Topological design,routing and capacity dimensioning for ISL networks in broadband LEO satellite systems

This paper considers the network design of intersatellite link (ISL) networks in broadband LEO satellite systems, where the major challenge is the topology dynamics. First, a general method to design convenient ISL topologies for connection‐oriented operation is presented, and a reference topology for numerical studies is derived. A permanent virtual topology is then defined on top of the orbiting physical one, thus forming a framework for discrete‐time dynamic traffic routing. On this basis, heuristic and optimization approaches for the combined routing and dimensioning task, operating on discrete time steps, are presented and their performance is numerically compared. It is shown that minimizing the worst‐case link capacity is an appropriate target function, which can be formulated as linear optimization problem with linear constraints. Using linear programming (LP) techniques, the dimensioning results are clearly better than with simple heuristic approaches. Copyright © 2001 John Wiley & Sons, Ltd.     

Upper bounding service capacity in multi-hop wireless SSMA-based ad hoc networks

Upper bounds on the service carrying capacity of a multi-hop, wireless, SSMA-based ad hoc network are considered herein. The network has a single radio band for transmission and reception. Each node can transmit to, or receive from, multiple nodes simultaneously. We formulate the scheduling of transmissions and control of transmit powers as a joint, mixed-integer, nonlinear optimization problem that yields maximum return at minimum power subject to SINR constraints. We present an efficient tabu search-based heuristic algorithm to solve the optimization problem and rigorously assess the quality of the results. Through analysis and simulation, we establish upper bounds on the VoIP call carrying capacity of the network as function of various parameters. We discuss the pros and cons of using SSMA as a spectrum sharing technique in wireless ad hoc networks.     

Design of logical topologies: a linear formulation forwavelength-routed optical networks with no wavelength changers

We consider the problem of constructing logical topologies over a wavelength-routed optical network with no wavelength changers. We present a general linear formulation which considers routing traffic demands, and routing and assigning wavelengths to lightpaths, as a combined optimization problem. The formulation also takes into account the maximum number of hops a lightpath is permitted to take, multiple logical links in the logical topology, multiple physical links in the physical topology, and symmetry/asymmetry restrictions in designing logical topologies. The objective is to minimize congestion. We show by examples how equality and inequality logical degree constraints have a bearing on congestion. We prove that, under certain conditions, having equality degree constraints with multiple edges allowed in the design of logical topologies does not affect congestion. This helps in reducing the dimensionality of the search space and hence speeds up the search for an optimal solution of the linear formulation. We solve the linear formulation for small examples and show the tradeoff between congestion, number of wavelengths available and the maximum number of hops a lightpath is allowed to take. For large networks, we solve the linear formulation by relaxing the integer constraints. We develop topology design algorithms for large networks based on rounding the solutions obtained by solving the relaxed problem. Since the whole problem is linearizable, the solution obtained by relaxation of the integer constraints yields a lower bound on congestion. This is useful in comparing the efficiency of our heuristic algorithms. Following Bienstock and Gunluk (1995), we introduce a cutting plane which helps in obtaining better lower bounds on congestion and also enables us to reduce the previously obtained upper bounds on congestion     

Bounds on the reliability of distributed systems with unreliable nodes & links

The reliability of distributed systems & computer networks in which computing nodes and/or communication links may fail with certain probabilities have been modeled by a probabilistic network. Computing the residual connectedness reliability (RCR) of probabilistic networks under the fault model with both node & link faults is very useful, but is an NP-hard problem. Up to now, there has been little research done under this fault model. There are neither accurate solutions nor heuristic algorithms for computing the RCR. In our recent research, we challenged the problem, and found efficient algorithms for the upper & lower bounds on RCR. We also demonstrated that the difference between our upper & lower bounds gradually tends to zero for large networks, and are very close to zero for small networks. These results were used in our dependable distributed system project to find a near-optimal subset of nodes to host the replicas of a critical task.     

