Joint optimization of data network design and facility selection

The authors describe a data network design model based on a mixed integer/linear programming (MILP) formulation that does not, as do most other approaches, separate link capacity and facility selection from routing and topological design; it fully integrates these processes to capture the important couplings that exist between them. The performance constraints are incorporated into the model in such a way that they are linear, but lead to the same grade of service for a balanced network as nonlinear average network delay constraints. It is shown that this formulation leads to a natural decomposition of the optimal design problem into two subproblems solvable sequentially. In the absence of capacity allocation constraints, the capacity and flow assignment problem is solved optimally and efficiently as part of the overall design process. Moreover, the model leads directly to the solution of multifacility design problems. A fast link reduction algorithm that efficiently designs single or multifacility networks and yields robust local extrema is presented. This algorithm is based on a special-purpose monotonic greedy drop heuristic procedure. An important application of this model is the design of multifacility networks     

A Cross-Layer Optimization Framework for Multihop Multicast in Wireless Mesh Networks

The optimal and distributed provisioning of high throughput in mesh networks is known as a fundamental but hard problem. The situation is exacerbated in a wireless setting due to the interference among local wireless transmissions. In this paper, we propose a cross-layer optimization framework for throughput maximization in wireless mesh networks, in which the data routing problem and the wireless medium contention problem are jointly optimized for multihop multicast. We show that the throughput maximization problem can be decomposed into two subproblems: a data routing subproblem at the network layer, and a power control subproblem at the physical layer with a set of Lagrangian dual variables coordinating interlayer coupling. Various effective solutions are discussed for each subproblem. We emphasize the network coding technique for multicast routing and a game theoretic method for interference management, for which efficient and distributed solutions are derived and illustrated. Finally, we show that the proposed framework can be extended to take into account physical-layer wireless multicast in mesh networks     

Cross-Layer Design of Wireless Multihop Backhaul Networks With Multiantenna Beamforming

A cross-layer design approach is considered for joint routing and resource allocation for the physical (PHY) and the medium access control (MAC) layers in multihop wireless backhaul networks. The access points (APs) are assumed to be equipped with multiple antennas capable of both transmit and receive beamforming. A nonlinear optimization problem is formulated, which maximizes the fair throughput of the APs in the network under the routing and the PHY/MAC constraints. Dual decomposition is employed to decouple the original problem into smaller subproblems in different layers, which are coordinated by the dual prices. The network layer subproblem can be solved in a distributed manner and the PHY layer subproblem in a semidistributed manner. To solve the PHY layer subproblem, an iterative minimum mean square error (IMMSE) algorithm is used with the target link signal-to-interference-and-noise-ratio (SINR) set dynamically based on the price generated from the upper layers. A scheduling heuristic is also developed, which improves the choice of the transmission sets over time. Simulation results illustrate the efficacy of the proposed cross-layer design.     

Clustering‐based low‐complexity resource allocation in two‐tier femtocell networks with QoS provisioning

In this paper, we study the resource allocation problem of the uplink transmission with delay quality‐of‐service constraints in two‐tier femtocell networks. Particularly, to provide statistical delay guarantees, the effective capacity is employed as the network performance measure instead of the conventional Shannon capacity. To make the problem computationally efficient and numerically tractable, we decompose the problem into three subproblems, namely, cluster configuration subproblem, intra‐cluster subchannel allocation subproblem and inter‐cluster power control subproblem. Firstly, we develop a low‐complexity heuristic semi‐dynamic clustering scheme, where the delay of the channel state information feedback via backhaul is considered. We model such system in the framework of networked partial observation Markov decision process and derive a strategy to reduce the search range for the best cluster configuration. Then, for a given cluster configuration, the cluster heads deal with subchannel allocation and power control within each cluster. We propose a subchannel allocation scheme with proportional fairness. Thereafter, the inter‐cluster power control subproblem is modeled as a set of exact potential games, and a channel quality related pricing mechanism is presented to mitigate inter‐cluster interference. The existence and uniqueness of Nash equilibriums for the proposed game are investigated, and an effective decentralized algorithm with guaranteed convergence is designed. Simulation results demonstrate that the proposed algorithms not only have much lower computational complexity but also perform close to the exhaustive search solutions and other existing schemes. Copyright © 2015 John Wiley & Sons, Ltd.     

