Free space optical (FSO) communication is a promising solution for next-generation communication networks. Atmospheric turbulence, however, severely degrades its performance. We thus propose a novel turbulence-robust end-to-end FSO communication system (TRFSO) that performs joint training across the entire transmission process from source to transmission channel to destination. To introduce physical FSO channel impairments into the training loop, we developed a bidirectional long short-term memory (BiLSTM)-based FSO channel model. Through end-to-end joint training, the system achieved high quality and robust transmission under dynamic atmospheric turbulence. Trained on experimental data collected over a physical FSO link under varying turbulence conditions, the proposed model accurately reproduced real-world channel distortions, achieving a minimum Kullback–Leibler (KL) divergence of 0.0019 nats in amplitude distribution matching. Our experimental results revealed that TRFSO significantly outperformed conventional separate coding modulation schemes in FSO links. Moreover, under strong turbulence, TRFSO achieved a 3.5 dB gain in average multi-scale structural similarity (MS-SSIM) compared with the same network architecture trained without the channel model.

Optical satellite networks, supported by optical inter-satellite links (OISLs), provide reliable and low-latency optical connectivity. However, periodic and predictable sun outage events significantly compromise OISL availability, leading to frequent OISL interruptions and reduced network reliability. Existing routing algorithms often overlook the regularity of sun outage-induced interrupts and their differentiated impacts on services, resulting in degraded service performance. To address this challenge, this paper proposes a sun outage-enhanced time discretization OISL model and introduces a sun outage link-aware routing (SOLR) algorithm. By incorporating joint awareness of sun outage patterns and service requirements, SOLR employs an adaptive optimization mechanism to dynamically adjust routing decisions within temporal windows. Experimental results demonstrate that SOLR extends stable path durations by 39.9%, reduces interruption rates by 28.5%, and decreases blocking rates by 36.4%, significantly outperforming link-state-based routing algorithms. By effectively mitigating the impact of sun outages, SOLR ensures continuous optical service connections. This interruption-tolerant framework bridges network modeling and service provisioning, offering a robust solution for mission-critical service in optical satellite networks.

Current AI training clusters widely use RoCEv2 to improve the communication efficiency of the interconnect networks across machines. RoCEv2 relies on Priority Flow Control (PFC) to ensure a lossless network. However, PFC brings certain side effects, such as head-of-line blocking, congestion spreading, and deadlock. Numerous studies have been proposed to eliminate the side effects. However, unlike traditional high-performance computing applications, distributed machine learning (DML) is not 100 % loss-intolerant. In light of this observation, this paper proposes a loss-tolerant flow control (LTFC). LTFC does not rely on PFC to ensure a lossless environment, but to control packet loss ratio within the tolerance threshold. Compared to the traditional trigger condition, LTFC reduces the likelihood that the PFC will be triggered. Additionally, we replace RoCEv2's default Go-back-N mechanism with a non-retransmission mechanism to eliminate retransmission latency. We demonstrate the bounded-loss tolerance feature of DML on our testbed and evaluate the performance of LTFC in large-scale simulations. Simulation results show that LTFC reduces the average flow completion time (FCT) by up to 26.9 % and tail FCT by up to 16.9 % compared to existing solutions.

The satellite-to-ground link is susceptible to eavesdropping from various open-space locations. In this paper, we investigate a new eavesdropping scenario in a multiconstellation satellite network, where very low earth orbit (VLEO) satellites eavesdrop on low earth orbit (LEO) constellations from legitimate positions. We model the VLEO eavesdropping risks in a theoretical way and propose a crossinterference scheme called LEO active cross-interference (LEOACI) to protect satellite-to-ground downlinks from eavesdropping attacks. The scheme prevents eavesdropping by scheduling spare satellites to transmit artificial noise (AN) over the protected link. Finally, we conduct simulations to evaluate the performance of the LEO-ACI scheme and it shows that VLEO eavesdroppers can possess high eavesdropping coverage and long duration. Meanwhile, the LEO network can ensure that 98.5 % of downlinks are free from eavesdropping at the cost of 12.3 % of the satellite-to-ground capacity degradation.