A Lagrangean relaxation based near-optimal algorithm for advance lightpath reservation in WDM networks

Advance lightpath reservation is a new research topic for connecting high-speed computer servers in lambda grid applications and for dynamic lightpath provisioning in the future optical internet. In such networks, users make call requests in advance to reserve network resources for communications. The challenge of the problem comes from how to jointly determine call admission control, lightpath routing, and wavelength assignment. In this paper, we propose an efficient Lagrangean relaxation (LGR) approach to resolve advance lightpath reservation for multi-wavelength optical networks. The task is first formulated as a combinatorial optimization problem in which the revenue from accepting call requests is to be maximized. The LGR approach performs constraint relaxation and derives an upper-bound solution index according to a set of Lagrangean multipliers generated through subgradient-based iterations. In parallel, using the generated Lagrangean multipliers, the LGR approach employs a new heuristic algorithm to arrive at a near-optimal solution. By upper bounds, we assess the performance of LGR with respect to solution accuracy. We further draw comparisons between LGR and three heuristic algorithms—Greedy, First Come First Serve, and Deadline First, via experiments over the widely-used NSFNET network. Numerical results demonstrate that LGR outperforms the other three heuristic approaches in gaining more revenue by receiving more call requests.     

Slot allocation strategies for delay sensitive traffic support in asynchronous ad hoc wireless networks

Supporting real‐time traffic in ad hoc wireless networks is considered as a challenging problem. Existing bandwidth reservation mechanisms assume a TDMA environment where achieving time synchronisation is expensive in terms of resources. Heuristics that exist for slot allocation schemes assume a CDMA over TDMA model in order to alleviate the presence of hidden terminals. Slot allocation strategies in the presence of hidden terminals assume significance in a single channel system for supporting delay sensitive traffic. In this paper, we propose three heuristics for the slot allocation process in asynchronou single channel multihop wireless networks in the presence of hidden terminals. The heuristics we propose are the early fit reservation (EFR), minimum bandwidth‐based reservation (MBR) and position‐based hybrid reservation (PHR). The EFR heuristic assigns bandwidth link‐by‐link in the forward path. The MBR heuristic allocates bandwidth to the links in the increasing order of free conn‐slots. The PHR heuristic assigns bandwidth for every link proportional to its position in the path. Simulation studies show that EFR performs better in terms of delay characteristics. MBR provides better call blocking performance at the cost of high end‐to‐end delay. PHR provides a better delay performance compared to MBR and better call blocking performance comparedto EFR. Copyright © 2004 John Wiley & Sons, Ltd.     

Optimum Integrated Link Scheduling and Power Control for Multihop Wireless Networks

In this paper, a new mathematical programming formulation is developed for minimizing the schedule length in multihop wireless networks based on the optimal joint scheduling of transmissions across multi-access communication links and the allocation of transmit power levels while meeting the requirements on the signal-to-interference-plus-noise ratio at intended receivers. The authors prove that the problem can be represented as a mixed-integer linear programming (MILP) and show that the latter yields a solution that consists of transmit power levels that are "strongly Pareto optimal". It was demonstrated that the MILP formulation can be used effectively to derive optimal scheduling and power levels for networks with as many as 30 designated communication links. The authors show that the MILP formulation can also be effectively solved to provide upper and lower bounds (corresponding to an approximation factor Delta) for the optimum schedule length of networks with as many as 100 designated links. It is proved that the integrated link scheduling and power control problem (ILSP) is NP-complete. Consequently, a heuristic algorithm of polynomial complexity is developed and investigated for solving the problem in a timely and practical manner. The algorithm is based on the properties of a novel interference graph, i.e., the "generalized power-based interference graph", whose "chromatic" and "independence numbers" provide fundamental bounds for the ILSP. It is demonstrated that the frame length of schedules realized by the heuristic scheme resides in the 25th percentile of those attained by the optimal mechanism for randomly generated topologies with as many as 30 designated communication links. Furthermore, it is shown that the algorithm significantly outperforms a corresponding algorithm presented in the literature     

An analytical framework for remote sensing satellite networks based on the model predictive control with convex optimization

