The capacity of optical transport networks has been increasing steadily and the networks are becoming more dynamic, complex, and transparent. Though it is common to use worst case assumptions for estimating the quality of transmission (QoT) in the physical layer, over provisioning results in high margin requirements. Accurate estimation on the QoT for to-be-established lightpaths is crucial for reducing provisioning margins. Machine learning (ML) is regarded as one of the most powerful methodological approaches to perform network data analysis and enable automated network self-configuration. In this paper, an artificial neural network (ANN) framework, a branch of ML, to estimate the optical signal-to-noise ratio (OSNR) of to-be-established lightpaths is proposed. It takes account of both nonlinear interference between spectrum neighboring channels and optical monitoring uncertainties. The link information vector of the lightpath is used as input and the OSNR of the lightpath is the target for output of the ANN. The nonlinear interference impact of the number of neighboring channels on the estimation accuracy is considered. Extensive simulation results show that the proposed OSNR estimation scheme can work with any RWA algorithm. High estimation accuracy of over 98% with estimation errors of less than 0.5dB can be achieved given enough training data. ANN model with R=4 neighboring channels should be used to achieve more accurate OSNR estimates. Based on the results, it is expected that the proposed ANN-based OSNR estimation for new lightpath provisioning can be a promising tool for margin reduction and low-cost operation of future optical transport networks.

Efficiently locating nodes and allocating demand has been a significant problem for telecommunication network carriers. Most of location models focused on where to locate nodes and how to assign increasing demand with optical access networks. However, the population in industrialized countries will decline over the coming decades. Recent advance in the optical amplifier technology has enabled node integration; an excess telecommunication node is closed and integrated to another node. Node integration in low-demand areas will improve the efficiency of access networks in this approaching age of depopulation. A dynamic node integration problem (DNIP) has been developed to organize the optimal plan for node integration. The problem of the DNIP was that it cannot consider the requirements of network carriers. In actual situations, network carriers often want to specify the way each node is managed, regardless of the mathematical optimality of the solution. This paper proposes a requirement modeling language (RML) for the DNIP, with which the requirements of network carriers can be described. The described statements are used to solve the DNIP, and consequently the calculated optimal solution always satisfies the requirements. The validity of the proposed method was evaluated with computer simulations in a case study.

This paper presents an artificial fish swarm algorithm (AFSA) to solve the multicast routing problem, which is abstracted as a Steiner tree problem in graphs. AFSA adopts a 0-1 encoding scheme to represent the artificial fish (AF), which are then subgraphs in the original graph. For evaluating each AF individual, we decode the subgraph into a Steiner tree. Based on the adopted representation of the AF, we design three AF behaviors: randomly moving, preying, and following. These behaviors are organized by a strategy that guides AF individuals to perform certain behaviors according to certain conditions and circumstances. In order to investigate the performance of our algorithm, we implement exhaustive simulation experiments. The results from the experiments indicate that the proposed algorithm outperforms other intelligence algorithms and can obtain the least-cost multicast routing tree in most cases.

Since video traffic has become a dominant flow component on the Internet, the Future Internet and New Generation Network must consider delay guarantees as a key feature in their designs. Using the stochastic network optimization, optimal control policies are designed for delay-constrained traffic in single-hop wireless networks. The resulting policy is a scheduling policy with delay guarantees. For a cross-layer design that involves both flow control and scheduling, the resulting policy is a flow control and scheduling policy that guarantees delay constraints and achieves utility performance within O(1/V) of the optimality.

We consider the minimum cost edge installation problem (MCEI) in a graph G=(V,E) with edge weight w(e)≥ 0, e∈ E. We are given a vertex s∈ V designated as a sink, an edge capacity λ>0, and a source set S⊆ V with demand q(v)∈ [0,λ], v∈ S. For each edge e∈ E, we are allowed to install an integer number h(e) of copies of e. MCEI asks to send demand q(v) from each source v∈ S along a single path Pv to the sink s without splitting the demand of any source v∈ S. For each edge e∈ E, a set of such paths can pass through a single copy of e in G as long as the total demand along the paths does not exceed the edge capacity λ. The objective is to find a set P={Pv| v∈ S∈ of paths of G that minimizes the installing cost ∑e∈ E h(e)w(e). In this paper, we propose a (15/8+ρST)-approximation algorithm to MCEI, where ρST is any approximation ratio achievable for the Steiner tree problem.

We consider the capacitated multi-source multicast tree routing problem (CMMTR) in an undirected graph G=(V,E) with a vertex set V, an edge set E and an edge weight w(e) ≥ 0, e ∈ E. We are given a source set S ⊆ V with a weight g(e) ≥ 0, e ∈ S, a terminal set M ⊆ V-S with a demand function q : M → R+, and a real number κ > 0, where g(s) means the cost for opening a vertex s ∈ S as a source in a multicast tree. Then the CMMTR asks to find a subset S′⊆ S, a partition {Z1,Z2,...,Zl} of M, and a set of subtrees T1,T2,...,Tl of G such that, for each i, ∑t∈Ziq(t) ≤ κ and Ti spans Zi∪{s} for some s ∈ S′. The objective is to minimize the sum of the opening cost of S′and the constructing cost of {Ti}, i.e., ∑s∈S′g(s)+w(Ti), where w(Ti) denotes the sum of weights of all edges in Ti. In this paper, we propose a (2ρUFL+ρST)-approximation algorithm to the CMMTR, where ρUFL and ρST are any approximation ratios achievable for the uncapacitated facility location and the Steiner tree problems, respectively. When all terminals have unit demands, we give a ((3/2)ρUFL+(4/3)ρST)-approximation algorithm.

In this paper we propose a novel and efficient method for the optimization of the power/ground (P/G) network in VLSI circuit layouts with reliability constraints. Previous algorithms in the P/G network sizing used the sequence-of-linear-programming (SLP) algorithm to solve the nonlinear optimization problems. However the transformation from nonlinear network to linear subnetwork is not optimal enough. Our new method is inspired by the biological evolution and use the grid-genetic-algorithm (GGA) to solve the optimization problem. Experimental results show that new P/G network sizes are smaller than previous algorithms, as the fittest survival in the nature. Another significant advance is that GGA method can be applied for all P/G network problems because it can get the results directly no matter whether these problems are linear or not. Thus GGA can be adopted in the transient behavior of the P/G network sizing in the future, which recently faces on the obstacles in the solution of the complex nonlinear problems.

We consider the problem of designing a physically diverse network that can support any two simultaneous node-to-node traffic flow requirements as called for by special events such as communication link failures or surges in network traffic. The design objective is to obtain a network with the minimum level of network capacity, yet robust enough to handle any two simultaneous traffic flow requirements between any nodes. To arrive at the minimum necessary network capacity,we introduce the concept of nodal requirement. Based on nodal requirements, we can build what may be called uniform protection subnetworks for equal nodal requirements. Successive uniform protection subnetworks can be built for incremental nodal requirements. This direct approach supersedes the extant work on building fully connected networks or loops from maximum spanning trees that can cope with only one traffic flow requirement. Our nodal requirements approach generalizes well to multiple simultaneous traffic flow requirements. Hub subnetworks are introduced to provide protection for networks with a unique node that has the largest nodal requirement. Further, a heuristic is considered and analyzed that assigns edge capacities of the protection network directly based on the largest two traffic flow requirements incident on the end nodes of an edge. The heuristic is attractive in being simple to implement.