To satisfy the stringent requirements of emerging broadband services in home networks, a novel Centralized optical and Wi-Fi Access Network (C-WAN) has been proposed within the context of Fiber-to-The-Room (FTTR). In C-WAN, centralized management and control of multiple Wi-Fi access points (APs) deployed in each room are facilitated by relocating portions of Wi-Fi protocols from the APs to a centralized entity. This approach significantly enhances network performance, including throughput and roaming capabilities. However, C-WAN also imposes strict demands on the fronthaul networks, specifically requiring high bandwidth and ultra-low latency. In this context, orthogonal frequency division multiplexing passive optical network (OFDM-PON) emerges as a promising solution to support the C-WAN fronthaul network by allocating dedicated subcarriers to each AP. In C-WAN over OFDM-PON, Wi-Fi stations still contend for access to the wireless channel based on existing Wi-Fi protocols, which may result in prolonged wireless access delays. Consequently, the Quality of Service (QoS) requirements for time-sensitive (TS) services may not be met. Additionally, the variation in maximum Wi-Fi throughput due to the contention-based access mechanism presents a significant challenge for the efficient allocation of optical network resources under stringent delay constraints. To address these issues, we propose a priority-based access mechanism that assigns higher priority to TS services for accessing Wi-Fi channels and obtaining wireless resources. Building on this mechanism, we further develop a Wi-Fi throughput prediction model, which is used to optimize the allocation of optical network resources. Simulation results demonstrate that the proposed scheme can effectively reduce wireless access delay and jitter for TS services, meeting their performance requirements while also improving the utilization of optical network resources.

Hyperspectral images (HSIs) and digital surface models (DSMs) derived from light detection and ranging (LiDAR) data are packed with rich on-ground object characteristics, making their joint classification crucial for accurate remote sensing (RS) identification. However, variations in illumination properties hinder efficient and precise crossmodal information interaction. This phenomenon further exacerbates the difficulty in correctly identifying corresponding spectral curves, ultimately resulting in suboptimal performance. In this paper, a hybrid updating graph motivated illumination-wise property unification network (HugIpuNet) is proposed to solve these challenges. Our core motivation is to unify global illumination descriptions with assistance from elevations for capturing precise multimodal properties from HSIs and DSMs derived from LiDAR data. HS spectral curves and LiDAR elevations interact constantly, considering their geological connection to strengthen their coupling capability. HugIpuNet further considers several illumination-wise mechanisms to address intricate and variable environmental conditions. The whole structure utilizes various hybrid updating graphs to enhance the attribute unification ability in complex environmental scenarios. Dynamic adjacent matrices are constructed to introduce uncertainty to limit the effect caused by description distortions. Experiments show an average 2.86% improvement in overall accuracy (OA) compared with multiple state-of-the-art approaches. The detailed datasets and codes will be available at https://github.com/weixinttt/HugIpuNet.

Hyperspectral (HS) and light detection and ranging (LiDAR) fusion for land-cover identification in multimodal tasks is always restricted owing to modality heterogeneity, especially when annotated samples are unavailable. Clustering techniques become pivotal in these unsupervised scenarios, yet their inability to offer interpretable fusion mechanisms poses challenges in pursuing enhanced identification performance. In this article, an Intuitive Assimilation Modality-driven Crossmodal Subspace Clustering (IamCSC) method is proposed to exploit intuitive crossmodal information. Different from existing multiview clustering approaches, our core innovation is that IamCSC distills an intuitive assimilation modality (IAM) with distinguishing geological characteristics of HS and LiDAR images simultaneously. The IAM with a small number of IAM channels still preserves the spectral similarity and diversity of neighboring samples. Meanwhile, it reflects sample elevations by peak regions along the IAM channel dimension. Specifically, the IAM connects and balances these two RS modalities to learn an assimilation cluster structure from modality-specific subspace representations. Several matrix-wise constraints are considered for capturing the optimal modality-shared representation. Given that IamCSC does not exhibit over-reliance on spatial neighborhood contributions, it achieves satisfactory performance particularly in image pairs with larger ground sampling distances (GSDs). Multiple experiments clearly prove the effectiveness of IamCSC over state-of-the-art techniques qualitatively and quantitatively. The codes are provided inhttps://github.com/GEOywb/IamCSC.

