Probabilistic Path Selection Research Articles

Risk‐aware last‐minute data backup in inter‐datacenter networks

Network failure caused by disasters (both natural and man-made like earthquakes, floods, cyclones, electromagnetic pulse attacks etc.) result in communication disruption and huge amounts of data loss in the backbone datacenter (DC) networks. To prevent such large-scale network disruptions and quickly resume connectivity after the disaster, network operators require improved and efficient data-transfer algorithms in geographically distributed (geo-distributed) optical inter-DC networks. Minimising loss of infrastructure and preventing network disruption requires estimating the damage from a possible disaster. In this study, the authors consider a mutual backup model, where DCs can serve as backup sites of each other, thereby significantly reducing the backup duration (i.e. DC-Backup-Window (DC-B-Wnd)). They specifically consider the joint optimisation of probabilistic backup site selection and the amount of data to be backed up. They propose mixed-integer linear programming models for backup time minimisation using a single DC as well as dual DCs at backup sites. Further, they investigate the trade-off between DC-B-Wnd and the computational complexity of the proposed algorithms and perform extensive numerical simulations to show that, in the case of disasters, single and dual DC backups with risk-aware probabilistic path selection give shorter backup windows as compared to existing algorithms.

EpiACO - a method for identifying epistasis based on ant Colony optimization algorithm

BackgroundIdentifying epistasis or epistatic interactions, which refer to nonlinear interaction effects of single nucleotide polymorphisms (SNPs), is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Though many works have been done for identifying epistatic interactions, due to their methodological and computational challenges, the algorithmic development is still ongoing.ResultsIn this study, a method epiACO is proposed to identify epistatic interactions, which based on ant colony optimization algorithm. Highlights of epiACO are the introduced fitness function Svalue, path selection strategies, and a memory based strategy. The Svalue leverages the advantages of both mutual information and Bayesian network to effectively and efficiently measure associations between SNP combinations and the phenotype. Two path selection strategies, i.e., probabilistic path selection strategy and stochastic path selection strategy, are provided to adaptively guide ant behaviors of exploration and exploitation. The memory based strategy is designed to retain candidate solutions found in the previous iterations, and compare them to solutions of the current iteration to generate new candidate solutions, yielding a more accurate way for identifying epistasis.ConclusionsExperiments of epiACO and its comparison with other recent methods epiMODE, TEAM, BOOST, SNPRuler, AntEpiSeeker, AntMiner, MACOED, and IACO are performed on both simulation data sets and a real data set of age-related macular degeneration. Results show that epiACO is promising in identifying epistasis and might be an alternative to existing methods.

Optimal path selection for minimizing the differential delay in Ethernet-over-SONET

We consider the problem of minimizing the differential delay in a virtually concatenated Ethernet-over-SONET (EoS) system by suitable path selection. In such a system, a service provider can dynamically add virtual channels to or drop them from a Virtually Concatenated Group (VCG). A new virtual channel can be added to the VCG provided that the differential delays between the new channel and the existing ones are within a certain limit that reflects the available memory buffer of the EoS system. We model the problem of finding such a virtual channel as a constrained path selection problem, where the delay of the required (feasible) path is constrained not only by an upper bound but also by a lower bound. We consider two cases: exactly known link delays and imprecisely known link delays. For the first case, we propose two algorithms for finding a feasible path. The first is based on a link metric that linearly combines the original link weight (the link delay) and its inverse. The theoretical properties of such a metric are studied and used to develop a highly efficient heuristic for path selection. The second algorithm is a “backward–forward” heuristic in which the nodes in the graph are prelabeled during the backward phase. The labels are then used in the forward phase to identify a feasible path. For the imprecise-link-state case, the problem is modeled as one of finding the most probable feasible path, where link weights are random variables. A “backward–forward” heuristic is proposed which again uses prelabeling of the graph in the backward direction followed by a forward search that attempts to minimize an objective function. Simulations are conducted to evaluate the performance of the proposed algorithms and to demonstrate the advantages of the probabilistic path selection approach over the classic trigger-based approach.

Mobility-aware mesh construction algorithm for low data-overhead multicast ad hoc routing

We study the problem of controlling data overhead of mesh-based multicast ad hoc routing protocols by adaptively adding redundancy to the minimal data overhead multicast mesh as required by the network conditions. We show that the computation of the minimal data overhead multicast mesh is NP-complete, and we propose an heuristic approximation algorithm inspired on epidemic algorithms. In addition, we propose a mobility-aware and adaptive mesh construction algorithm based on a probabilistic path selection being able to adapt the reliability of the multicast mesh to the mobility of the network. Our simulation results show that the proposed approach, when implemented into ODMRP, is able to offer similar performance results and a lower average latency while reducing data overhead between 25 to 50% compared to the original ODMRP.

Bandwidth-delay constrained path selection under inaccurate state information

A key issue in any QoS routing framework is how to compute a path that satisfies given QoS constraints. We focus on the path computation problem subject to bandwidth and delay constraints. This problem can be solved easily if the exact state information is available to the node computing the path. In practice, nodes have only imprecise knowledge of the network state. Reliance on outdated information and treating it as exact can significantly degrade the effectiveness of the path selection. We adopt a probabilistic approach in which the state parameters (available bandwidth and delay) are characterized by random variables. The goal is then to find the most-probable bandwidth-delay-constrained path (MP-BDCP). We provide efficient solutions for the MP-BDCP problem by decomposing it into the most-probable delay-constrained path (MP-DCP) problem and the most-probable bandwidth-constrained path (MP-BCP) problem. MP-DCP by itself is known to be NP-hard, necessitating the use of approximate solutions. We use the central limit theorem and Lagrange relaxation techniques to provide two complementary solutions for MP-DCP. These solutions are highly efficient, requiring on average a few iterations of Dijkstra's shortest path algorithm. As for MP-BCP, it can be easily transformed into a variant of the shortest path problem. Our MP-DCP and MP-BCP solutions are then combined to obtain a set of near-nondominated paths for the MP-BDCP problem. Decision makers can then select one or more of these paths based on a specific utility function. Extensive simulations demonstrate the efficiency of the proposed algorithmic solutions and, more generally, to contrast the probabilistic path selection approach with the standard threshold-based triggered approach.