Geo-distributed machine learning (GDML) can facilitate collaborative learning among geographically-dispersed data centers to meet the demands of distributed and privacy-preserving training for large-scale distributed Internet of Things applications. Unfortunately, the efficiency of distributed training tasks heavily depends on synchronized communication between multiple distributed models over bandwidth-limited wide area networks (WANs). The fine-grained Optical Transport Network (fgOTN), thanks to its adjustable bandwidth connections, represents more flexible transmission and has the ability for accurate synchronization across GDML tasks in WANs. However, flexible bandwidth assignment and complex interdependencies among tasks pose significant challenges to resource allocation for GDML in fgOTN. Specifically, flexible bandwidth assignment exacerbates resource competition among task flows, leading to decreased learning efficiency. This paper provides novel resource allocation solutions for GDML in fgOTN. We first formulate this problem as a linear programming aimed at maximizing the completion ratio of GDML tasks. Subsequently, we propose an innovative resource allocation algorithm based on genetic algorithm (GARA) for GDML in fgOTN. GARA considers both task completion and bandwidth adjustment through population generation based on prior knowledge and adaptive mutation based on completion ratio. Simulation analysis demonstrates that GARA effectively prioritizes resource allocation for high-priority tasks to alleviate resource competition, achieving the highest task completion ratio while avoiding excessive network reconfiguration.

Intelligent computing center (ICC) is a new type of data center constructed with intelligent computing power, such as graphic processing units (GPUs) and artificial intelligence acceleration cards. With billions of parameters, the emergence of large models (e.g., ChatGPT) presents a significant demand of computing power. It may be challenging for a single ICC to provide the required computing power during large model training. Thus, ICC interconnections (ICCI) will become a typical and effective solution to provide intensive computing power. Due to human activities, traditional computing tasks (e.g., transaction processing and online entertainment) exhibit a tidal effect of computing demand, which leads to the tidal variation of remaining computing resources. Moreover, distributed model training (DMT) tasks are likely to cover peaks and valleys of the tidal effect in computing power. In this case, it is easy for DMT tasks to cause an ICC to become a hotspot (i.e., computing load in an ICC exceeds a desired threshold), which significantly degrades the reliability and performance of the ICC. This paper proposes DeepHM, a deep reinforcement learning-based hotspot management strategy through task migration in ICCI networks. To comprehensively consider the bandwidth metrics of the ICCI network, we further propose a dynamic wavelength allocation strategy, i.e., DeepHM-DWA. Simulation results show that the DeepHM and DeepHM-DWA reduce the hotspot compute unit time blocks by 19% and 18% with fewer number of migrated workers while balancing the computing load among multiple ICCs. DeepHM and DeepHM-DWA reduce the average completion time ratio of the DMT tasks by 2% and 5%, respectively.

Quantum entanglement enables quantum networks to provide end-to-end sharing of entangled particles, establishing multi-hop path-to-path connections between remote parties. Implementing entanglement distribution plays a vital role in increasing the network scale, and practical entanglement algorithms are required to provide end-to-end multi-hop quantum entanglement. We consider the real-time entanglement distribution (R-TED) and pre-established entanglement distribution (P-EED) to meet this requirement. Based on these two types of entanglement distribution, we propose two algorithms, i.e., R-TED-based routing and entangled pairs allocation (REA) algorithm as well as P-EED-based REA algorithm for end-to-end entanglement establishment, where the practical physical factors (e.g., finite storage capacity and limited storage time) are considered. The R-TED-based REA algorithm can orchestrate the nodes in a route and perform entanglement swapping by adopting real-time entanglement. For the P-EED-based REA algorithm, remote entangled particle sharing can be achieved via pre-shared entanglement distribution and hop-by-hop entanglement swapping. This way, the entanglement routing selection satisfies the storage time constraint and allows two far-apart nodes to share long-distance entangled particles with limited memory cells. We evaluate the performance of the proposed algorithms under different network topologies and sizes, based on which we demonstrate that the network size can significantly affect the efficiency advantage achieved by the P-EED-based approach over the R-TED-based approach.