Numerous deep learning (DL) studies have indicated that fusing hyperspectral (HS) and light detection and ranging (LiDAR) data is effective for land-cover classification. However, the sequential characteristics (seqCHAs) in the spatial domain are always ambiguous and neglected. In this letter, we propose a deep crossmodal sequential interaction network (CsiNet) for HS and LiDAR data joint classification. We aim to verify the contributions of crossmodal seqCHAs in multimodal joint classification tasks and present an effective crossmodal sequential flattening (SF) strategy. Specifically, CsiNet sorts the neighboring samples in terms of the spectral and 3-D spatial diversities between the corresponding samples and the central one. Notably, the 3-D spatial diversity considers the shared sample positions in both modalities and the sample elevations in LiDAR data simultaneously. CsiNet is capable of extracting crossmodal sequential features comprehensively by long and short-term memory (LSTM) layers and better simulating sequential properties of samples compared with convolutional layer based networks. Experiments conducted on the Muufl Gulfport (MUUFL) and Houston 2013 datasets prove that CsiNet outperforms several state-of-the-art techniques qualitatively and quantitatively. When using 1% training samples per category, the overall accuracies of CsiNet on both datasets achieve 90.27% and 92.41% and are increased by 0.27% and 0.17% than the best comparison technique, respectively. Ablation experiments verify the effectiveness of CsiNet by replacing the crossmodal SF strategy with several alternative ones. All codes are available athttps://github.com/GEOywb/CsiNet.

This paper addresses the virtual network (VN) mapping problems for the network profit optimization in space-division-multiplexing optical data center networks (SDM-ODCNs). We first define both link resource availability (LRA) and node resource availability (NRA) for the VN mapping optimization, by which an integer linear program (ILP) model and two VN mapping approaches are proposed to achieve the high network profit. Simulation results verify that our proposed LRA VN mapping approach achieves greatly close network performance to that by solving solutions of the integer linear program model and significantly outperforms its counterpart approaches. The improved network profits out of the VN mapping is as a result of well suppressed average crosstalk, rejection ratio of VNs, and spectrum fragmentation ratio in SDM-ODCNs.

Fusing hyperspectral (HS) and light detection and ranging (LiDAR) data is capable of enhancing land-cover interpretation capability in multimodal remote sensing (RS) tasks. Nevertheless, nonuniform surrounding illumination in real-world capturing scenarios tends to distort the spectral curves of on-ground objects, inevitably resulting in poor classification performance. This nonuniform illumination further interferes with the cross-modal RS information interaction procedure. In this article, an Illumination Insensitive Modalitywise Assimilation guided Multistage Interaction network (MAMInet II) is proposed to tackle this challenge. Our primary motivation is to eliminate complicated illumination interference (CII) and capture high-level modalitywise assimilation information (MAI) from HS and LiDAR data simultaneously. Notably, the geological connection between these two RS modalities is emphasized in MAMInet II, making the joint classification framework interpretable. Specifically, MAMInet II presents one channelwise illumination removal mechanism by simulating real-world illumination conditions and associating HS spectral curves with LiDAR elevations. One generative switching mechanism contributes to closing the cross-modal RS distribution gap and enhancing the cross-modal capturing ability. These mechanisms are extended to multiscale and multistage versions for aligning local and global RS properties. Experiments illustrate that MAMInet II outperforms several state-of-the-art techniques qualitatively and quantitatively. All source codes are available athttps://github.com/weixinttt/MAMInet-II.

Emerging machine learning techniques enable Internet-connected devices to generate service function chain (SFC) requests for reliability-sensitive applications. To facilitate reliable SFC provisioning, prior works have proposed the SFC dedicated protection approach for fog and cloud networks, which generally have abundant resources and connectivity. However, with limited resources and connectivity, it might be inefficient to directly employ these approaches at the network edge. In this work, we study how to efficiently embed and provide dedicated protection when accommodating SFCs at the network edge. We formally define the problem of SFC embedding with dedicated protection (SFCE-DP) to minimize the bandwidth resources usage at the network edge. Based on the proposed technique of augmenting path in a layered graph, we construct an efficient algorithm, called augmenting-path-based SFC embedding with dedicated protection (AP-SDP), to optimize SFCE-DP. When the network resources are limited, our results show that AP-SDP significantly outperforms the benchmark that is directly extended from the state-of-the-art.