一种新型的动态路由和波长分配算法

本文讨论了WDM光网中，在动态业务流量和有限范围波长变换情况下的动态路由和波长分配（RWA）问题，基于Moone-Dijkstra算法，考虑到动态波长变换的可能和限制，提出了一种新型的、可实现动态最小代价路由和最佳虚波长通道的综合启发式算法（DMC－OVMP）。该算法对路由子问题和波长分配子问题既相互独立，又相互结合，优化了RWA，保证了网络信息传输的安全性。对中国教育和科研计算机网（CERNET）基于本算法进行了计算机仿真，实现了低的网络阻塞率。     

Joint Channel Assignment and Routing for Throughput Optimization in Multiradio Wireless Mesh Networks

Multihop infrastructure wireless mesh networks offer increased reliability, coverage, and reduced equipment costs over their single-hop counterpart, wireless local area networks. Equipping wireless routers with multiple radios further improves the capacity by transmitting over multiple radios simultaneously using orthogonal channels. Efficient channel assignment and routing is essential for throughput optimization of mesh clients. Efficient channel assignment schemes can greatly relieve the interference effect of close-by transmissions; effective routing schemes can alleviate potential congestion on any gateways to the Internet, thereby improving per-client throughput. Unlike previous heuristic approaches, we mathematically formulate the joint channel assignment and routing problem, taking into account the interference constraints, the number of channels in the network, and the number of radios available at each mesh router. We then use this formulation to develop a solution for our problem that optimizes the overall network throughput subject to fairness constraints on allocation of scarce wireless capacity among mobile clients. We show that the performance of our algorithms is within a constant factor of that of any optimal algorithm for the joint channel assignment and routing problem. Our evaluation demonstrates that our algorithm can effectively exploit the increased number of channels and radios, and it performs much better than the theoretical worst case bounds     

Joint source coding, routing and power allocation in wireless sensor networks

This paper proposes a cross-layer optimization framework for the wireless sensor networks. In a wireless sensor network, each sensor makes a local observation of the underlying physical phenomenon and sends a quantized version of the observation to a central location via wireless links. As the sensor observations are often partial and correlated, the network performance is a complicated and nonseparable function of individual data rates at each sensor. In addition, due to the shared nature of wireless medium, nearby transmissions often interfere with each other. Thus, the traditional "bit-pipe" model for network link capacity no longer holds. This paper deals with the joint optimization of source quantization, routing, and power control in a wireless sensor network. We follow a separate source and channel coding approach and show that the overall network optimization problem can be naturally decomposed into a source coding subproblem at the application layer and a wireless power control subproblem at the physical layer. The interfaces between the layers are precisely the dual optimization variables. In addition, we introduce a novel source coding model at the application layer, which allows the efficient design of practical source quantization schemes at each sensor. Finally, we propose a dual algorithm for the overall network optimization problem. The dual algorithm, when combined with a column- generation method, allows an efficient solution for the overall network optimization problem.     

Survivable routing in IP-over-WDM networks: An efficient and scalable local search algorithm

In IP-over-WDM networks, a logical IP network is routed on top of a physical optical fiber network. An important challenge here is to make the routing survivable. We call a routing survivable if the connectivity of the logical network is guaranteed in the case of a failure in the physical network. In this paper we describe FastSurv, a local search algorithm for survivable routing. The algorithm works in an iterative manner: after each iteration it learns more about the structure of the logical graph and in the next iteration it uses this information to improve its solution. The algorithm can take link capacity constraints into account and can be extended to deal with multiple simultaneous link failures and node failures. In a large series of tests we compare FastSurv with current state-of-the-art algorithms for this problem. We show that it can provide better solutions in much shorter time, and that it is more scalable with respect to the number of nodes, both in terms of solution quality and run time.     

Service Curve Based Routing Subject to Deterministic QoS Constraints

We consider the problem of routing in packet-switching networks subject to deterministic QoS constraints with the objective of minimizing induced cost. In particular, we propose a novel blend of generic service curves as abstraction of network transport services, of the associated network calculus under min–plus algebra and of existing heuristics for the restricted shortest path problem. Since the minimum cost objective requires an unambiguous definition of link service cost, we derive a cost model for the deployed generic service curve abstraction. Furthermore we describe the details of the different steps of the developed routing algorithm in pseudo-code notation. Our approach is subdivided into a pre-computation and an on-demand phase. Within the on-demand phase we deploy tabu-search as local search heuristic to exploit the neighbourhood of a pre-computed minimum weight path. The presented approach provides a generic technical solution for minimum-cost routing subject to deterministic QoS constraints. It is specifically designed to provide a solution path whenever the computation is stopped. Although such solutions may be non-optimal and even infeasible with respect to the QoS demand, obtaining a possible path is important in the trade-off of required processing time and accuracy of results.     

基于蚂蚁算法的多约束QOS组播路由选择

描述了多约束QoS组播路由问题的网络模型,提出了一种解决该问题的改进的蚂蚁算法.该算法对网络进行预处理,生成初始解,并转化为网络的初始信息素分布,利用蚂蚁算法的正反馈特性调整信息量的分配,使之迅速收敛到问题的最优解.仿真表明,算法可以稳定地获得优于现有启发式算法的解,是一种有效的组播路由算法.     

