Intelligent reflecting surfaces (IRSs) have emerged as a promising solution to mitigate line-of-sight (LoS) blockages and enhance signal coverage in optical wireless communication (OWC) systems with minimal additional power. In this work, we consider a mirror-based IRS to assist a dynamic indoor visible light communication (VLC) environment. We formulate an optimization problem that aims to maximize the sum rate by adjusting the orientation of the IRS mirrors. To enable real-time adaptability, the problem is modelled as a Markov decision process (MDP), and a deep reinforcement learning (DRL) algorithm is developed based on the deterministic policy gradient for real-time mirror-based IRS optimization in dynamic VLC networks. The proposed DRL is employed to optimize mirror orientation toward mobile users under blockage and mobility constraints. Simulation results demonstrate that our proposed DRL algorithm outperforms the conventional deep Q- learning (DQL) algorithm and achieves substantial improvements in sum rate compared to random-orientation IRS configurations

Intelligent reflecting surfaces (IRSs) technology has been considered a promising solution in visible light communication (VLC) systems due to its potential to overcome the line-of-sight (LoS) blockage issue and enhance coverage. Moreover, integrating IRS with a downlink non-orthogonal multiple access (NOMA) transmission technique for multi-users is a smart solution to achieve a high sum rate and improve system performance. In this paper, a dynamic IRS-assisted indoor NOMA-VLC system is modeled, and an optimization problem is formulated to maximize sum energy efficiency (SEE) and fairness among multiple mobile users under power allocation and IRS mirror orientation constraints. Due to the non-convex nature of the optimization problem and the non-linearity of the constraints, conventional optimization methods are impractical for real-time solutions. Therefore, a two-agent deep reinforcement learning (DRL) algorithm is designed for optimizing power allocation and IRS orientation based on centralized training with decentralized execution to obtain fast and real-time solutions in dynamic environments. The results show the superior performance in terms of SEE, sum rate and fairness index of the proposed DRL algorithm compared to standard DRL algorithms and conventional algorithms typically used for resource allocation in wireless communication. The results also show that the proposed two-agent DRL algorithm achieves higher performance compared to deployments without IRS and with randomly oriented IRS elements.

One of the crucial issues in indoor optical wireless communication (OWC) is service interruptions due to blockages that obstruct the line of sight (LoS) between users and their access points (APs). Recently, reflecting surfaces referred to as intelligent reflecting surfaces (IRSs) have been considered to provide improved connectivity in OWC systems by reflecting AP signals toward users. In this study, we investigate the integration of IRSs into an indoor OWC system to improve the sum rate of the users and to ensure service continuity. We formulate an optimization problem for the sum rate maximization, where the allocation of both APs and mirror elements of IRSs to users is determined to enhance the aggregate data rate. Moreover, reinforcement learning (RL) algorithms, specifically Q-learning and SARSA algorithms, are proposed to provide real-time solutions with low complexity and without prior system knowledge. The results show that using RL algorithms achieves near-optimal solutions that are close to the solutions of mixed integer linear programming (MILP). The results also show that the proposed scheme achieves a significant data rate increase compared to a traditional scheme that allocates the resources, APs and mirror elements, based on the distance.

In this paper, we put forward a proof of concept for sixth generation (6G) Terabit infrared (IR) laser-based indoor optical wireless networks. We propose a novel double-tier access point (AP) architecture based on anarray of arraysof vertical cavity surface emitting lasers (VCSELs) to provide a seamless grid-of-beam (GoB) coverage with multi-Gb/s per beam. We present systematic design and thorough analytical modeling of the AP architecture, which are then applied to downlink system modeling using non-imaging angle diversity receivers (ADRs). We propose static beam clustering with coordinated multi-beam joint transmission (CoMB-JT) for network interference management and devise various clustering strategies to address inter-beam interference (IBI) and inter-cluster interference (ICI). Non-orthogonal multiple access (NOMA) and orthogonal frequency division multiple access (OFDMA) schemes are also adopted to handle intra-cluster interference, and the resulting signal-to-interference-plus-noise ratio (SINR) and achievable data rate are derived. The network performance is studied in terms of spatial distributions and statistics of the downlink SINR and data rate through extensive computer simulations. The results demonstrate that data rates up to 15 Gb/s are achieved within the coverage area and a properly devised clustering strikes a balance between the sum rate and fairness depending on the number of users.