With the continual growth in user communication needs, classical optical communications are facing developmental bottlenecks. On one hand, the communication capacity of available optical fiber resources is approaching its upper limit. On the other hand, emerging quantum computing technology poses security threats. Quantum key distribution (QKD), as a representative quantum cryptography technology, is being introduced into existing optical infrastructure to mitigate security threats. It also has pioneering application value for next-generation quantum information networks. However, limited optical fiber resources struggle to support the introduction of quantum communication over classical optical networks. There are also incompatible noise factors between the two communication paradigms. This paper proposes transitioning from unilateral adaptive routing and wavelength assignment (ARW A) to bilateral synergistic RW A (SRW A) to facilitate the coexistence of two heterogeneous communication paradigms in optical networks. Simulations have proven SRW A can increase end-to-end key supply rates from bit/s to kbit/s levels. It has an enabling effect on QKD over classical optical networks.

In recent years, research on satellite networks has gained significant attention, with their capability for seamless global coverage and meeting real-time communication demands serving as a key solution to address deficiencies in ground communication network coverage and to improve the real-time transmission of services. Traditional satellite networks, originally employed for singular purposes such as data relay, are gradually transitioning to satellite internet to support various Internet-based services. The integration of satellites with ground networks, known as the Satellite-Terrestrial Integrated Network (STIN), has become an inevitable trend, making the deployment of ground stations (GSs) a critical issue in the STIN construction. Traditional GS deployment strategies are insufficient to meet the real-time demands of emerging services. In this context, a GS deployment strategy based on the data center-user gravity model (GSD-DG) is proposed, where the influence of all data center factors on GS deployment is considered. This approach takes into account constraints such as satellite connectivity, user traffic, and data center gravity. The integration of GS with data centers plays a pivotal role in enhancing the internet service latency performance. Simulation results indicate that the proposed strategy significantly reduces service latency by 24.5% compared to the benchmark, providing a more effective GS deployment solution to further optimize the STIN service latency performance.

In the sixth-generation fixed network (F6G), network security becomes an important topic. Encryption is an effective method to prevent network attacks and realize network security. Quantum key distribution (QKD) is a promising technology to effectively address the challenge by providing secret keys due to the laws of quantum physics. New services such as high immersion experience and holographic have the characteristics of time-varying bandwidth and requirements. The introduction of optical service unit (OSU) technology makes it possible to provide the exact bandwidth used by the service. In optical transport networks, a lightpath needs to be established before service transmission, and will be removed after service transmission. Signaling is used for lightpath establishment, removal, and bandwidth adjustment. Data information transmitted in data layer and signaling information transmitted in control layer are highly vulnerable to cyberattacks, such as eavesdropping. The supply of bandwidth and key resources need to be optimized to achieve secure and stable service transmission in optical networks. Hence, how to realize bandwidth and key on demand (BKoD) provisioning for dynamic services is a key problem. To improve the flexibility of bandwidth and key resource allocation and utilization, a QKD-secured OSU-based optical transport network can be deployed. In this paper, a novel QKD-secured OSU-based optical transport network architecture is proposed and a service aware dynamic resource provisioning (SADRP) algorithm is proposed to realize BKoD. The proposed architecture uses the QKD technique to provide keys for both signaling information and data information for the first time. The proposed algorithm supplies resources according to the dynamic demand of bandwidth and key, so as to achieve the balance between dynamic demand and static resource utilization. Simulations results show that compared with the benchmark algorithm, the SADRP algorithm reduces blocking probability by 4.16%, reduces bandwidth resource utilization rate by 4.39%, reduces key resource utilization rate by 3.48%, and improves security rate by 4.17%.