By overcoming the “server box” barrier, resource disaggregation in data centers (DCs) can significantly improve resource utilization. This may then provide a more cost-efficient approach for resource upgrade and expansion. The advantages of resource disaggregation have been explored in earlier research to improve the efficiency of resource usage. This paper investigates the potential benefits of resource disaggregation from the aspect of reliability, which has not been considered before. Resource disaggregation gives rise to a new failure pattern. For example, in a conventional server, the failure of one type of resource leads to the failure of the entire server, so that other types of resources in the same server also become unavailable. After disaggregating, the failure of different types of resources becomes more isolated so that other resources are still available. In this paper, we model the reliability of a resource allocation request in a server-based or disaggregated DC based on whether the request is allocated with only working resources or is also provisioned with backup resources. We then consider a resource allocation problem to maximize the number of requests accepted with guaranteed reliability. This is formulated as an integer linear programming (ILP) problem, and a more straightforward heuristic approach is also proposed. Our numerical studies demonstrate that it may be possible to significantly improve service reliability with this resource disaggregation approach.

In multimodal data fusion and land-cover interpretation tasks, the fusion interpretability between hyperspectral image (HSI) and light detection and ranging (LiDAR) data is always nontrivial to be clarified. Furthermore, the heterogeneous sample and distribution variances of these two remote sensing (RS) modalities impede the joint classification performance. In this paper, a Hyperspectral Intrinsic Image Decomposition guided Data Fusion Network (HI2D2FNet) is proposed. Generally, classic hyperspectral intrinsic image decomposition (HIID) performs well in image enhancement and shadow removal. It decomposes one HSI into one reflectance component and one shading component. Inspired by the core mechanism of HIID, our motivation is to preliminarily exploit its potential for multimodal RS data fusion and explore the inherent modality connection between HSI and LiDAR data from the intrinsic perspective. Specifically, compared with existing techniques, HI2D2FNet is capable of fusing the horizontal geometry information in the shading component with the vertical geometry information in the LiDAR data from both sample and distribution perspectives in the spatial domain. The generated cross-modal geometry feature contributes to guiding the reflectance stream optimization. This unique fusion framework connects both modalities with respect to geometry information and enhances the specific fusion interpretability. The decomposition and fusion modules in HI2D2FNet are optimized simultaneously in a novel alternative optimization pattern. Furthermore, several unique cross-modal constraints in terms of prior RS properties are presented. Experiments conducted on three widely available datasets prove the superiority of HI2D2FNet over state-of-the-art techniques. The source codes will be available at https://github.com/GEOywb/HI2D2FNet.

Remote sensing (RS) modalities from multi-sensor platforms, including hyperspectral (HS) and light detection and ranging (LiDAR) data, have been garnering increasing attention in overcoming the lack of information diversity. However, the specific modality correlation is always weakened and neglected. By reducing the spectral uncertainty, hyperspectral intrinsic image decomposition (HIID) has been proven to be effective in enhancing the HS image quality. It is an ill-posed problem and is challenged to capture the inherent modality connection. This paper proposes a shadow mask driven multimodal intrinsic image decomposition (smMIID) for HS and LiDAR data fusion. Notably, smMIID creatively clarifies and activates the potential of the crossmodal information in RS modalities and builds on the strength of the LiDAR elevation information when constructing HIID constraints. Our motivation is to overcome the deficiency of information diversity and modality correlation in existing IID based frameworks for better data fusion performance. Classic IID methods decompose the HS image into one reflectance component (RC) and one shading component (SC). There are three constraints in smMIID: 1) the fundamental constraint on RC and SC, 2) the HS-LiDAR hybrid gradient based constraint on RC and 3) the LiDAR gradient based constraint on SC. A shadow mask is obtained and utilized to guarantee spectral consistency when combining neighboring samples. Experiments on HS and LiDAR datasets prove that smMIID outperforms other techniques in terms of visualization and classification. The analyses are provided theoretically and experimentally to demonstrate that smMIID is robust to shadow mask generation.

