7 resultados para Wang-Landau algorithm
em DigitalCommons@University of Nebraska - Lincoln
Resumo:
Survivable traffic grooming (STG) is a promising approach to provide reliable and resource-efficient multigranularity connection services in wavelength-division-multiplexing (WDM) optical networks. In this paper, we study the STG problem in WDM mesh optical networks employing path protection at the connection level. Both dedicated-protection and shared-protection schemes are considered. Given network resources, the objective of the STG problem is to maximize network throughput. To enable survivability under various kinds of single failures, such as fiber cut and duct cut, we consider the general shared-risklink- group (SRLG) diverse routing constraints. We first resort to the integer-linear-programming (ILP) approach to obtain optimal solutions. To address its high computational complexity, we then propose three efficient heuristics, namely separated survivable grooming algorithm (SSGA), integrated survivable grooming algorithm (ISGA), and tabu-search survivable grooming algorithm (TSGA). While SSGA and ISGA correspond to an overlay network model and a peer network model, respectively, TSGA further improves the grooming results from SSGA and ISGA by incorporating the effective tabu-search (TS) method. Numerical results show that the heuristics achieve comparable solutions to the ILP approach, which uses significantly longer running times than the heuristics.
Resumo:
We investigate waveband switching (WBS) with different grouping strategies in wavelength-division multiplexing (WDM) mesh networks. End-to-end waveband switching (ETEWBS) and same-destination-intermediate waveband switching (SD-IT-WBS) are analyzed and compared in terms of blocking probability and cost savings. First, an analytical model for ETEWBS is proposed to determine the network blocking probability in a mesh network. For SD-IT-WBS, a simple waveband switching algorithm is presented. An analytical model to determine the network blocking probability is proposed for SD-IT-WBS based on the algorithm. The analytical results are validated by comparing with simulation results. Both results match well and show that ETE-WBS slightly outperforms SD-IT-WBS in terms of blocking probability. On the other hand, simulation results show that SD-IT-WBS outperforms ETE-WBS in terms of cost savings.
Resumo:
Waveband switching (WBS) is an important technique to save switching and transmission cost in wavelength -division multiplexed (WDM) optical networks. A cost-efficient WBS scheme would enable network carriers to increase the network throughput (revenue) while achieving significant cost savings. We identify the critical factors that determine the WBS network throughput and switching cost and propose a novel intermediate waveband switching (IT-WBS) algorithm, called the minimizing-weighted-cost (MWC) algorithm. The MWC algorithm defines a cost for each candidate route of a call. By selecting the route with the smallest weighted cost, MWC balances between minimizing the call blocking probability and minimizing the network switching cost. Our simulations show that MWC outperforms other wavelength/waveband switching algorithms and can enhance the network throughput at a reduced cost.
Resumo:
In this paper, we propose a Loss Tolerant Reliable (LTR) data transport mechanism for dynamic Event Sensing (LTRES) in WSNs. In LTRES, a reliable event sensing requirement at the transport layer is dynamically determined by the sink. A distributed source rate adaptation mechanism is designed, incorporating a loss rate based lightweight congestion control mechanism, to regulate the data traffic injected into the network so that the reliability requirement can be satisfied. An equation based fair rate control algorithm is used to improve the fairness among the LTRES flows sharing the congestion path. The performance evaluations show that LTRES can provide LTR data transport service for multiple events with short convergence time, low lost rate and high overall bandwidth utilization.
Resumo:
Survivable traffic grooming (STG) is a promising approach to provide reliable and resource-efficient multigranularity connection services in wavelength division multiplexing (WDM) optical networks. In this paper, we study the STG problem in WDM mesh optical networks employing path protection at the connection level. Both dedicated protection and shared protection schemes are considered. Given the network resources, the objective of the STG problem is to maximize network throughput. To enable survivability under various kinds of single failures such as fiber cut and duct cut, we consider the general shared risk link group (SRLG) diverse routing constraints. We first resort to the integer linear programming (ILP) approach to obtain optimal solutions. To address its high computational complexity, we then propose three efficient heuristics, namely separated survivable grooming algorithm (SSGA), integrated survivable grooming algorithm (ISGA) and tabu search survivable grooming algorithm (TSGA). While SSGA and ISGA correspond to an overlay network model and a peer network model respectively, TSGA further improves the grooming results from SSGA and ISGA by incorporating the effective tabu search method. Numerical results show that the heuristics achieve comparable solutions to the ILP approach, which uses significantly longer running times than the heuristics.
Resumo:
Sparse traffic grooming is a practical problem to be addressed in heterogeneous multi-vendor optical WDM networks where only some of the optical cross-connects (OXCs) have grooming capabilities. Such a network is called as a sparse grooming network. The sparse grooming problem under dynamic traffic in optical WDM mesh networks is a relatively unexplored problem. In this work, we propose the maximize-lightpath-sharing multi-hop (MLS-MH) grooming algorithm to support dynamic traffic grooming in sparse grooming networks. We also present an analytical model to evaluate the blocking performance of the MLS-MH algorithm. Simulation results show that MLSMH outperforms an existing grooming algorithm, the shortest path single-hop (SPSH) algorithm. The numerical results from analysis show that it matches closely with the simulation. The effect of the number of grooming nodes in the network on the blocking performance is also analyzed.
Resumo:
Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new regions that the current hypothesis misclassifies. We propose a new active learning algorithm that balances such exploration with refining of the decision boundary by dynamically adjusting the probability to explore at each step. Our experimental results demonstrate improved performance on data sets that require extensive exploration while remaining competitive on data sets that do not. Our algorithm also shows significant tolerance of noise.