In this paper, we consider the problem of maximizing the throughput of remote sensing satellite networks. In such networks, the link capacities and routing matrices are varying over time. We propose a convex optimization‐based analytical framework for the problem. To maximize the network throughput under the premise of satisfying the delay constraint, we formulate the data transmission schedule into an optimization problem aiming at maximizing the delay‐constrained throughput. Considering the fact that the future link capacities cannot be accurately known in the actual situation, we propose a heuristic and distributed framework on the basis of model predictive control for approximately solving the problem. This framework can be used to design remote sensing data transmission schedules under various scenarios. We adopt a generic example to simulate and analyze the framework. The simulation results show that the proposed analytic framework can obtain the approximate solution that is very close to the optimal solution by solving the convex optimization problem step‐by‐step. The heuristic algorithm based on model predictive control can obtain the approximate solution, which is very close to the optimal solution in distributed scenario.     

A system for routing and capacity assignment in computercommunication networks

The combined problem of selecting a primary route for each communicating pair and a capacity value for each link in computer communication networks is considered. The network topology and traffic characteristics are given: a set of candidate routes and of candidate capacities for each link are also available. The goal is to obtain the least costly feasible design where the costs include both capacity and queuing components. Lagrangean relaxation and subgradient optimization techniques were used to obtain verifiable solutions to the problem. The method was tested on several topologies, and in all cases good feasible solutions, as well as tight lower bounds, were obtained. The model can be generalized to deal with different classes of customers, characterized by different priorities, message lengths, and/or delay requirements     

Lagrangean Based Methods for Solving Large-Scale Cellular Network Design Problems

The cellular network design (CND) problem is formulated as a comprehensive linear mixed integer programming model integrating the base station location (BSL) problem, the frequency channel assignment (FCA) problem and the topological network design (TND) problem. A solution algorithm based on Lagrangean relaxation is proposed for solving this complex cellular network design problem. Pursuing the optimum solution through exact algorithms to this problem appears to be unrealistic considering the large scale nature and NP-hardness of the problem. Therefore, the solution algorithm strategy consists in computing effective lower and upper bounds for the problem. Lower bounds are evaluated through a Lagrangean relaxation technique and subgradient method. A Lagrangean heuristic is developed to compute upper bounds based on the Lagrangean solution. The bounds are improved through a customized branch and bound algorithm which takes in account specific knowledge of the problem to improve its efficiency. Thirty two random test instances are solved using the proposed algorithm and the CPLEX optimization package. The results show that the duality gap is excessive, so it cannot guarantee the quality of the solution. However, the proposed algorithm provides optimal or near optimal solutions for the problem instances for which CPLEX also provides the optimal solution. It further suggests that the proposed algorithm provides optimal or near optimal solutions for the other instances too. Finally, the results demonstrate that the proposed algorithm is superior to CPLEX as a solution approach for the CND problem.     

Shared Risk Link Group (SRLG)-Diverse Path Provisioning Under Hybrid Service Level Agreements in Wavelength-Routed Optical Mesh Networks

The static provisioning problem in wavelength-routed optical networks has been studied for many years. However, service providers are still facing the challenges arising from the special requirements for provisioning services at the optical layer. In this paper, we incorporate some realistic constraints into the static provisioning problem, and formulate it under different network resource availability conditions. We consider three classes of shared risk link group (SRLG)-diverse path protection schemes: dedicated, shared, and unprotected. We associate with each connection request a lightpath length constraint and a revenue value. When the network resources are not sufficient to accommodate all the connection requests, the static provisioning problem is formulated as a revenue maximization problem, whose objective is maximizing the total revenue value. When the network has sufficient resources, the problem becomes a capacity minimization problem with the objective of minimizing the number of used wavelength-links. We provide integer linear programming (ILP) formulations for these problems. Because solving these ILP problems is extremely time consuming, we propose a tabu search heuristic to solve these problems within a reasonable amount of time. We also develop a rerouting optimization heuristic, which is based on previous work. Experimental results are presented to compare the solutions obtained by the tabu search heuristic and the rerouting optimization heuristic. For both problems, the tabu search heuristic outperforms the rerouting optimization heuristic.     