Complex-valued limited-memory BFGS (CL-BFGS) algorithm is efficient for the training of complex-valued neural networks (CVNNs). As an important parameter, the memory size represents the number of saved vector pairs and would essentially affect the performance of the algorithm. However, the determination of a suitable memory size for the CL-BFGS algorithm remains challenging. To deal with this issue, an adaptive method is proposed in which the memory size is allowed to vary during the iteration process. Basically, at each iteration, with the help of multistep quasi-Newton method, an appropriate memory size is chosen from a variable set {1,2, ... , M} by approximating complex Hessian matrix as close as possible. To reduce the computational complexity and ensure desired performance, the upper bound M is adjustable according to the moving average of memory sizes found in previous iterations. The proposed adaptive CL-BFGS (ACL-BFGS) algorithm can be efficiently applied for the training of CVNNs. Moreover, it is suggested to take multiple memory sizes to construct the search direction, which further improves the performance of the ACL-BFGS algorithm. Experimental results on some benchmark problems including the pattern classification, complex function approximation, and nonlinear channel equalization problems are given to illustrate the advantages of the developed algorithms over some previous ones.

In the 5G-and-beyond era, ultra-reliable low latency communication (URLLC) services are ubiquitous in edge networks. To enhance the performance metrics and the quality of service (QoS), URLLC services are delivered via a sequence of software-based network functions, also known as a service function chain (SFC). Towards reliable SFC delivery, it is imperative to incorporate fault-tolerance during SFC deployments. However, deploying an SFC with fault-tolerance is challenging because the protection mechanism needs to jointly consider multiple concurrent physical/virtual network failures and hardware/software failures. Considering these concurrent heterogeneous failures, this work investigates how to effectively deliver an SFC in edge networks with the objective of minimizing bandwidth resource consumption. First, we introduce the concept of${k}$-heterogeneous-faults-tolerance and propose an augmented protection graph, called${k}$-connected service function slices layered graph (KC-SLG). Based on the KC-SLG, we formulate a novel problem called${k}$-heterogeneous-faults-tolerant SFC embedding and propose an effective algorithm, called fault-tolerant service function graph embedding (FT-SFGE). FT-SFGE employs two proposed techniques:${k}$-connected network slicing (KC-NS) and${k}$-connected function slicing (KC-FS). Via thorough mathematical proofs, we show that KC-NS is 2-approximate. Extensive simulations show that KC-FS has the best average cost-efficiency when${k}$= 2, and FT-SFGE outperforms the schemes directly extended from the state-of-the-art.

Multi-band transmission over existing fibers would be a key strategy for ongoing capacity expansion even though upgrading from conventional C-band to multi-band, such as the C+L-band transmission being deployed by operators, would be a slow and complex process. After the C+L band, which band should be upgraded first in the next stage is an open question. In this paper, we try to answer this by comparing the potential capacity increase and the investment cost to upgrade different bands. We propose an OSNR estimation model comprehensively covering various impairments to evaluate the quality of transmission of an optical channel. Along with routing and spectrum assignment, a traffic grooming algorithm is also developed to evaluate the capacity that can be achieved after upgrading different bands in an optical network. Our studies show that the E band should be the first to be upgraded next since it both expands the transmission capacity significantly using only a few additional amplifiers, and also improves the quality of transmission of the C+L band.

