Optical Switching and Networking最新文献

In this paper, the information value model (IVM) of an access point (AP) and the related intelligent handover model (IHM) are firstly founded in the subway light-fidelity (LiFi) network. At first, based on statistical methods, the IVM is created by analyzing the behavioral characteristics of subway passengers. By the IVM, the probability of user access to each AP can be successfully predicted. Next, based on this probability, the IHM is created by deeply analyzing the alternating blockage problem of handholds. By the IHM, the system can self-adapt to the hysteresis state. This not only effectively avoids ping-pong handovers, but also reduces outage probability. Finally, by simulation, the effectiveness of the above IVM and IHM has been demonstrated. The simulation results indicate that the accuracy of predicting user target AP can be up to 92% by the IVM. Specially, compared with using the standard handover model, the throughput of the network not only increases 11.5%, but also the outage probability of the network decreases 23.6% by using the IHM.

For addressing the problems of high bandwidth blocking probability and low spectrum utilization caused by spectrum fragmentation in elastic optical networks (EONs), a dynamic EONs node architecture for deploying spectrum slicer is studied to transmit the request in the paper. The spectrum slicer can relax spectrum contiguity and improve spectrum fragmentation utilization for request's lightpath establishing. Then, a fragmentation aware routing and spectrum allocation based on spectrum slicing algorithm (SS-FA-RSA) is put forward. In the routing selection phase of SS-FA-RSA, a path weight value formula is designed to trade-off between path length, normalized fragmentation rate and available spectrum slicer, which reorder the candidate paths to select the most appropriate routing path for the request. Moreover, in the spectrum allocation phase, the spectrum slicing algorithm is proposed to increase the utilization of spectrum fragmentation by reusing spectrum fragmentation in EONs. Comparing with other algorithms, the simulation results show the proposed SS-FA-RSA can get better in bandwidth blocking probability and spectrum utilization.

To improve the blocking probability (BP) performance and enhance the resource utilization, a correct decision of routing strategy which is most adaptable to the network configuration and traffic dynamics is essential for adaptive routing in optical datacenter networks (DCNs). A neural network (NN)-assisted decision-making scheme is proposed to find the optimal routing strategy in optical DCNs by predicting the BP performance for various candidate routing strategies. The features of an optical DCN architecture (i.e., the rack number N, connection degree D, spectral slot number S and optical transceiver number M) and the traffic pattern (i.e., the ratio of requests of various capacities R, and the load of arriving request) are used as the input to the NN to estimate the optimal routing strategy. A case of two-strategy decision in the transparent optical multi-hop interconnected DCN is studied. Three metrics are defined for performance evaluation, which include (a) the ratio of the load range with wrong decision over the whole load range of interest (i.e., decision error E), (b) the maximum BP loss (BPL) and (c) the resource utilization loss (UL) caused by the wrong decision. Numerical results show that the ratio of error-free cases over tested cases always surpasses 83% and the average values of E, BPL and UL are less than 3.0%, 4.0% and 1.2%, respectively, which implies the high accuracy of the proposed scheme. The results validate the feasibility of the proposed scheme which facilitates the autonomous implementation of adaptive routing in optical DCNs.

We present a Demand-aware Reconfigurable Data Center Network architecture design (DROAD) with integrated fast-switching optics and space switches that allows dynamic reconfiguration and separation of intra- and inter-cluster connections. The performance analysis results show a 64% improvement in average Flow Completion Time and a significant reduction in TCP session time, as well as a reduced number of sessions needed to be opened compared to traditional electrically-switched leaf-spine networks.

The continuously emerging cloud services provide an unprecedented traffic growth into the large-scale data centers (DCs) globally. In this paper, we introduce an optical DC network (DCN) architecture to organize the servers into computing clusters. Since a high percentage of the total DCΝ traffic is served within a cluster, we assume two distinct networks: the intra-cluster passive optical network that handles the traffic destined to any server of the same cluster and the inter-cluster one to route the traffic to any other cluster. The servers interconnection within the passive optical intra-cluster network causes low power consumption, while the Top-of-Cluster (ToC) switch requires less ports than a relative Top-of-Rack (ToR) one to interconnect the same number of servers within the intra network, reducing even more the total power consumption. In the data plane, the intra- and the inter-cluster networks use separate wavelengths. In the control plane, the software-defined networking (SDN) paradigm is followed. Especially, in each cluster we adopt a cluster controller to coordinate the medium access control (MAC) in both the intra and inter-cluster networks. Unlike other studies that assume electrical connectivity with the controller, we consider that it is performed in the optical domain to guarantee the effective synchronized operation of the control and data planes. In our work, we focus on the intra-cluster network. We propose a synchronous transmission software-defined bandwidth allocation (SD-BA) MAC protocol to fairly coordinate the collisions-free transmission of different quality of service traffic categories in the intra-cluster network, based on the wavelength and time division multiplexing (W&TDM) techniques. The proposed DCN architecture along with the SD-MAC protocol provides scalability and efficiency. Simulations results show that the proposed SD-BA MAC protocol achieves almost 100% bandwidth utilization, while it reaches at high loads 145% higher throughput, 573% lower delay and 233% less dropped packets as compared to the relative DMAC network architecture (Zheng and Sun, Apr. 2020) [24]. Also, the proposed intra-cluster DCN architecture is compared to some other currently leading relative ones in terms of throughput and power consumption and it is proven to be a performance and energy efficient DCN solution.