Bounds on capacity and minimum energy-per-bit for AWGN relay channels

Upper and lower bounds on the capacity and minimum energy-per-bit for general additive white Gaussian noise (AWGN) and frequency-division AWGN (FD-AWGN) relay channel models are established. First, the max-flow min-cut bound and the generalized block-Markov coding scheme are used to derive upper and lower bounds on capacity. These bounds are never tight for the general AWGN model and are tight only under certain conditions for the FD-AWGN model. Two coding schemes that do not require the relay to decode any part of the message are then investigated. First, it is shown that the "side-information coding scheme" can outperform the block-Markov coding scheme. It is also shown that the achievable rate of the side-information coding scheme can be improved via time sharing. In the second scheme, the relaying functions are restricted to be linear. The problem is reduced to a "single-letter" nonconvex optimization problem for the FD-AWGN model. The paper also establishes a relationship between the minimum energy-per-bit and capacity of the AWGN relay channel. This relationship together with the lower and upper bounds on capacity are used to establish corresponding lower and upper bounds on the minimum energy-per-bit that do not differ by more than a factor of 1.45 for the FD-AWGN relay channel model and 1.7 for the general AWGN model.     

Optimization for Fault Localization in All-Optical Networks

Fault localization is a critical issue in all-optical networks. The limited-perimeter vector matching (LVM) protocol is a novel fault-localization protocol proposed for localizing single-link failures in all-optical networks. In this paper, we study the optimization problems in applying the LVM protocol in static all- optical networks. We consider two optimization problems: one is to optimize the traffic distribution so that the fault-localization probability in terms of the number of localized links is maximized, and the other is to optimize the traffic distribution so that the time for localizing a failed link is minimized. We formulate the two problems into an integer linear programming problem, respectively, and use the CPLEX optimization tool to solve the formulated problems. We show that by optimizing the traffic distribution the fault-localization probability can be maximized and the fault-localization time can be minimized. Moreover, a heuristic algorithm is proposed to evaluate the optimization results through simulation experiments.     

Wavelength and Waveband Assignment for Ring Networks Based on Parallel Multi‐granularity Hierarchical OADMs

In this paper we study the optimization issues of ring networks employing novel parallel multi‐granularity hierarchical optical add‐drop multiplexers (OADMs). In particular, we attempt to minimize the number of control elements for the off‐line case. We present an integer linear programming formulation to obtain the lower bound in optimization, and propose an efficient heuristic algorithm called global bandwidth resource assignment that is suitable for the design of large‐scale OADM networks.     

Lifetime maximization schemes under end‐to‐end frame‐error constraints in wireless multimedia sensor networks

Of significance in wireless multimedia sensor networks (WMSN) is the maintenance of media quality and the extension of route lifetime since media stream is more sensitive in quality requirement than data flow. In this paper, the problem of how to balance the needs on constraining end‐to‐end (e2e) quality and prolonging lifetime in an established route can be interpreted as a nonlinear optimization paradigm, which is then shown to be a max—min composite formulation when an e2e frame‐error probability is given. To solve this max—min problem, we propose two novel methods: route‐associated power management (RAPM) and link‐associated power management (LAPM). For computation‐restricted sensor nodes, the RAPM scheme with adding a simplification condition on power management can effectively reduce the power cost at computation and also rapidly determine optimum lifetime from numerous candidate routes. On the other hand, if computing power is not the major concern in a sink node, rather than using a heuristic method, we employ the LAPM algorithm to solve the lifetime maximization problem in a more accurate fashion. Solid theoretical analysis and simulation results are presented to validate our proposed schemes. Both analytical and simulation results demonstrate that the LAPM scheme is very comparable to the heuristic approach. Copyright © 2009 John Wiley & Sons, Ltd.     

