Latency-optimized high performance Data Vortex optical switching network

With the increasingly complex environment of optical backbone network, the threat it faces also increases. Network survivability optimization is one of the most important research fields of communication networks. For a given optical network, it is of great practical significance to build or backup links to reduce the influence caused by external disturbances. In this paper, combined with the resources of limited equipment, the appropriate network survivability evaluation system was selected, and the determined network attack mode was used to finally establish the survivability optimization model of optical backbone network. At the same time, combined with the specific case, the genetic algorithm was used to solve the established damage resistance optimization model, and the verification and analysis were carried out. Through the case simulation, when 10% link resources are invested, the network survivability increases by about 10%, and the cost of unit survivability is 2558.4. When 15% of the link resources are invested, the network survivability increases by about 25%, and the cost per unit survivability is about 3733.3. When 25% of the link resources are invested, the network survivability increases by about 30%, and the cost per unit survivability is about 5521.3. When 35% of the link resources are invested, the network survivability increases by about 60%, and the cost per unit survivability is about 3909.0. Therefore, according to the existing resources and the expected optimization degree, this paper can effectively provide a specific optimization scheme, which provides theoretical support and practical guidance for the destruction resistance optimization of optical networks.

In the process of research on the characteristic detection method for large scale network attacks, due to the use of the current algorithm for large-scale network attack detection, it is unable to describe the attack characteristics of network attacks or detect in low accuracy. Therefore, an attack detection method for large scale network based on cooperative planning is proposed. The method is based on collaborative planning to design the detection characteristic of large scale network attacks, which is transformed into a space search problem. The difference between the parameters of the flow vector and the normal vector of the normal network spatial data is extracted as characteristics, combining Gauss mixture model with the expectation maximization algorithm, to design Lorenz chaotic asynchronous tracking detection algorithm for modeling and detection of network data stream. The experimental simulation shows that the detection method of large scale network attack detection method has high accuracy and high efficiency. Currently, characteristic detection technology for network attack is based on ant colony algorithm, particle algorithm and the fuzzy algorithm for large-scale network. Among them, the commonly characteristic detection method is technology based on the fuzzy algorithm for large scale network attack. However the current algorithms with characteristic detection for network attack could not describe attack characteristics in detail, which lead to low accuracy. In view of the above defect, a method for large scale network attack detection based on cooperative planning is proposed. The method is represented for network attack with characteristic feature, which is transformed into a search problem in the space. To extract difference of parameter vector characteristic of network spatial data flow between to test network and normal network, with Gaussian mixture model and expectation maximization algorithm combined, Lorenz chaotic asynchronous detecting and tracking algorithm is designed to model and test the network data flow. The experimental simulation shows that the detection method for large scale network has high accuracy and efficiency.

The growing computational demands of artificial intelligence motivate the development of optical neural networks (ONNs), while their practical deployment remains constrained by optimization complexity and limited adaptability. We propose a mutual learning knowledge distillation framework for optical diffractive neural networks (ODNNs), synergizing photonic computing with adaptive training strategies. Our architecture enables bidirectional knowledge transfer between parameter-efficient optical student networks and electrical ResNet teachers, leveraging both labeled data and probabilistic guidance. The validation on Fashion-MNIST and Kuzushiji-MNIST demonstrates classification accuracy improvements of 5.9% and 13.88%, respectively, over conventional ODNNs training. In addition, we further developed the photonic to photonic mutual learning computational paradigm, in which an optical network with 0.98 × 106 parameters can bootstrap compact photonic classifiers with 0.19 × 106 parameters, achieving an accuracy of 79.5% on MNIST and yielding an absolute improvement of 9% relative to the noncooperative baseline. Even though the all-optical mutual learning paradigm still has a gap of about 19% with the state-of-the-art ODNNs, the number of parameters of the guided student networks is smaller than the state-of-the-art networks by several or even dozens of times. These results establish a paradigm for overcoming fundamental limitations in ONN optimization, paving the way for a new era in which ONNs can solve large-scale practical problems through photonic–electronic and photonic–photonic co-design.

