James Yiming Zhang

Optimization of Semi-Dynamic Lightpath Rearrangements in a WDM Network

In this paper, we study the routing and wavelength assignment (RWA) problem in a semi-dynamic scenario where rearrangements are conducted in a series of sessions after traffic demands vary. Unlike pure static RWA problems, each rearrangement scheme must consider established lightpaths in the previous session. A novel formulation of the WDM network rearrangement problem is used to minimize rejected new demands and rerouted lightpaths. This is done by coordinating the re-routing of existing lightpaths with the adaptation to varying demands. The Lagrangean Relaxation and Subgradient Method (LRSM) has been successfully used to solve the problem along with fairness consideration. The superior performance and reduced computation complexity of our algorithm are demonstrated in sample networks. In addition, we evaluate the benefit of using wavelength converters in a WDM network rearrangement. In contrast to previous studies with conclusions that wavelength converters are of little value in the static RWA problem, we show that wavelength converters improve network performance in a WDM network rearrangement.

Lightpath Scheduling and Routing for Traffic Adaptation in WDM Networks

We study the benefits and trade-offs of using scheduled lightpaths for traffic adaptation. We propose a network planning model that allows lightpaths to slide within their desired timing windows with no penalty on the optimization objective and to slide beyond their desired timing windows with a decreasing tolerance level. Our model quantitatively measures the timing satisfactions or violations. We apply the Lagrangian relaxation and subgradient methods to the formulated optimization problem, with which great computational efficiency is demonstrated when compared with other existing algorithms. Our simulation results show how timing flexibility improves network resource utilization and reduces rejections.

Lightpath scheduling and routing for green data centres

Optical networking technologies enable data centres to be located near sources of green energy (i.e., renewable energy). Since some green energy sources are intermittent and are not always available, we need to dynamically connect distribution networks to the green energy powered data centres. On the other hand, the availability of green energy is reasonably predictable, and we are thus able to schedule connectivity to data centres in advance. We propose a WDM network planning model, which allows lightpaths to slide within their desired timing windows with no penalty on the optimization objective, and to slide beyond their desired timing windows with a deteriorating “green-level”. Our simulation results show the tradeoffs between the consumption of brown energy (i.e., energy generated by carbon-intensive means), the capability of providing required connectivity to data centres, network resource utilization, and overall operation objective.

A proof of wavelength conversion not improving the lagrangian bound of the static RWA problem

The fact that wavelength conversion hardly improves the performance of static routing and wavelength assignment (RWA) in wavelength division multiplexing (WDM) networks has been observed in many previous studies. However, other than simulation results, until now there was no formal proof of such fact. In this paper, we formally prove that wavelength conversion does not improve the Lagrangian bound of the static RWA problem.

Grade-of-service differentiated static resource allocation schemes in WDM networks

This paper presents a study on the Grade-of-Service (GoS) differentiation of static resource allocation in lightpath routed WDM networks, where lightpath requests between node pairs are given. Each request is associated with a service grade. The goal is to maintain certain service levels for the requests of all grades. The service levels are measured in terms of their acceptance ratios. We solve this network optimization problem by adopting a penalty-based framework, in which network design and operation goals can be evaluated based on cost/revenue. We propose a static GoS differentiation model as one minimizing the total rejection and cost penalty, in which the rejection penalty reflects the revenue of accepting a request, and the cost penalty reflects the resource consumption of providing a lightpath to a request. Then, a solution based on the Lagrangian relaxation and subgradient methods is used to solve the proposed optimization problem. Three different application scenarios are presented: static GoS differentiation of requests between the same node pair, static GoS differentiation of requests between different node pairs, and an integration of static GoS differentiation into the network profit objective. The fairness issues and the impact of relative penalty factors are discussed to provide guidelines for network planning.

