders. Such a probability is referred to as the system reliability. A graphical representation is first proposed to transform a manufacturing system into a manufacturing network. Thereafter, we decompose the manufacturing network into general processing paths and reworking paths. Three algorithms are subsequently developed for different scenarios and multiple production lines to generate the minimal capacity vectors that machines should provide to satisfy demand. The system reliability can be derived in terms of such capacity vectors afterwards.     

Reliability evaluation for a manufacturing network with multiple production lines

This paper focuses on performance evaluation of a manufacturing system with multiple production lines based on the network-analysis perspective. Due to failure, partial failure, or maintenance, the capacity of each machine is stochastic (i.e., multi-state). Hence, the manufacturing system can be constructed as a stochastic-flow network, named manufacturing network herein. This paper intends to measure the probability that the manufacturing network can satisfy customers’ orders. Such a probability is referred to as the system reliability. A graphical representation is first proposed to transform a manufacturing system into a manufacturing network. Thereafter, we decompose the manufacturing network into general processing paths and reworking paths. Three algorithms are subsequently developed for different scenarios and multiple production lines to generate the minimal capacity vectors that machines should provide to satisfy demand. The system reliability can be derived in terms of such capacity vectors afterwards.     

Parallel Algorithms for Dynamic Shortest Path Problems

The development of intelligent transportation systems (ITS) and the resulting need for the solution of a variety of dynamic traffic network models and management problems require faster‐than‐real‐time computation of shortest path problems in dynamic networks. Recently, a sequential algorithm was developed to compute shortest paths in discrete time dynamic networks from all nodes and all departure times to one destination node. The algorithm is known as algorithm DOT and has an optimal worst‐case running‐time complexity. This implies that no algorithm with a better worst‐case computational complexity can be discovered. Consequently, in order to derive algorithms to solve all‐to‐one shortest path problems in dynamic networks, one would need to explore avenues other than the design of sequential solution algorithms only. The use of commercially‐available high‐performance computing platforms to develop parallel implementations of sequential algorithms is an example of such avenue. This paper reports on the design, implementation, and computational testing of parallel dynamic shortest path algorithms. We develop two shared‐memory and two message‐passing dynamic shortest path algorithm implementations, which are derived from algorithm DOT using the following parallelization strategies: decomposition by destination and decomposition by transportation network topology. The algorithms are coded using two types of parallel computing environments: a message‐passing environment based on the parallel virtual machine (PVM) library and a multi‐threading environment based on the SUN Microsystems Multi‐Threads (MT) library. We also develop a time‐based parallel version of algorithm DOT for the case of minimum time paths in FIFO networks, and a theoretical parallelization of algorithm DOT on an ‘ideal’ theoretical parallel machine. Performances of the implementations are analyzed and evaluated using large transportation networks, and two types of parallel computing platforms: a distributed network of Unix workstations and a SUN shared‐memory machine containing eight processors. Satisfactory speed‐ups in the running time of sequential algorithms are achieved, in particular for shared‐memory machines. Numerical results indicate that shared‐memory computers constitute the most appropriate type of parallel computing platforms for the computation of dynamic shortest paths for real‐time ITS applications.     

基于优化链路权值的域内路由保护方案

目前,互联网部署的域内链路状态路由协议,如开放最短路径优先(Open Shortest Path First,OSPF)和中间系统到中间系统(Intermediate System-to-Intermediate System,IS-IS),采用被动恢复方案应对网络故障。随着网络的发展,大量的实时应用部署在互联网上,OSPF的收敛时间无法满足这些实时应用对收敛时间的需求。因此,学术界和工业界提出采用路由保护方案来应对网路中出现的故障。然而,已有的路由保护方案存在两个方面的问题:1)默认路径和备份路径的交叉度较高,如LFA；2)为了计算两条交叉度低的路径,对默认路径加以限制,即默认路径不采用最短路径,如Color Tree。为了解决上述两个问题,首先将上述问题归结为整数规划模型,接着利用启发式方法计算近似最优解,最后在实际网络和模拟网络中对所提算法进行了大量实验。实验结果表明,所提算法可以降低默认路径和备份路径的交叉度,极大地提高网络的可用性。     