Optical wireless communication (OWC) has recently received massive interest as a new technology that can support the enormous data traffic increasing on daily basis. In particular, laser-based OWC networks can provide terabits per second (Tbps) aggregate data rates. However, the emerging OWC networks require a high number of optical access points (APs), each AP corresponding to an optical cell, to provide uniform coverage for multiple users. Therefore, inter-cell interference (ICI) and multi-user interference (MUI) are crucial issues that must be managed efficiently to provide high spectral efficiency. In radio frequency (RF) networks, rate splitting (RS) is proposed as a transmission scheme to serve multiple users simultaneously following a certain strategy. It was shown that RS provides high data rates compared to orthogonal and non-orthogonal interference management schemes. Considering the high density of OWC networks, the application of RS within each optical cell might not be practical due to severe ICI. In this paper, a novel strategy is derived, referred to as blind interference alignment-rate splitting (BIA-RS), to fully coordinate the transmission among the optical APs, while determining the precoding matrices of multiple groups of users formed beforehand. Therefore, RS can be implemented within each group to manage MUI. The proposed BIA-RS scheme requires two layers of power allocation to achieve high performance. Given that, a max-min fractional optimization problem is formulated to optimally distribute the power budget among the groups and the messages intended to the users of each group. Finally, a power allocation algorithm is designed with multiple Lagrangian multipliers to provide practical and sub-optimal solutions. The results show the high performance of the proposed scheme compared to other counterpart schemes.

This paper investigates the integration of Open Radio Access Network (O-RAN) within non-terrestrial networks (NTN), and optimizing the dynamic functional split between Centralized Units (CU) and Distributed Units (DU) for enhanced energy efficiency in the network. We introduce a novel framework utilizing a Deep Q-Network (DQN)-based reinforcement learning approach to dynamically find the optimal RAN functional split option and the best NTN-based RAN network out of the available NTN-platforms according to real-time conditions, traffic demands, and limited energy resources in NTN platforms. This approach supports capability of adapting to various NTN-based RANs across different platforms such as LEO satellites and high-altitude platform stations (HAPS), enabling adaptive network reconfiguration to ensure optimal service quality and energy utilization. Simulation results validate the effectiveness of our method, offering significant improvements in energy efficiency and sustainability under diverse NTN scenarios.

To achieve multi-Gb/s data rates in 6G optical wireless access networks based on narrow infrared (IR) laser beams, a high-speed receiver with two key specifications is needed: a sufficiently large aperture to collect the required optical power and a wide field-of-view (FOV) to avoid strict alignment issues. This paper puts forward the systematic design and optimisation of multi-tier non-imaging angle diversity receivers (ADRs) composed of compound parabolic concentrators (CPCs) coupled with photodiode (PD) arrays for laser-based optical wireless communication (OWC) links. Design tradeoffs include the gain-FOV tradeoff for each receiver element and the area-bandwidth tradeoff for each PD array. The rate maximisation is formulated as a non-convex optimisation problem under the constraints on the minimum required FOV and the overall ADR dimensions to find the optimum configuration of the receiver bandwidth and FOV, and a low-complexity optimal solution is proposed. The ADR performance is studied using computer simulations and insightful design guidelines are provided through various numerical examples. An efficient technique is also proposed to reduce the ADR dimensions based on CPC length truncation. It is shown that a compact ADR with a height of$\leq 0.5$cm and an effective area of$\leq 0.5$cm2 reaches a data rate of 12 Gb/s with a half-angle FOV of 30° over a 3 m link distance.

Recently, Optical wireless communication (OWC) have been considered as a key element in the next generation of wireless communications due to its potential in supporting unprecedented communication speeds. In this paper, infrared lasers referred to as vertical-cavity surface-emitting lasers (VC-SELs) are used as transmitters sending information to multiple users. In OWC, rate-maximization optimization problems are usually complex due to the high number of optical access points (APs) needed to ensure coverage. Therefore, practical solutions with low computational time are essential to cope with frequent updates in user-requirements that might occur. In this context, we formulate an optimization problem to determine the optimal user association and resource allocation in the network, while the serving time is partitioned into a series of time periods. Therefore, cooperative ANN models are designed to estimate and predict the association and resource allocation variables for each user such that sub-optimal solutions can be obtained within a certain period of time prior to its actual starting, which makes the solutions valid and in accordance with the demands of the users at a given time. The results show the effectiveness of the proposed model in maximizing the sum rate of the network compared with counterpart models. Moreover, ANN-based solutions are close to the optimal ones with low computational time.