Hyperspectral images (HSIs) provide abundant high-quality spectral information through an immense number of spectral channels, which can be used to classify on-ground objects for Earth observation accurately. However, these highly correlated channels and complex informative features always limit the application of HSIs. In this letter, we propose a multilevel dual-direction modifying variational autoencoder (MD2MVAE) for hyperspectral feature extraction. Its architecture is inspired by the spectral–spatial coherence in HSIs. Our motivation is to modify spectral sequential features by spatial sequential features in a multilevel dual-direction network. The dual-direction strategy has two implications: 1) the spatial continuity is captured by flattening neighboring samples in dual classic directions and 2) the spectral–spatial continuities are captured in the forward and backward directions. This multilevel dual-direction network provides a feasible way to avoid the loss of spatial information when flattening samples in the spatial domain, without using any convolutional layers. Inspired by our previous work, a variational autoencoder (VAE)-based network is used to enhance the noise immunity of latent features. To preserve the consistency of spectral–spatial sequential features, a combined loss function based on the solid angle on a unit sphere is proposed for parameter optimization. Two typical datasets are selected as benchmarks to show the effectiveness of MD2MVAE, compared with the state-of-the-art methods.

Data center virtualization is a flexible and efficient way to enable multiple users to share the common resources of a physical data center (DC). For efficient sharing, virtual data center (VDC) embedding is a vital problem that should be carefully addressed. However, existing studies on VDC embedding mostly assume that the capacity of each VDC is fixed, but do not consider the time-varying feature of resource demands. Considering the fact that the resource demands of most enterprise IT services exhibit the time-varying feature, resource allocation based on the fixed capacity assumption would cause a great inefficiency. To overcome this inefficiency, we propose a new VDC consolidation scheme that takes into account the time-varying feature of resource demands when embedding VDCs. We first develop a resource demand prediction model for each VDC using the Long Short-Term Memory (LSTM) neural network, which is used to predict the real-time resource demands of VDCs at different future moments. Based on the predicted resource demands, we then embed VDCs whose peaks and valleys of resource demands stagger each other onto common physical servers and links, such that the required physical resources can be minimized under the condition that all the resource demands of different VDCs are satisfied at all the different moments. An integer linear programming (ILP) model and a resource demand correlation-based heuristic algorithm are also developed for the proposed scheme. Simulation results show that the proposed consolidation scheme can significantly improve resource utilization in a DC. It can save up to 25 percent of physical servers and 29 percent of physical links used for accommodating the same requests as compared to a scheme assigning resources based on the fixed capacity assumption.

Emerging 5G technologies can significantly reduce end-to-end service latency for applications requiring strict quality of service (QoS). With network function virtualization (NFV), to complete a client’s request from those applications, the client’s data can sequentially go through multiple service functions (SFs) for processing/analysis but introduce additional processing delay. To reduce the processing delay from the serially-running SFs, network function parallelism (NFP) that allows multiple SFs to run in parallel is introduced. In this work, we study how to apply NFP into the SF chaining and embedding process such that the latency, including processing and propagation delays, can be jointly minimized. We introduce a novel augmented graph to address the parallel relationship constraint among the required SFs. Considering parallel relationship constraints, we propose a novel problem called parallelism-aware service function chaining and embedding (PSFCE). For this problem, we propose a near-optimal maximum parallel block gain (MPBG) first optimization algorithm when computing resources at each physical node are enough to host the required SFs. When computing resources are limited, we propose a logarithm-approximate algorithm, called parallelism-aware SFs deployment (PSFD), to jointly optimize processing and propagation delays. We conduct extensive simulations on multiple network scenarios to evaluate the performances of our schemes. Accordingly, we find that (i) MPBG is near-optimal, (ii) the optimization of end-to-end service latency largely depends on the processing delay in small networks and is impacted more by the propagation delay in large networks, and (iii) PSFD outperforms the schemes directly extended from existing works regarding end-to-end latency.

The improved efficiency achieved by virtualizing data centers (DCs) has been well established. In this paper, we propose a mixed Virtual Data Center (VDC) capable of supporting both unicast and multicast services. We provide a new method to realize the embedding of these VDCs. We also provide a Mixed Integer Linear Programming (MILP) formulation and a scalable heuristic algorithm for efficiently embedding its demands. Numerical results show that mixed VDC embedding supporting both unicast and multicast services performs significantly better than existing embedding methods in terms of system cost, power consumption, link capacity utilization, and VDC acceptance ratio.