Resource Criticality Analysis of Static Resource Allocations and Its Applications in WDM Network Planning

Various static resource allocation algorithms have been used in WDM networks to allocate resources such as wavelength channels, transmitters, receivers, and wavelength converters to a given set of static lightpath demands. However, although optimized resource allocations can be obtained, it remains an open issue how to determine which resources are the bottlenecks in achieving better performance. Existing static resource allocation algorithms do not explicitly measure the impact of changes of network resources or lightpath demands on the design objective. We propose such a measurement based on the Lagrangian relaxation framework. We use the optimized values of Lagrange multipliers as a direct measurement of the criticality of resources. Such a quantitative measurement can be naturally acquired along with the optimization process to obtain the optimal solution (or a near-optimal solution) to the static routing and wavelength assignment problem. We investigate three practical applications of the resource criticality (RC) analysis in WDM network planning. In the first application, we use our proposed measurement to identify critical resources and thus to decide the best way to add or reallocate resources. In the second application, we estimate the impact of the addition or removal of lightpath demands on the design objective. This kind of estimation helps to set a proper price for lightpath demands. In the third application, the results of the RC analysis are used to speed up the convergence of the optimization process for different network scenarios.

A proof of wavelength conversion not improving Lagrangian bounds of the sliding scheduled RWA problem

Extensive previous studies confirmed that wavelength conversion may only marginally improve the solutions to the static Routing and Wavelength Assignment (RWA) problem. This means that, for the static RWA problem, certain RWA schemes that do not use wavelength conversion can achieve a performance almost as good as the one from the best RWA scheme. Previous research work on sliding scheduled RWA problems, where a given set of lightpath demands are allowed to slide within their time windows, has also indicated in limited simulation results that the benefit of using wavelength conversion is marginal. However, the observation cannot be conclusive without the solid mathematical proof. We are thus motivated to investigate whether schedule sliding really requires wavelength conversion to achieve a better performance. In this paper, we prove that wavelength conversion does not improve the Lagrangian bound of the sliding scheduled RWA problem. In most test cases, this bound is very close to the best achieved objective function value. Our proof implies that, for those cases, the improvements achieved by making use of wavelength conversion are very marginal.

Forward-Looking WDM Network Reconfiguration with Per-Link Congestion Control

We study reconfigurations of wavelength-routed Wavelength Division Multiplexing (WDM) networks in response to lightpath demand changes, with the objective of servicing more lightpath demands without additional network resources from a long-term network operation point of view. For the reconfiguration problem under study, we assume WDM network operators are provided with lightpath demands in batches. With limited network resources, our problem has two unique challenges: balancing network resource allocations between current and future lightpath demands, and modeling future lightpath demands. The first challenge implies making tradeoffs between accepting as many current immediate lightpath demands as possible and reserving a certain amount of network resources for near future predicted lightpath demands. The second challenge implies modeling future predicted lightpath demands, which are not exactly known or certain as the current lightpath demands. Our proposed model allows a natural separation between the operation of the optical layer and the user traffic layer (predominantly the IP-layer), while supporting their interactions, for which we propose a new formulation for per-link congestion control, associated with a mathematical solution procedure. Our simulation results reveal that by properly controlling resource allocations in the current session using our proposed mechanism, rejections in future sessions are greatly reduced.

Corrections to "Lightpath (Wavelength) Routing in Large WDM Networks" and "Dynamic Routing and Assignment of Wavelength Algorithms in Multifiber Wavelength Division Multiplexing Networks"

In this comment, several errors in Chlamtac et al., 1996, Lightpath (Wavelength) Routing in Large WDM Networks and one error in Xu et al., 2000, Dynamic Routing and Assignment of Wavelength Algorithms in Multifiber Wavelength Division Multiplexing Networks are pointed out. We present corrections to the errors.

Differentiated Static Resource Allocation in WDM Networks

We present a study on the static resource allocation in lightpath routed WDM networks, where each request is associated with a service grade. The goal is to maintain certain acceptance ratios for the requests of all grades, as well as to minimize the resource consumption. We propose a model of static Grade-of-Service (GoS) differentiation as minimizing the total rejection and cost penalty. Then, we use the Lagrangian relaxation and subgradient methods to solve the problem. The results of using static GoS differentiation are presented.