Optimal deployment strategy of airships for near-space communication system using multiobjective advanced evolutionary algorithm

This paper presents a near-space communication system (NSCS) using advanced deployment strategies to gain high throughput. The airships are deployed according to the user's location, assuming robust backbone network characteristics such as signal path loss, fading factor, routing efficiency, and safety issues among bi-connected airships. Due to the independent flying nature of airships, it is very attractive to deploy them as aerial base stations and construct airborne networks to provide service for on-ground users. However, it is quite challenging to optimally deploy multiple airships for on-demand coverage while maintaining the connectivity among airships. A balance between the network parameters, i.e., capacity and coverage area, should be maintained for optimal deployment of the airships. We have derived the maximum throughput of NSCS, including system parameters, as a multiobjective optimization problem subjected to efficient routing protocol and safety constraints. A decomposition-based advanced multiobjective evolutionary algorithm (AMOEA/D) is adopted to solve the deployment optimization problem. The proposed algorithm is motivated by the non-dominated solutions that maintain population diversity over the variable space. Two designed test problems, that is, the L-shaped hotspot problem and nine hotspot problems, are also investigated. Numerical results show that the proposed method improves the system performance compared with benchmark external archive-guided MOEA/D (EGA-MOEA/D) and non-dominated sorted genetic algorithms (NSGA-II) by 10.46% and 3.84%, respectively.     

QoS aware traffic grooming and integrated routing on IP over WDM networks

In this article, we consider traffic grooming and integrated routing in IP over WDM networks. The challenges of this problem come from jointly considering traffic grooming, IP routing, and lightpath routing and wavelength assignment (RWA). Due to the high bandwidth of optical fiber, there exists a mismatch between the capacity needed by an IP flow and that provided by a single lightpath. Traffic grooming is therefore used to increase the network utilization by aggregating multiple IP flows in a single lightpath. However, traffic grooming incurs additional delays that might violate Quality-of-Service (QoS) requirements of IP users. In this work, the tradeoff between traffic grooming and IP QoS routing is well-formulated as a mixed integer and linear optimization problem, in which the revenue from successfully provisioning IP paths is to be maximized. Problem constraints include IP QoS, routing, optical RWA, and the WDM network capacity. We propose a novel Lagrangean relaxation (LGR) algorithm to perform constraint relaxation and derive a set of subproblems. The Lagrangean multipliers are used in the proposed algorithm to obtain a solution in consideration of grooming advantage and resource constraints simultaneously. Through numerical experiments and comparisons between the proposed algorithm and a two-phase approach, LGR outperforms the two-phase approach under all experimental cases. In particular, the improvement ratio becomes even more significant when the ratio of IP flow to the wavelength capacity is smaller.     

Tabu search for dynamic routing communications network design

This paper presents a tabu search approach for optimizing the link capacities in a dynamic routing telecommunications network. The traffic between any two nodes in the network is routed over a one‐link direct path or, if no direct capacity is available, over a two‐link alternate path. The alternate routing paths can be changed dynamically from hour to hour as the traffic between pairs of nodes may vary with the time of day. The problem is to determine the optimal capacity level for each link in the network to minimize cost while satisfying the grade‐of‐service constraints. Although the problem can be formulated as a nonlinear integer programming problem, no efficient solution procedures are available. In this paper, we develop a two‐level tabu search heuristic for solving the problem that utilizes probabilistic move selection and coordinated solution recovery strategies. The macro level of the algorithm iteratively determines an hour for possible improvement and then the micro level seeks to optimize the routing paths for that hour. Our computational experience with both real and simulated problems indicates that significant savings can be obtained by this approach over the conventional network designs. This revised version was published online in June 2006 with corrections to the Cover Date.     

Design and operation of packet-switched networks with uncertainmessage requirements

An important concern when choosing the link design and message routes is the uncertainty regarding future average hourly message requirements between sources and destinations. A model is proposed for this problem which accounts for the uncertain average number of source-destination messages by representing them as random variables. The model also allows use of the public switched telephone network (PSTN) to accommodate some of the message requirements. This is especially important with the advent of ISDN, which will offer high-speed capacity on a universal basis. Most previous research on packet-switched network design and operation has assumed known average message requirements (although actual message requirements vary according to a Poisson process) and has focused exclusively on the use of leased lines without the availability of the PSTN. These leased lines have fixed monthly costs for specific point-to-point transmission, whereas PSTN lines can access any node from a given location. It is shown how to accelerate convergence of the flow-deviation algorithm for solving the model. Computational results are reported     

Exact approach to reliability of wireless mesh networks with directional antennas

The paper is devoted to modeling and optimization of reliable wireless mesh networks that employ directional antennas. We introduce two mixed-integer programming formulations that allow to simultaneously characterize routing patterns and transmission schedules. The first model allows for maximizing the minimal flow in a network. The second model involves reliability constraints and aims at minimizing the number of used directional antennas. In both cases locations of mesh routers are known. However, the number of installed radio interfaces and their directions are subject to optimization. We discuss a way of solving a cost minimization problem based on the introduced characterization, and present an extensive numerical study that illustrates the efficiency of the solution algorithm. We also provide an algorithm capable of verifying feasibility of obtained solutions. Moreover, in rare cases of failed verification, the algorithm provides additional constraints that should be added to the problem.     