Internet traffic demand keeps on increasing and tends to show strong bidirectional asymmetry. To tackle this asymmetry issue, we propose here a novel unidirectional design approach to alleviate the impact of asymmetry for elastic optical networks (EONs). We decouple a bidirectional transponder into an isolated unidirectional transmitter (Tx) and an isolated unidirectional receiver (Rx). Based on this, we further study how different multi-flow transmitters will affect the design performance, i.e., whether the transmitter is using an array of laser diodes or a broadband laser source with a filter to generate multiple sub-carriers. We evaluate the proposed approach by considering the routing and spectrum allocation (RSA) optimization problem, for which Integer Linear Programming (ILP) models and a SWP-based heuristic algorithm are developed. Simulation studies show that the proposed approaches are efficient to significantly improve the network capacity utilization and minimize the network cost compared to a network design limited to considering only bidirectional symmetric traffic flows.

With the explosive expansion of the information and communication technology (ICT) section, its energy saving has become an important issue and is receiving wide interest. In this study, we propose an adaptive clock frequency strategy for router cards to minimize the total energy consumption of an IP over WDM network. Rather than always running at full speed, the clock frequency of a router card is adaptively adjusted according to its actual-carried traffic demand. Given forecast traffic demand matrixes between different node pairs in different time slots, we develop a mixed integer linear programming (MILP) model to optimally choose the clock frequencies for each router card in different time slots such that the total energy consumption of the router cards is minimized. For lower computational complexity, the optimization model is also decomposed into two models, which correspond to the two subproblems of the optimization problem. The first subproblem minimizes the total number of router cards at each network node based on the peak-hour traffic, and the second subproblem optimally chooses the clock frequencies for each router card in different time slots. Due to the high-computational complexity of the MILP models, we also develop an efficient heuristic algorithm, in which two key steps that tackle the two subproblems are specifically developed. The joint MILP model provides a lower bound on the energy consumption, which shows to save more than 40% energy compared to the case without adaptive router card clock frequency. It is also found that the heuristic algorithm is efficient and performs close to the MILP models. In addition, the results also show that a router card supporting a small number of discrete clock frequencies can perform close to a card with continuously changed clock frequencies, and the benefit of adaptive clock frequency becomes weak with increasing router card power consumption overhead.

The articles in this special issue focus on energy efficiency techniques deployed in optical fiber networking.

Next-generation passive optical network (PON) has been considered in the past few years as a cost-effective broadband access technology. With the ever-increasing power saving concern, energy efficiency has been an important issue in its operations. In this paper, we propose a novel sleep-time sizing and scheduling framework for the implementation of green bandwidth allocation (GBA) in TDMA-PONs. The proposed framework leverages the batch-mode transmission feature of GBA to minimize the overhead due to frequent ONU on-off transitions. The optimal sleeping time sequence of each ONU is determined in every cycle without violating the maximum delay requirement. With multiple ONUs possibly accessing the shared media simultaneously, a collision may occur. To address this problem, we propose a new sleep-time sizing mechanism, namely Sort-And-Shift (SAS), in which the ONUs are sorted according to their expected transmission start times, and their sleep times are shifted to resolve any possible collision while ensuring maximum energy saving. Results show the effectiveness of the proposed framework and highlight the merits of our solutions .

This paper deals with the design of a span-restorable (SR) elastic optical network under different spectrum conversion capabilities, including 1) no spectrum conversion, 2) partial spectrum conversion, and 3) full spectrum conversion. We develop Integer Linear Programming (ILP) models to minimize both the required spare capacity and the maximum number of link frequency slots used for each of the three spectrum conversion cases. We also consider using the Bandwidth Squeezed Restoration (BSR) technique to obtain the maximal restoration levels for the affected service flows, subject to the limited frequency slot capacity on each fiber link. Our studies show that the spectrum conversion capability significantly improves spare capacity efficiency for an elastic optical network.

The six papers in this special issue are devoted to the topic of next generation spectrum efficient and elastic optical transport systems.