Leading by the development of Starlink, the low earth orbit (LEO) satellite networks are expected to be the customer-grade Internet infrastructure that will co-work with the terrestrial networks for carrying Internet traffic. In this paper, focusing on network usage fluctuations that are caused by human activity variations in work and rest time slots, we introduce the concept of time zones into the global satellite network and develop a time-zone-aware traffic model. Accordingly, we propose a relay-based routing algorithm to balance the workload of the satellites and the inter-satellite links over different time zones. Simulation results show that the proposed model and algorithm can reduce the blocking ratio by up to 34.8% and increase the bandwidth utilization ratio by up to 45.6%, with a limited cost in average connection latency.

Datacenter networks (DCNs) with cloud–edge collaboration are emerging to satisfy the communication, computation, and caching (3C) requirements of future services such as cloud-based IoT services. However, the enroute data over DCNs with cloud–edge collaboration is likely to suffer from cyberattacks such as eavesdropping. A large number of services require not only 3C resources, but also cryptographic resources for encryption to ensure high security. Quantum key distribution (QKD) is a practical approach to provide secret keys for remote users with information-theoretic security against attacks from quantum computing. A QKD-secured DCN (QKD-DCN) with cloud–edge collaboration can be deployed to satisfy the communication, computation, caching, and cryptographic (4C) requirements of services. This article innovatively solves the new 4C resource-allocation (4CRA) problem in the network to minimize the cryptographic resource consumption. It formulates an integer linear programming (ILP) model and proposes a heuristic cryptographic-dependent 4CRA algorithm to find optimal solutions. The proposed algorithm is compared with two baseline 4CRA algorithms which, respectively, consider the minimized service delivery latency and the first-fit resource availability. Analytical simulations show that the proposed algorithm minimizes the key-resource-consumption ratio and the average key-resource consumption under static and dynamic traffic scenarios in different network topologies.

Edge computing provides computing capability at close-user proximity to reduce service latency for end users. To improve the efficiency of edge computing infrastructures, geographically-distributed edge datacenters can co-work with each other and with cloud datacenters, forming a new paradigm referred to as cooperative edge-cloud computing. In this context, applications typically run on a virtual machine (VM) that can be replicated at multiple sites, and thus user traffic can be served at all the sites where corresponding VMs reside. For the performance of many applications, latency is a critical parameter. In this work, taking applications’ latencies as the primary constraint, we model the problem of “VM placement and workload assignment” as a mixed integer linear program and develop heuristic algorithms accordingly. The goal is to minimize the consumption of information technology (IT) infrastructures for placing VMs in cooperative edge-cloud computing, while meeting the heterogeneous latency demands of different applications. Some preliminary results indicate that edge datacenter's resource efficiency can be optimized by proper cross-site VM placement and workload re-direction.

The development of reusable rockets makes it possible to launch a massive number of low earth orbit satellites for Internet access. These satellites are expected to be connected by inter-satellite links (ISLs) to provide global Internet service to end-users on the surface of the earth. From the infrastructure’s perspective, the ISLs can be implemented with lasers, forming the optical satellite networks. The design of terrestrial optical networks is usually traffic-driven, meaning more links/bandwidth are deployed to the traffic-intensive areas. But, the orbiting nature of satellites determines that laser ISLs are also moving at high velocities, making the presence of corresponding bandwidth loosed coupled with a specific area. As a result, the traffic-driven network design methodology will be inapplicable to the optical satellite networks. In this work, we model the optical satellite networks and define the laser ISL planning problem, Accordingly, we propose an ISL removal algorithm to remove parts of the ISLs from the grid-mesh topology to improve the average bandwidth utilization ratio (i.e., bandwidth efficiency) while maintaining networks’ reliability and availability. We conduct a simulation study based on the Starlink constellation to evaluate the grid-mesh topology and to investigate the proposed algorithm’s impact on the network performance. Results show that the bandwidth resource in the grid-mesh topology can hardly be used efficiently and removing certain ISLs properly can improve the bandwidth efficiency of optical satellite networks significantly. We also gain another interesting insight that maintaining too many ISLs will not improve, but degrade the network availability.