The design of a compact high-speed and wide field of view (FOV) receiver is challenging due to the presence of two well-known trade-offs. The first one is the area-bandwidth trade-off of photodetectors (PDs) and the second one is the gain-FOV trade-off due to the use of optics. The combined effects of these two trade-offs imply that the achievable data rate of an imaging optical receiver is limited by its FOV, i.e., a rate-FOV trade-off. In this paper, we propose an imaging receiver design in the form of an array of (PD) arrays. To control the area-bandwidth trade-off, small PDs are used in an array of arrays structure instead of a single large PD. Moreover, to achieve a reasonable receiver FOV, we use an array of focusing lenses that focus the light individually on each inner PD array. The proposed array of arrays structure provides an effective method to control both gain-FOV trade-off (via an array of lenses) and area-bandwidth trade-off (via arrays of small PDs). We first derive a tractable analytical model for the signal-to-noise ratio (SNR) of an array of PDs that is equipped with a focusing lens assuming maximum ratio combining (MRC). Then, we extend the model to the proposed array of arrays structure and the accuracy of the analytical model is verified based on several Optic Studio-based simulations. Next, we formulate an optimization problem to maximize the achievable data rate of the imaging receiver subject to a minimum required FOV. The optimization problem is solved for two commonly used modulation techniques, namely, on-off keying (OOK) and direct current (DC) biased optical orthogonal frequency division multiplexing (DCO-OFDM) with variable rate quadrature amplitude modulation (QAM). Our results show the limits of high speed wide-FOV imaging receivers that can support mobility. For example, it is demonstrated that a data rate of$\sim 24$Gbps with a FOV of 15° is achievable using OOK with a total receiver size of 2 cm$\!\times \!\,\,2$cm.

Optical wireless communication offers unprecedented communication speeds that can support the massive use of the Internet on a daily basis. In indoor environments, optical wireless networks are usually multi-user multiple-input multiple-output (MU-MIMO) systems, where a high number of optical access points (APs) is required to ensure coverage. In this work, a laser-based optical wireless network is considered for serving multiple users. Moreover, blind inference alignment (BIA) is implemented to achieve a high degree of freedom (DoF) without the need for channel state information (CSI) at transmitters, which is difficult to provide in such wireless networks. Then, an objective function is defined to allocate the resources of the network taking into consideration the requirements of users and the available resources. This optimization problem can be solved through exhaustive search or distributed algorithms. However, a practical algorithm that provides immediate solutions in real time scenarios is required. In this context, an artificial neural network (ANN) model is derived in order to obtain a sub-optimal solution with low computational time. The implementation of the ANN model involves three important steps, dataset generation, offline training, and real time application. The results show that the trained ANN model provides a significant solution close to the optimal one.

Due to the emergence of new paradigms and services such as 5G/6G, IoT, and more, current deployed wired Data Center Networks (DCNs) are not meeting the required performance metrics due to their limited reconfigurability, scalability, and throughput. To that end, wireless DCNs using technologies such as Optical Wireless Communication (OWC) have become viable and cost-effective solutions as they offer higher capacity, better energy efficiency, and better scalability. This paper proposes an OWC-based spine and leafDCNs where the leaf switches are enabled with OWC transceivers, and the spine switches are replaced by Access Points (APs) in the ceiling connected to a backbone network. The APs are interconnected through a Passive Optical Network (PON) that also connects the architecture with upper network layers. An Infrared (IR) OWC system that employs Wavelength Division Multiplexing (WDM) is proposed to enhance the DCN downlink communication. The simulation (i.e., channel modeling) results show that our proposed data center links achieve good data rates in the data center up to 15 Gbps. For the PON, Arrayed Waveguide Grating Routers (AWGRs) that enable WDM are proposed to connect the APs. We evaluate the performance of the considered architecture in term of its power efficiency compared to traditional spine and leaf data centers. The results show that the OWC-enabled DCN reduces the power consumption by 42% compared to traditional the spine and leaf architecture.