Achieving the benefits of high-capacity of optical networks and the mobility feature of wireless networks leads to integrate EPON and WiMAX for a promising broadband access solution. To efficiently put both benefits together, we propose an integration architecture of EPON-WiMAX based upon a Distributed Antenna (DA) environment, where collaborative Base Stations (BSs) concurrently transmit same wireless downlink signals (specifically for multicast and broadcast services (MBSs)) to Mobile Stations (MSs) in overlapped cell coverage areas. It helps enlarge the region of the available network coverage area by increasing Signal to Interference and Noise Ratio (SINR) in overlapped cell coverage areas through cooperation between Optical Line Terminal (OLT) and Optical Network Unit (ONU)- BS. We also present an cost-efficient cell planning to optimally control the size of overlapped cell coverage areas for the proposed DA-based integration architecture with a case study under a required region of the available network coverage area in consideration of the number of ONU-BSs and the distance between ONU-BSs. Performance evaluation results show that the proposed DA-based integration architecture enhances cost efficiency compared to the Traditional Antenna (TA) (non-DA)-based integration architecture with a similar level of spectral efficiency of MSs.

We consider path-based survivable service provisioning in transparent optical networks with the constraints of wavelength continuity and a limited number of add/drop ports at each OXC node in the presence of limited tunability of transmitters. We develop simple but valid analytical models to estimate the effects of number of add/drop ports and transmitter tunability on survivable service provisioning performance. We propose effective algorithms for the assignments of wavelength resources and add/drop ports for each survivable connection service and conduct simulations to evaluate the impacts of number of add/drop ports and transmitter tunability on path- based survivable service provisioning and further to examine the validity of the analytical models. It is found that a certain system add/drop ratio is required at each node so as to eliminate the blocking due to the lack of free add/drop ports. A network with a higher density requires a larger relative number of add/drop ports (i.e., add/drop ratio) for a given overall blocking objective. A network with a higher density benefits more in blocking from transmitter tunability. Finally, the analytical models are verified to be able to qualitatively predict the trends and effects of all the related constraints on the performance of survivable service provisioning.

This work introduces a significant extension to the method p-cycles for network protection. The main advance is the generalization of the p-cycle concept to protect multi-span segments of contiguous working flow, not only spans that lie on the cycle or directly straddle the p-cycle. This effectively extends the p-cycle technique to include path protection or protection of any flow segment along a path as well as the original span protection use of p-cycles. It also gives an inherent means of transit flow protection against node loss. We develop a capacity optimization model for the new scheme and compare it to prior p-cycle designs and other types of efficient mesh-survivable networks. Results show that path-segment-protecting p-cycles have capacity efficiency near that of a path-restorable network is to suggest the use of flow p-cycles to protect transparent optical express flows through a regional network.

The paper introduces an extension to the method of p-cycles for network protection. The p-cycle concept is generalized to protect path segments of contiguous working flow, not only spans that lie on the cycle or directly straddle the p-cycle. The original span protecting use of the p-cycle technique is extend to include path protection or protection of any flow segment along a path. It also gives an inherent means of protecting working flows that transit a failed node. We use integer linear programming to study the new concept and determine its inherent capacity requirements relative to prior p-cycle designs and other types of efficient mesh-survivable networks. Results show that path-segment-protecting p-cycles ("flow p-cycles") have capacity efficiency near that of the shared backup path-protection (SBPP) scheme currently favored for optical networking. Because its protection paths are fully preconnected and because it protects path segments (not entire paths), it has the potential for both higher speed and higher availability than SBPP. We also develop capacity optimization models to support 100% restoration of transiting flows through failed nodes. Only a very small additional spare capacity is needed to achieve both 100% span and intermediate node-failure restorabilities, and a very high transiting traffic restorability can be accomplished for node failure restorability given spare capacity only for span-failure protection. An immediate practical application is to suggest the use of flow p-cycles to protect transparent optical express flows through a regional network.

A limited-wavelength-interchangeable cross-connect (L-WIXC) is considered in this paper. While a (fully) wavelength-interchangeable cross-connect (WIXC) is functionally equivalent to a conventional switching matrix, the modeling of L-WIXC will differ from that of WIXC due to limitations in the wavelength interchange capability. We propose an analytical model for describing the connection setup in various L-WIXCs. Two methods for evaluating the blocking performances of L-WIXCs based on this model are introduced and applied for the analysis of four classes of L-WIXC.