Quantum key distribution (QKD) has demonstrated a great potential to provide future-proofed security, especially for 5G and beyond communications. As the critical infrastructure for 5G and beyond communications, optical networks can offer a cost-effective solution to QKD deployment utilizing the existing fiber resources. In particular, measurement-device-independent QKD shows its ability to extend the secure distance with the aid of an untrusted relay. Compared to the trusted relay, the untrusted relay has obviously better security, since it does not rely on any assumption on measurement and even allows to be accessed by an eavesdropper. However, it cannot extend QKD to an arbitrary distance like the trusted relay, such that it is expected to be combined with the trusted relay for large-scale QKD deployment. In this work, we study the hybrid trusted/untrusted relay based QKD deployment over optical backbone networks and focus on cost optimization during the deployment phase. A new network architecture of hybrid trusted/untrusted relay based QKD over optical backbone networks is described, where the node structures of the trusted relay and untrusted relay are elaborated. The corresponding network, cost, and security models are formulated. To optimize the deployment cost, an integer linear programming model and a heuristic algorithm are designed. Numerical simulations verify that the cost-optimized design can significantly outperform the benchmark algorithm in terms of deployment cost and security level. Up to 25% cost saving can be achieved by deploying QKD with the hybrid trusted/untrusted relay scheme while keeping much higher security level relative to the conventional point-to-point QKD protocols that are only with the trusted relays.

With the advent of smart homes, smart cities, and smart everything, the Internet of Things (IoT) has emerged as an area of incredible impact, potential, and growth. Internet of Things date security remain a major challenge, in the current Internet of Things systems, a relatively easy method of data encryption is used to ensure the security of data transmission, which is commonly called lightweight cryptography. However, such method is at the risk of being cracked by quantum computers, which will contribute to many challenges specially related to privacy and security in IoT. As a result, the architecture of IoT needs to be re-designed considering the security challenges brought by quantum computers. On the other hand, Quantum Key Distribution (QKD) allows two users to share unconditionally secure keys. Unlike classical cryptosystems, the security of QKD is based on the fundamentals of quantum mechanics. This security of QKD is independent of computational complexity and will not be affected, no matter how much computing power the adversary has. This paper introduces a quantum key distribution enhanced Internet of Things architecture and proposes a residual-adaptive key provisioning scheme, which is evaluated in terms of key distribution success rate in the simulation.

Free-space long-distance quantum key distribution (QKD) has the characteristics of low attenuation and wide coverage, which can overcome the limitation of transmission distance based on ground optical fiber QKD networks. Long-distance QKD requests can be relayed by quantum satellites to achieve intercontinental QKD. At present, there is only one quantum satellite in space, quantum satellite network with multiple quantum satellites is necessary to be deployed to cover the global. Thus, how to construct the quantum satellite network becomes a new challenge. In this paper, we propose a centralized and distributed collaborative scheme of quantum satellite network. Low-Earth-orbit (LEO) quantum satellites, as important relay satellites, will affect the performance of the global QKD network. Based on the above architecture, we analyze the influence of the structure of LEO quantum satellite constellation on the QKD performance. Simulation results show that the key relay performance of the constellation is related to the form of inter-satellite link. and the more the number of satellites is, the greater the successful probability of the key relay services will be.

Quantum key distribution (QKD) networks are potential to be widely deployed in the immediate future to provide long-term security for data communications. Given the high price and complexity, multi-tenancy has become a cost-effective pattern for QKD network operations. In this work, we concentrate on addressing the online multi-tenant provisioning (On-MTP) problem for QKD networks, where multiple tenant requests (TRs) arrive dynamically. On-MTP involves scheduling multiple TRs and assigning non-reusable secret keys derived from a QKD network to multiple TRs, where each TR can be regarded as a high-security-demand organization with the dedicated secret-key demand. The quantum key pools (QKPs) are constructed over QKD network infrastructure to improve management efficiency for secret keys. We model the secret-key resources for QKPs and the secret-key demands of TRs using distinct images. To realize efficient On-MTP, we perform a comparative study of heuristics and reinforcement learning (RL) based On-MTP solutions, where three heuristics (i.e., random, fit, and best-fit based On-MTP algorithms) are presented and a RL framework is introduced to realize automatic training of an On-MTP algorithm. The comparative results indicate that with sufficient training iterations the RL-based On-MTP algorithm significantly outperforms the presented heuristics in terms of tenant-request blocking probability and secret-key resource utilization.