Optical wireless Communication (OWC) is a strong candidate in the next generation (6G) of cellular networks. In this paper, a laser-based optical wireless network is deployed in an indoor environment using Vertical Cavity Surface Emitting Lasers (VCSELS) as transmitters serving multiple users. Specifically, a commercially available low-cost VCSEL operating at S50nm wavelength is used. Considering the confined coverage area of each VCSEL, an array of VCSELs is designed to transmit data to multiple users through narrow beams taking into account eye safety regulations. To manage multi-user interference (MUI), Zero Forcing (ZF) is implemented to maximize the multiplexing gain of the network. The energy efficiency of the network is studied under different laser beam waists to find the effective laser beam size that results in throughput enhancement. The results show that the energy efficiency increases with the laser beam waist. Moreover, using micro lenses placed in front of the VCSELs leads to significant increase in the energy efficiency.

In this paper, the design of a multiple-input multiple-output (MIMO) optical wireless communication (OWC) link based on vertical cavity surface emitting laser (VCSEL) arrays is systematically carried out with the aim to support data rates in excess of 1 Tb/s for the backhaul of sixth generation (6G) indoor wireless networks. The proposed design combines direct current optical orthogonal frequency division multiplexing (DCO-OFDM) and a spatial multiplexing MIMO architecture. For such an ultra-high-speed line-of-sight (LOS) OWC link with low divergence laser beams, maintaining alignment is of high importance. In this paper, two types of misalignment error between the transmitter and receiver are distinguished, namely, radial displacement error and orientation angle error, and they are thoroughly modeled in a unified analytical framework assuming Gaussian laser beams, resulting in a generalized misalignment model (GMM). The derived GMM is then extended to MIMO arrays and the performance of the MIMO-OFDM OWC system is analyzed in terms of the aggregate data rate. Novel insights are provided into the system performance based on computer simulations by studying various influential factors such as beam waist, array configuration and different misalignment errors, which can be used as guidelines for designing short range Tb/s MIMO OWC systems.

A disaster may not necessarily demolish the telecommunications infrastructure, but instead it might affect the national grid and cause blackouts, consequently disrupting the network operation unless there is an alternative power source(s). In disaster-resilient networks, fiber cut, datacenter destruction, and node isolation have been studied before with different scenarios, but the power outage impact has not been investigated before. In this paper, power outages are considered, and the telecommunication network performance is evaluated during a blackout. A mixed Integer Linear Programming (MILP) model is developed to evaluate the network performance for a single node blackout under two scenarios: minimization of blocking and minimization of renewable and battery energy consumption. Insights analyzed from the MILP model results have demonstrated the trade-off between the two evaluated optimization cost functions and shown that the proposed scheme can extend the network lifetime while minimizing the required amount of backup energy.

Optical wireless communication provides data transmission at high speeds which can satisfy the increasing demands for connecting a massive number of devices to the Internet. In this paper, vertical-cavity surface-emitting(VCSEL) lasers are used as transmitters due to their high modulation speed and energy efficiency. However, a high number of VCSEL lasers is required to ensure coverage where each laser source illuminates a confined area. Therefore, multiple users are classified into different sets according to their connectivity. Given this point, a transmission scheme that uses blind interference alignment (BIA) is implemented to manage the interference in the laser-based network. In addition, an optimization problem is formulated to maximize the utility sum rate taking into consideration the classification of the users. To solve this problem, a decentralized algorithm is proposed where the main problem is divided into sub-problems, each can be solved independently avoiding complexity. The results demonstrate the optimality of the decentralized algorithm where a sub-optimal solution is provided. Finally, it is shown that BIA can provide high performance in laser-based networks compared with zero forcing (ZF) transmit precoding scheme.