Capacity Planning of DiffServ Networks with Best-Effort and Expedited Forwarding Traffic

For networks providing a specific level of service guarantees, capacity planning is an imperative part of network management. Accurate dimensioning is especially important in DiffServ networks, where no per-flow signaling or control exists. In this paper, we address the problem of capacity planning for DiffServ networks with only Expedited Forwarding (EF) and best effort (BE) traffic classes. The problem is formulated as an optimization problem, where the total link cost is minimized, subject to the performance constraints of both EF and BE classes. The edge to edge EF demand pairs and the BE demands on each link are given. The variables to be determined are the non-bifurcated routing of EF traffic, and the discrete link capacities. We show that Lagrangian relaxation and subgradient optimization methods can be used to effectively solve the problem. Computational results show that the solution quality is verifiably good while the running time remains reasonable on practical-sized networks. This represents the first work for capacity planning of multi-class IP networks with non-linear performance constraints and discrete link capacity constraints.     

Lagrangean relaxation for service location in large‐scale networks with QoS constraints

Current trends in computing indicate that there is a great potential for service‐oriented computing and similar technologies, such as application‐oriented networks (AONs), where services can relocate to adapt to the conditions of the underlying network. In such environments, providing and consuming services and establishing a relationship between consumers (users of services) and producers (providers of services) are still challenging and vastly researched aspects. Bearing this in mind, we define a service location and planning (SLP) problem that uniquely matches producers to consumers and accounts for realistic parameters such as, quality of service (QoS) constraints of throughput and delay, and network constraints of underlying link layer bandwidth capacities, and cost of meeting consumer requests. Our contribution lies in the mathematical formulation of the SLP problem as an integer linear programming (ILP) problem that can be solved optimally for small‐scale networks and extending this work using Lagrangean relaxation (LR) approximation techniques to solve the SLP problem for large‐scale networks. Copyright © 2009 John Wiley & Sons, Ltd.     

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.     

Optimal scheduling of coordinated multipoint transmissions in cellular networks

In this paper, we study the optimal scheduling problem in coordinated multipoint (CoMP) transmission–based cellular networks. We consider joint transmission and coordinated scheduling together in CoMP transmission–based cellular networks and develop an optimization framework to compute the optimal max‐min throughput and the optimal scheduling of the transmissions to the users. The optimization problem is found to be a complex linear program with number of variables in for a cellular network of N users and K cells. We solve the optimization problem for several network instances using an optimization tool. The numerical results show that the optimal CoMP transmission provides a significant throughput gain over a traditional transmission. We find that in optimal scheduling the fraction time of coordinated scheduling is higher than that of joint transmission. To solve the optimization problem without any optimization tool, we propose a heuristic algorithm. The performance of the heuristic algorithm is evaluated and found to be provided throughput around 97% of the optimal throughput. Further, we extend the optimization framework to study joint scheduling and power allocation (JSPA) problem in CoMP transmission–based cellular networks. We numerically solve the JSPA problem for the network instances and demonstrate that the optimal power allocation at the base stations is not binary for a significant fraction of time of scheduling. However, the gain in max‐min throughput by the optimal JSPA technique over the optimal scheduling technique is not significant.     

Link scheduling with power control for throughput enhancement in multihop wireless networks

Joint scheduling and power control schemes have previously been proposed to reduce power dissipation in wireless ad hoc networks. However, instead of power consumption, throughput is a more important performance concern for some emerging multihop wireless networks, such as wireless mesh networks. This paper examines joint link scheduling and power control with the objective of throughput improvement. The MAximum THroughput link Scheduling with Power Control (MATH-SPC) problem is first formulated and then a mixed integer linear programming (MILP) formulation is presented to provide optimal solutions. However, simply maximizing the throughput may lead to a severe bias on bandwidth allocation among links. To achieve a good tradeoff between throughput and fairness, a new parameter called the demand satisfaction factor (DSF) to characterize the fairness of bandwidth allocation and formulate the MAximum Throughput fAir link Scheduling with Power Control (MATA-SPC) problem is defined. An MILP formulation and an effective polynomial-time heuristic algorithm, namely, the serial linear programming rounding (SLPR) heuristic, to solve the MATA-SPC problem are also presented. Numerical results show that bandwidth can be fairly allocated among all links/flows by solving the MILP formulation or by using the heuristic algorithm at the cost of a minor reduction of network throughput. In addition, extensions to end-to-end throughput and fairness and multiradio wireless multihop networks are discussed.