Traffic flows are often processed by a chain of Service Functions (SFs) (known as Service Function Chaining (SFC)) to satisfy service requirements. The deployed path for a SFC is called Service Function Path (SFP). SFs can be virtualized and migrated to datacenters, thanks to the evolution of Software Defined Network (SDN) and Network Function Virtualization (NFV). In such a scenario, provisioning of paths (i.e., SFPs) between virtualized network functions is an important problem. SFP provisioning becomes more complex in a multi-domain network topology. `Topology aggregation' helps to create a single-domain view of such a network by abstracting multi-domain networks. However, traditional `topology aggregation' methods are unable to abstract SF resources properly, which is required for SFP provisioning. In this paper, we propose an SFC-Oriented Topology Aggregation (SOTA) method to enable abstraction for SFs in multi-domain optical networks. This study explores the node and the link aggregation degree to evaluate information compression during the `Topology aggregation' process. Additionally, we also propose a new data structure named wheel matrix and related operations to store routing information in the aggregated topology. Based on SOTA, we propose two cross-domain SFP provisioning algorithms named Ordered Anchor Selection (OAS) and ${k}$ -paths OAS (K-OAS), and a benchmark named Global OAS (GOAS). Simulation results show that SOTA could aggregate large-scale multi-domain optical networks into a small network that contains only 6.9% of the nodes and 10.1% of the links. Both OAS and K-OAS can calculate SFPs efficiently and reduce blocking probability up to 52.10% compared to the benchmark.

Mobile Fronthaul (MF) is defined as the connection between Remote Radio Head (RRH) and Baseband Processing Unit (BBU) in a Cloud Radio Access Network (C-RAN). Dedicated MF connections between RRH and BBU would be very costly. Thus, Time Division Multiplexing Ethernet Passive Optical Network (TDM EPON) is a promising solution to reduce cost as it can enable multiplexing gain. Note that, even though mobile users transmit intermittently, in the upstream channel RRH is sampling radio signal all the time, limiting the achievable multiplexing gain. Our study enhances the conventional TDM EPON architecture by introducing traffic classification, sifting of useless data to avoid the transmission of unnecessary EPON frames, and hence increasing the multiplexing gain in the upstream channel. We also propose a Hybrid Bandwidth Allocation (HBA) scheme to exploit the traffic usefulness classification information. Simulation results show significant improvements in terms of load and number of connected RRHs that can be supported by same EPON, while keeping the end-to-end delay under 100 μs.

5G Radio Access Networks (RANs) are supposed to increase their capacity by 1000x to handle growing number of connected devices and increasing data rates. The concept of cloud-RAN (CRAN) has been recently proposed to decouple digital units (DUs) and radio units (RUs) of base stations (BSs), and centralize DUs into central offices. CRAN can ease the implementation of advanced radio coordination techniques, e.g., Coordinated Multi-Point (CoMP) Transmission/Reception, to enhance its system throughput. However, separating DUs and RUs, and implementing CoMP in CRAN require low-latency and high-bandwidth connectivity links, called "fronthaul". Today, consensus has not yet been achieved on how BSs, fronthaul, and central offices will be orchestrated to enhance the system throughput. In this study, we present a CRAN over Passive Optical Network (PON) architecture called virtualized-CRAN (V-CRAN). V-CRAN leverages the concept of virtualized PON (VPON) that can dynamically associate any RU to any DU so that several RUs can be coordinated by the same DU, and the concept of virtualized BS (V-BS) that can jointly transmit common signals from multiple RUs to a user. We propose a novel mathematical model based on constraint programming for joint allocation of radio, optical network, and baseband processing resources to enhance RAN throughput, and we solve it by optimally forming VPONs and V-BSs. Comprehensive simulations show that V-CRAN can enhance the system throughput and the efficiency of resource utilization.