A Lower Bound for Nearly Minimal Adaptive and Hot Potato Algorithms

Recently, Chinn et al. [10] presented lower bounds for store-and-forward permutation routing algorithms on the n \times n mesh with bounded buffer size and where a packet must take a shortest (or minimal ) path to its destination. We extend their analysis to algorithms that are nearly minimal. We also apply this technique to the domain of hot potato algorithms, where there is no storage of packets and the shortest path to a destination is not assumed (and is in general impossible). We show that ``natural' variants and ``improvements' of several algorithms in the literature perform poorly in the worst case. As a result, we identify algorithmic features that are undesirable for worst-case hot potato permutation routing. Recent works in hot potato routing have tried to define simple and greedy classes of algorithms. We show that when an algorithm is too simple and too greedy, its performance in routing permutations is poor in the worst case. Specifically, the technique of [10] is also applicable to algorithms that do not necessarily send packets in minimal or even nearly minimal paths: it may be enough that they naively attempt to do so when possible. In particular, our results show that a certain class of greedy algorithms that was suggested recently by Ben-Dor et al. [6] contains algorithms that have poor performance in routing worst-case permutations. Received August 24, 1995; revised May 27, 1997.     

Multi-robot area patrol under frequency constraints

Patrolling involves generating patrol paths for mobile robots such that every point on the paths is repeatedly covered. This paper focuses on patrolling in closed areas, where every point in the area is to be visited repeatedly by one or more robots. Previous work has often examined paths that allow for repeated coverage, but ignored the frequency in which points in the area are visited. In contrast, we first present formal frequency-based optimization criteria used for evaluation of patrol algorithms. Then, we present a patrol algorithm that guarantees maximal uniform frequency, i.e., each point in the target area is covered at the same optimal frequency. This solution is based on finding a circular path that visits all points in the area, while taking into account terrain directionality and velocity constraints. Robots are positioned uniformly along this path in minimal time, using a second algorithm. Moreover, the solution is guaranteed to be robust in the sense that uniform frequency of the patrol is achieved as long as at least one robot works properly. We then present a set of algorithms for handling events along the patrol path. The algorithms differ in the way they handle the event, as a function of the time constraints for handling them. However, all the algorithms handle events while maintaining the patrol path, and minimizing the disturbance to the system.     

Redundant multicast routing in multilayer networks with shared risk resource groups: Complexity,models and algorithms

This paper presents a redundant multicast routing problem in multilayer networks that arises from large-scale distribution of realtime multicast data (e.g., Internet TV, videocasting, online games, stock quotes). Since these multicast services commonly operate in multilayer networks, the communications paths need to be robust against a single router or link failure as well as multiple such failures due to shared risk link groups (SRLGs). The main challenge of this multicast is to ensure the service availability and reliability using a path protection scheme, which is to find a redundant path that is SRLG-disjoint (diverse) from each working path. The objective of this problem is, therefore, to find two redundant multicast trees, each from one of the two redundant sources to every destination, at a minimum total communication cost whereas two paths from the two sources to every destination are guaranteed to be SRLG-diverse (i.e., links in the same risk group are disjoint). In this paper, we present two new mathematical programming models, edge-based and path-based, for the redundant multicast routing problem with SRLG-diverse constraints. Because the number of paths in path-based model grows exponentially with the network size, it is impossible to enumerate all possible paths in real life networks. We develop three approaches (probabilistic, non-dominated and nearly non-dominated) to generate potentially good paths that may be included in the path-based model. This study is motivated by emerging applications of internet-protocol TV service, and we evaluate the proposed approaches using real life network topologies. Our empirical results suggest that both models perform very well, and the nearly non-dominated path approach outperforms all other path generation approaches.     

GANCSA: A novel approach for finding the best path for migration of mobile-agents

A mobile agent is a mobile object, whose movement requires a wise determination of the best path, as this path is applied to migrate a mobile agent from a source node to a destination node in an interconnected network of computers. Usually, the choice of the best path is made using optimization algorithms, which use the minimum time as a key measure for choosing the best path.This work proposes a new complex approach, which is called The Genetic Algorithm with Node Compression-based Search Algorithm (GANCSA). This approach utilizes a mathematical process and an optimization technique to achieve the finding of the best migration path in minimal time (sequential nodes in a time frame) for migrating a mobile agent in an interconnected network of computers. GANCSA encompasses the Genetic Algorithm (GA) as an optimization technique, and the Node Compression-based Search Algorithm (NCSA) to minimize the number of nodes.The testing of the proposed GANCSA shows that the combination of GA and NCSA reduces the time of selecting the best path from 336.448 ms to 286.29 ms after 10 iterations, believing that more time reduction can be achieved by increasing the iterations.