The rapid growth of time-sensitive applications and services has driven enhancements to computing infrastructures. The main challenge that needs addressing for these applications is the optimal placement of the end-users’ demands to reduce the total power consumption and delay. One of the widely adopted paradigms to address such a challenge is fog computing. Placing fog units close to end-users at the edge of the network can help mitigate some of the latency and energy efficiency issues. Compared to the traditional hyperscale cloud data centres, fog computing units are constrained by computational power, hence, the capacity of fog units plays a critical role in meeting the stringent demands of the end-users due to intensive processing workloads. In this paper, we first propose a federated fog computing architecture where multiple distributed fog cells collaborate in serving users. These fog cells are connected through dedicated Passive Optical Network (PON) connections. We then aim to optimize the placement of virtual machines (VMs) demands originating from the end-users by formulating a Mixed Integer Linear Programming (MILP) model to minimize the total power consumption. The results show an increase in processing capacity and a reduction in the power consumption by up to 26% compared to a Non-Federated fogs computing architecture.

Currently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions. In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks’ control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial efforts toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks.

In this paper, we study the impact of big data's volume and variety dimensions on energy efficient big data networks (EEBDN) by developing a mixed integer linear programming (MILP) model to encapsulate the distinctive features of these two dimensions. First, a progressive energy efficient edge, intermediate, and central processing technique is proposed to process big data's raw traffic by building processing nodes (PNs) in the network along the way from the sources to datacenters. Second, we validate the MILP operation by developing a heuristic that mimics, in real time, the behavior of the MILP for the volume dimension. Third, we test the energy efficiency limits of our green approach under several conditions where PNs are less energy efficient in terms of processing and communication compared to data centers. Fourth, we test the performance limits in our energy efficient approach by studying a “software matching” problem where different software packages are required to process big data. The results are then compared to the classical big data networks (CBDN) approach where big data is only processed inside centralized data centers. Our results revealed that up to 52% and 47% power saving can be achieved by the EEBDN approach compared to the CBDN approach, under the impact of volume and variety scenarios, respectively. Moreover, our results identify the limits of the progressive processing approach and in particular the conditions under which the CBDN centralized approach is more appropriate given certain PNs energy efficiency and software availability levels.

Cloud computing is expected to be a major factor that will dominate the future Internet service model. This paper summarizes our work on energy efficiency for cloud networks. We develop a framework for studying the energy efficiency of four cloud services in IP over WDM networks: cloud content delivery, storage as a service (StaaS), and virtual machines (VMS) placement for processing applications and infrastructure as a service (IaaS). Our approach is based on the co-optimization of both external network related factors such as whether to geographically centralize or distribute the clouds, the influence of users' demand distribution, content popularity, access frequency and renewable energy availability and internal capability factors such as the number of servers, switches and routers as well as the amount of storage demanded in each cloud. Our investigation of the different energy efficient approaches is backed with Mixed Integer Linear Programming (MILP) models and real time heuristics.

Environmental sustainability in high capacity networks and cloud data centers has become one of the hottest research subjects. In this paper, we investigate the effective use of renewable energy and hence resource allocation in core networks with clouds as a means of reducing the carbon footprint. We develop a Green Virtual Network Embedding (GVNE) framework for minimizing the use of non-renewable energy through intelligent provisioning of bandwidth and cloud data center resources. The problem is modeled as a mixed integer linear program (MILP). The results show that it is better to instantiate virtual machines in cloud data centers that have access to abundant renewable energy even at the expense of traversing several links across the network. The GVNE model reduces the overall CO2emissions by up to 32% for the network considering solar power availability and data center locations.

In this paper, we investigate maximizing the profit achieved by infrastructure providers (InPs) from embedding virtual network requests (VNRs) in IP/WDM core networks with clouds. We develop a mixed integer linear programming (MILP) model to study the impact of maximizing the profit on the power consumption and acceptance of VNRs. The results show that higher acceptance rates do not necessarily lead to higher profit due to the high cost associated with accepting some of the requests. The results also show that minimum power consumption can be achieved while maintaining the maximum profit

In this paper, we develop a Mixed Integer Linear Programming (MILP) model to study the impact of renewable energy availability, represented by wind farms, on the location of clouds and the content replication schemes of cloud content over IP/WDM networks. In our analysis, we assume that renewable energy is only available to power clouds while the IP/WDM network is powered by non-renewable energy. Our results show that popularity based replication in clouds is the most energy efficient content replication scheme when the clouds are powered only by non-renewable energy sources or when renewable energy availability is limited. With abundant renewable energy, a cloud with a full copy of the content can be built at each node. However, the model should achieve a trade-off between the transmission power losses to deliver renewable energy from wind farms to clouds and the non-renewable power consumption of the IP/WDM network. We discuss this trade-off and show how to optimize the transmission power losses of renewable energy while minimizing the non-renewable network power consumption.