With the prevalence of some high bandwidth-demanding applications, such as cloud computing, traditional wavelength-division-multiplexing passive optical networks have difficulties in satisfying such growing bandwidth demands due to its limited allocation-flexibility and utilization-efficiency. Therefore, elastic optical networks (EONs). In order to realize the flexibility in EONs, sophisticated routing and spectrum allocation (RSA) algorithms areone of the keyenabling technologies. However, most of the previous RSA algorithms were proposed with invariant routing and spectrum allocation strategies, which ignored considering the time-varying characteristics of EONs due to the variable network architecture and service provisioning. And such time-varying characteristics can deteriorate the spectrum fragmentation and the service blocking performances of EONs, which stimulates the application of various machine-learning technologies in EONs. In this paper, a long short-term memory based routing and spectrum assignment (LSTM-RSA) algorithm is proposed for EONs. By employing the long short-term memory technique to sense the complex status of EONs (e.g. spectral usage on the selected paths), the proposed LSTM-RSA algorithm gradually learns successful strategies through accumulating operation experience in the process of interaction and obtains higher returns through enhanced operation, which helps improve the spectrum fragmentation and the service blocking performances in EONs. Simulation results show that the spectrum fragmentation rate and the blocking rate of the proposed LSTM-RSA algorithm are reduced by about 6% and 8.9%, respectively, when compared to the traditional shortest-path-routing first-fitting RSA algorithm.

Telecom operators' infrastructure is sustained by optical communication networks that provide the means for exchanging large amounts of information, which is essential for many modern society needs. Optical networks are characterized by rapid breakthroughs in a variety of technologies. Relevantly, the last decade encompassed remarkable advances in optical networks’ subfields of signal processing, electronics, photonics, communications, protocols, and control-plane architectures. Hence, these advancements unlocked unprecedented transmission capacities, reconfigurability and programmability, entailing an evolution in the way which networks were designed, planned, and analyzed. In this paper, we review the historical status of optical planning and design tools by focusing on the major enabling technologies and relevant landmarks of the last decade(s). We begin by pinpointing the major breakthroughs in the optical data plane, estimation models capturing the transmission medium behavior and the control plane. We then distil the implications that these advancements entail in the landscape of optical network design and analysis tools, which commonly sit “on top” of the control plane or as a fully separated entity. Then, we speculate with our view for the future, in which automatic validation of optical network operations and dimensioning jointly with learning/artificial intelligence mechanisms will permit zero-touch optical networking: i.e. updating, provisioning, and upgrading network capacities, by means of automation with minimal human intervention. We conclude with a proposal of an architecture that encompasses data and control planes in a comprehensive manner for paving the way towards zero-touch optical networking.

The survivable logical topology mapping (SLTM) problem in IP-over-WDM networks is to map each link in the logical topology (IP layer) onto a lightpath in the physical topology (optical layer) such that a failure of a physical link does not cause the logical topology to become disconnected. This problem is known to be NP-complete. For this SLTM problem, two lines of investigations have been reported in the literature: the mathematical programming approach [1] and the structural approach introduced by Kurant and Thiran in [2] and pursued by Thulasiraman et al. [3,4,5]. In this paper we present an integrated treatment of the theoretical foundation of the survivable topology mapping problem presented in [3,4,5]. We believe that the algorithmic strategy developed in this paper will serve as an important phase in any strategy in the emerging area of resilient slicing of elastic optical networks. We conclude with a comparative evaluation, based on simulations, of the different algorithmic strategies developed in the paper, and also pointing to applications beyond IP-over-WDM optical networks, in particular, survivable design of inter-dependent multi-layer cyber physical systems such as smart power grids.

In today's wide area networks, especially in Optical Transport Networks (OTN) with Software Defined Networking (SDN) features enabled over Wavelength Division Multiplexing (WDM), Bandwidth on Demand (BoD) is an important service that can be satisfied by dynamic end-to-end service provisioning. Service provisioning time (SPT) and Blocking Probability (BP) are critical performance metrics for the users and carriers. This paper extends the concept of the Resource Delayed Release (RDR) strategy for WDM networks. The basic idea of this strategy is to introduce a delay in releasing the optical channel, when the channel is no longer carrying any services. This delay can help speed up the provisioning time for carrying the next service request, avoiding the time usually taken to establish a new optical channel. The main goals of the RDR method are to reduce SPT and BP while simultaneously satisfying the quality of service (QoS) constraints. In this paper, we investigate the effects of uniform and non-uniform traffic on the performance of RDR strategy. For non-uniform traffic simulation, we use a mesh topology with the 14 most populous cities in USA as of 2018 and model the non-uniform traffic based on population density. Further, we introduce a new metric called the Bandwidth Blocking Probability (BBP) to measure the quality of the service offered by the network. Simulation results show advantages of using the RDR method under a wide variety of traffic scenarios for both uniform and non-uniform traffic distributions compared to the traditional method. RDR reduces SPT by 45–90% for uniform traffic and 41–75% for non-uniform traffic. RDR reduces BP by 35–85% for uniform traffic and 30–75% for non-uniform traffic. Additionally, RDR lowers BBP by 31–73% for uniform traffic and 29–68% for non-uniform traffic.