We propose a multi-path fragmentation-aware routing, modulation and spectrum assignment algorithm (RMSA) for advance reservation (AR) and immediate reservation (IR) requests in elastic optical networks. To decrease fragmentation, we propose splitting requests into different parts and transferring each of these parts along a single-path or multi-paths utilizing sliceable bandwidth variable transponders. We first introduce a model to solve the problem and propose a two-dimensional fragmentation occurrence measurement in spectrum and time domains. Then we propose a multi-path fragmentation-aware RMSA algorithm (MPFA). Simulation results show that MPFA can achieve better performance than existing algorithms in terms of blocking probability and spectrum utilization.

In network virtualization, when a disaster hits a physical network infrastructure, it is likely to break multiple virtual network connections. So, after a disaster occurs, the network operator has to schedule multiple teams of repairmen to fix the failed components, by considering that these elements may be geographically dispersed. An effective schedule is very important as different schedules may result in very different amounts of time needed to restore a failure. In this study, we introduce the multiple traveling repairmen problem (MTRP) for post-disaster resilience, i.e., to reduce the impact of a disaster. Re-provisioning of failed virtual links is also considered. We first formally state the problem, where our objective is to find an optimal schedule for multiple teams of repairmen to restore the failed components in physical network, maximizing the traffic in restored virtual network and with minimum damage cost. Then, we propose a greedy (GR) and a simulated annealing (SA) algorithm, and we measure the damage caused by a disaster in terms of disconnected virtual networks (DVN), failed virtual links (FVL), and failed physical links (FPL). Numerical result shows that both proposed algorithms can make good schedules for multiple repairmen teams, and SA leads to significantly lower damage in terms of DVN, FVL, and FPL than GR.

Cloud radio access network (C-RAN) has become a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing using cloud BBUs. In this paper, we propose a novel multistratum resources optimization (MSRO) architecture for cloud-based radio over optical fiber networks with software defined networking. Additionally, a global evaluation strategy (GES) is introduced in the proposed architecture. The MSRO can enhance the responsiveness to end-to-end user demands and globally optimize radio frequency, optical spectrum and BBU processing resources effectively to maximize radio coverage. The overall feasibility and efficiency of the proposed architecture with GES strategy are experimentally verified on OpenFlow-enabled testbed in terms of resource occupation rate and path provisioning latency.

Due to the accelerated evolution of application services, optical network virtualization simplifies optical-layer resource management and provides flexibility in spectrum resource allocation. However, the energy consumption is one of the great challenges in the virtual optical networks (VONs). This paper focuses on the energy efficiency problem in survivable VONs with the sliceable multi-flow transponders and the elastic regenerators. For each VON, all virtual links provide the dedicated-path protection in the flexible bandwidth optical networks. An integer linear program (ILP) and a minimum unit-energy submatrix (MinEnSub) VON mapping approach are developed to improve the energy efficiency, minimize the power consumption, and reduce the spectrum usage under different line rates. For comparison, a baseline VON mapping approach is introduced. Simulation results show that the ILP model and the proposed MinEnSub VON mapping approach can save power consumption, improve the energy efficiency, and reduce the spectrum usage compared with the baseline VON mapping approach in a 6-node network. As expected, in a 14-node network, simulation results also validate that our proposed MinEnSub VON mapping approach can achieve better performance in terms of power consumption, energy efficiency, number of frequencies, and the number of regenerators.

This paper addresses the minimum network cost problem for survivable virtual optical network mapping in flexible bandwidth optical networks. We develop an ILP model and the LBSD (the largest bandwidth requirement (LB) of virtual links versus the shortest distance (SD)) mapping approach to minimize the network cost for a given set of VONs, and we introduce two baseline mapping approaches, named LCLC (the largest computing resources' requirement versus the largest computing resources' provisioning (LC)) and LCSD (the largest computing resources' requirement versus shortest distance) mapping approaches, for comparison. Simulation results show that LBSD can achieve network cost near the ILP solutions in a 6-node network. Also, LBSD greatly reduces the cost, the spectrum usage, and the number of regenerators compared to LCLC and LCSD in the 6-node and NSFNET networks.