In this paper, we investigate the joint optimization of power, electricity cost and propagation delay in IP over WDM networks employing renewable energy. We develop a mixed integer linear programming (MILP) model to jointly minimize the three parameters and compare its results to the results of optimizing these parameters individually. The models results show that the joint optimization maintains the power consumption and electricity cost savings obtained by the non-renewable power-minimized and the electricity cost-minimized models while hardly affecting the propagation delay. Compared to the delay-minimized model, the joint optimization model achieves power consumption and electricity cost savings of 73% and 74%, respectively under the non-bypass approach considering a unicasting traffic profile. The power and cost savings under an anycasting traffic profile increases to 82%.

Orthogonal Frequency Division Multiplexing (OFDM) has been proposed as an enabling technique for elastic optical networks to support heterogeneous traffic demands. In this paper, we investigate the energy efficiency of rate and modulation adaptive optical OFDM-based networks. A mixed integer linear programming (MILP) model is developed to minimize the total power consumption of optical OFDM networks. We differentiate between two optimization schemes: power-minimized and spectrum-minimized optical OFDM-based networks. The results show that while similar power consumption savings of up to 31% are achieved by the two schemes compared to conventional IP over WDM networks, the spectrum-minimized optical OFDM is 51% more efficient in utilizing the spectrum compared to the power-minimized optical OFDM.

In this paper, we evaluate the energy consumption of BitTorrent based peer to peer (P2P) content distribution systems in bypass IP/WDM core networks and compare it to client-server (C/S) systems. A Mixed Integer Linear Programming (MILP) model is developed to carry out the comparison. Our results for homogeneous peers with similar upload capacities show that the original BitTorrent, based on random peer selection, has comparable energy consumption to the C/S model. The results also reveal that the power-minimized BitTorrent model achieves 30% energy savings compared to the C/S model as it converges to locality. Furthermore, a heterogeneous BitTorrent system with two upload capacity classes is investigated and the results show a 50% reduction in energy consumption compared to a C/S model. For real-time implementation, we develop a simple heuristic based on the model insights. Comparable power savings are achieved with a reduction of only 13% in the download rate.

In this paper we propose the use of renewable energy in IP over WDM networks to reduce the non-renewable energy consumption and consequently the CO2emission. A Linear Programming (LP) model and a novel heuristic, REO-hop, are developed for improving renewable energy utilization. The results show that the non-renewable energy consumption can be reduced by up to 73%. We also study IP over WDM networks with data centres and investigate the problem of whether to locate data centres next to renewable energy or to transmit renewable energy to data centres. Taking into account the electrical power transmission losses the results show that by optimizing the data centre locations such that the network's power consumption is minimized up to 71% of the network's power consumption can be saved.

In this paper we propose a low carbon emission IP over WDM network where renewable energy is used to reduce the CO2emissions at a given energy consumption level. We develop a Linear Programming (LP) model to minimize the non-renewable energy consumption of the network and propose a novel heuristic for improving renewable energy utilization. Compared with routing in the electronic layer, the results show that routing in the optical layer coupled with renewable energy nodes significantly reduces the CO2emission of the IP over WDM network by up to 94%, and the new heuristic introduced has little impact on the QoS. In order to identify the impact of the number and the location of nodes that employ renewable energy on the non-renewable energy consumption of the whole network, we also build another LP model. The results show that the nodes at the centre of the network have more impact than other nodes if they use renewable energy sources.

As storage area networks (SANs) are increasingly replacing traditional direct-attached storage (DAS) in many large data centers, many studies are considering extending SANs over large distances. SANs in a metropolitan wavelength-division multiplexing (WDM) scenario create asymmetrical traffic and hot-node scenarios. This paper proposes a sectioned WDM metropolitan ring network as a suitable extension for SANs. Two node architectures are considered, one uses two fixed transmitters and the other uses a tuneable transmitter. A tunable receiver is used with both architectures. For the two fixed-transmitters node architecture, two versions of medium access control (MAC) protocol are introduced. Simulation is carried out under both symmetrical and asymmetrical traffic sources. Performance of the sectioned ring is compared with that of the SAN ring network architecture proposed in [1].