Spectrum Sharing for Multi-Hop Networking with Cognitive Radios

Cognitive radio (CR) capitalizes advances in signal processing and radio technology and is capable of reconfiguring RF and switching to desired frequency bands. It is a frequency-agile data communication device that is vastly more powerful than recently proposed multi-channel multi-radio (MC-MR) technology. In this paper, we investigate the important problem of multi-hop networking with CR nodes. For such a network, each node has a pool of frequency bands (typically of unequal size) that can be used for communication. The potential difference in the bandwidth among the available frequency bands prompts the need to further divide these bands into sub-bands for optimal spectrum sharing. We characterize the behavior and constraints for such a multi-hop CR network from multiple layers, including modeling of spectrum sharing and sub-band division, scheduling and interference constraints, and flow routing. We develop a mathematical formulation with the objective of minimizing the required network-wide radio spectrum resource for a set of user sessions. Since the formulated model is a mixed-integer non-linear program (MINLP), which is NP-hard in general, we develop a lower bound for the objective by relaxing the integer variables and using a linearization technique. Subsequently, we design a near-optimal algorithm to solve this MINLP problem. This algorithm is based on a novel sequential fixing procedure, where the integer variables are determined iteratively via a sequence of linear programs. Simulation results show that solutions obtained by this algorithm are very close to the lower bounds obtained via the proposed relaxation, thus suggesting that the solution produced by the algorithm is near-optimal.     

A compressive sensing‐based network tomography approach to estimating origin–destination flow traffic in large‐scale backbone networks

A traffic matrix can exhibit the volume of network traffic from origin nodes to destination nodes. It is a critical input parameter to network management and traffic engineering, and thus it is necessary to obtain accurate traffic matrix estimates. Network tomography method is widely used to reconstruct end‐to‐end network traffic from link loads and routing matrix in a large‐scale Internet protocol backbone networks. However, it is a significant challenge because solving network tomography model is an ill‐posed and under‐constrained inverse problem. Compressive sensing reconstruction algorithms have been well known as efficient and precise approaches to deal with the under‐constrained inference problem. Hence, in this paper, we propose a compressive sensing‐based network traffic reconstruction algorithm. Taking into account the constraints in compressive sensing theory, we propose an approach for constructing a novel network tomography model that obeys the constraints of compressive sensing. In the proposed network tomography model, a framework of measurement matrix according to routing matrix is proposed. To obtain optimal traffic matrix estimates, we propose an iteration algorithm to solve the proposed model. Numerical results demonstrate that our method is able to pursuit the trace of each origin–destination flow faithfully. Copyright © 2014 John Wiley & Sons, Ltd.     

Designing packet‐switched feeder networks with survivability and node compatibility constraints: Korean HiNET‐P case

In this paper we present heuristic algorithms to solve the combined problem of topology design, capacity assignment and routing for feeder transport networks that connect local access subnets to a packet‐switched backbone network. The algorithms are designed to build a two‐edge‐connected topology with the average packet delay constraint, switch throughput constraint, and two optional, additional constraints: dual homing and node compatibility requirements. Graph‐theoretic algorithms are used for topology design and the flow deviation algorithm is used for routing and capacity assignment. Computational results using Korea Telecom’s field data show that the algorithms can generate low‐cost feasible networks for 50 node problems within 20 s. This revised version was published online in August 2006 with corrections to the Cover Date.     

Rate control-based framework and algorithm for optimal provisioning

Ideally, networks should be designed to accommodate a variety of different traffic types, while at the same time maximizing its efficiency and utility. Network utility maximization (NUM) serves as an effective approach for solving the problem of network resource allocation (NRA) in network analysis and design. In existing literature, the NUM model has been used to achieve optimal network resource allocation such that the network utility is maximized. This is important, since network resources are at premium with the exponential increase in Internet traffic. However, most research work considering network resource allocation does not take into consideration key issues, such as routing and delay. A good routing policy is the key to efficient network utility, and without considering the delay requirements of the different traffic, the network will fail to meet the user’s quality of service (QoS) constraints. In this paper, we propose a new NUM framework that achieves improved network utility while taking into routing and delay requirements of the traffic. Then, we propose a decomposition technique-based algorithm, D-NUM, for solving this model efficiently. We compare our approach with existing approaches via simulations and show that our approach performs well.