This paper addresses the problem of minimizing spectrum usage for shared-path protection with joint failure probability constraint in flexible bandwidth optical networks. To achieve this goal, we propose an integer linear programming (ILP) model for shared-path protection and a heuristic spectrum-aware shared protection (SASP) algorithm that considers joint failure probability. Simulation results show that the ILP model minimizes the total usage of frequency slots and average hops, but leads to high average joint failure probability. Moreover, the SASP algorithm achieves better trade-off between total usage of frequency slots and average joint failure probability compared to the ILP model and a conventional shared-path protection (CSPP) algorithm. As expected, in a 14-nodes network, the SASP algorithm performs better with respect to total spectrum usage and average hops, but results in much larger average joint failure probability compared to the CSPP algorithm.

The key current challenges for the industrial application of all optical switching networks are energy consumption, transmission rate, spectrum efficiency, and switching throughput. The energy consumption problem is mainly researched in this paper. From the perspective of components and modules, node equipment, and network levels, different enabling technologies are proposed to overcome this problem, which are also evaluated through different experimental demonstrations. First, high-sampling-rate digital-to-analog converters (DACs) and WSS-based ROADM modules are demonstrated as components and modules for energy-efficient all optical switching networks. Then, an all optical transport network test-bed consisting of 10 Pbit/s level all optical switching nodes based on multi-level and multi-planar switching architecture is experimentally demonstrated for the first time, which can reduce power consumption by 43%. A control architecture for energy-efficient all optical switching networks is built with OpenFlow based software defined networking (SDN), and experimental results are given to verify the performance of this control architecture. Finally, we describe an All Optical Networks Innovation (AONI) project in China, which aims to explore transmission, switching, and networking technologies in all optical switching networks, and then two application scenarios are forecast based on the technical breakthroughs of this project.

With the emergence of various broadband services, a lot of bandwidth fragments will be generated during the operation of services being mapped into optical channels, which will waste too many bandwidth resources and decrease the transmission performance of optical networks. Traffic grooming strategy in dynamic optical networks can optimise the utilisation of bandwidth resources and reduce the blocking probability. However, in the distributed control plane of automated switched optical networks, all the traffic grooming strategies are implemented in each control node, and resource collision will still occur because the same link resource may be used by two path computation requests or traffic engineering information flooded by open shortest path first-traffic engineering protocol may be not synchronous at each control node or signalling delay time is too long. To reduce the collision of resource, a unified control plane is designed based on path computation element (PCE) for Internet protocol over wavelength division multiplexing networks, and a novel PCE-based traffic grooming strategy is proposed in the framework of unified control plane. Based on this strategy, four PCE-based traffic grooming algorithms are proposed and compared with the distributed traffic grooming strategy without PCE on a simulation platform implemented using disperse event simulation tool OMNET++.

Multi-granularity optical cross-connect (MG-OXC)-based optical network is a promising optical network architecture as it is capable of flexible switching at different granularity levels. In MG-OXC-based optical networks, wavelength conversion (WC) capability and the number of usable add/drop ports of the nodes are two key factors affecting its performance. Two analytical models of blocking probability for MG-OXC-based optical networks both without WC capability and with sparse WC capability are proposed, exploiting Erlang's loss formula and birth–death process. Based on the models and simulation, the impact of WC capability and the number of add/drop ports on the blocking probability are investigated. Three kinds of granularities (i.e. fibre, waveband and wavelength) are considered in MG-OXC nodes to reduce the complexity and size of switch fabric. Both the analytical and simulation results are given on two network topologies under dynamic traffic patterns. Simulation results show that the proposed models are accurate and effective for the analysis of blocking probability in MG-OXC-based optical networks.