The subject of the study is an application of evolutionary algorithms to optical node optimization in dense wavelength division multiplexing optical networks. The wider context of the presented research is in essence an improvement of service flexibility and achieving savings in capital expenditures in DWDM networks. Thus, the main objective of the optimization is to minimize capital expenditure, which includes the costs of optical node resources, such as transponders and filters used in new generation of reconfigurable optical add drop multiplexers, etc. For this purpose a model based on integer programming is proposed. The efficiency of the integer programming based software is compared with that of evolutionary algorithms. The results obtained show that there is a large advantage in using evolutionary algorithms for optimizing large optical networks when compared with integer programming and mixed integer programming, whereby the two latter algorithms fail to find the optimal solution within reasonable computational time. The numerical experiments were carried out for realistic networks of different dimensions and traffic demand sets.

The vision of Large Scale Network Analysis (LSNA) states on large amounts of network data, which are produced by social media applications like Facebook, Twitter, and the competitive domain of biological networks as well as their needs for network data extraction and analysis. That raises data management challenges which are addressed by biological, data mining and linked (web) data management communities. So far, mainly these domains were considered when identifying research topics and measuring approaches and progress. We argue that an important domain, the Business Network Management (BNM), representing business and (technical) integration data, implicitely linked and available in enterprises, has been neglected. Not only do enterprises need visibilities into their business networks, they need ad-hoc analysis capabilities on them. In this paper, we introduce BNM as domain, which comes with large scale network data. We discuss how linked business data can be made explicit by what we called Network Mining (NM) from dynamic, heterogeneous enterprise environments to combine it to a (cross-) enterprise linked business data network and state on its different facets w.r.t large network analysis and highlight challenges and opportunities.

Machine learning (ML) is an effective solution to deal with some networking issues in large scale optical transport networks. Alarm (caused by failure, disaster, etc.) prediction is a typical and important use-case, where ML algorithm can work to predict events in advance. By accurate prediction, network administrators can be guided to undertake preventive measures ahead of time, thus avoiding the adverse effect. For training and testing of such event prediction models, it’s first and foremost to build a high-quality data set from real networks. However, the data collected from large-scale optical transport networks are inferior in quality and demand prompt pre-processing, for they are sometimes invalid, incomplete, inconsistent, and even inaccurate. After data pre-processing, there is only a small amount of available data left to build an ML data set, which extremely limits the performance of the alarm prediction model. This paper proposes a machine-learning-based alarm prediction method using self-optimizing data augmentation based on generative adversarial networks (GANs) for optical transport networks. Experimental results on a commercial backbone synchronous digital hierarchy (SDH) network with 274 nodes and 487 links demonstrate that the proposed method can achieve high accuracy for alarm prediction.

Understanding and troubleshooting wide area networks (such as military backbone networks and ISP networks) are challenging tasks due to their large, distributed, and highly dynamic nature. Building a system that can record and replay fine-grained behaviors of such networks would simplify this problem by allowing operators to recreate the sequence and precise ordering of events (e.g., packet-level forwarding decisions, route changes, failures) taking place in their networks. However, doing this at large scales seems intractable due to the vast amount of information that would need to be logged. In this paper, we propose a scalable and reliable framework to monitor fine-grained data-plane behavior within a large network. We give a feasible architecture for a distributed logging facility, a tree-based data structure for log compression and show how this logged information helps network operators to detect and debug anomalous behavior of the network. Experimental results obtained through trace-driven simulations and Click software router experiments show that our design is lightweight in terms of processing time, memory requirement and control overhead, yet still achieves over 99% precision in capturing network events.

Decoupled scheduling in Store-and-Forward OCS networks

This work mainly addresses the design a large scale network using dual stack mechanisms. We focused on the most important theoretical concepts of the IPv6 protocol, such as addressing, address allocation, routing with the OSPF and BGP protocols and routing protocols performance in dual stack network using GNS3 and Wireshark simulators. we have a tendency to measure a perfect model and a true large-scale network atmosphere victimization out there end-to-end activity techniques that focuses on a large-scale IPv4 and IPv6 backbone and created performance the IPv4 and IPv6 network. In this paper, we compiled IPv6 address planning in large scale network, performance statistics of each network in terms of TCP throughput, delay jitters, packet loss rate, and round trip time. It is found that, a minor degradation within the throughput of the TCP, delay jitter, a lower packet loss rate, and a rather longer round trip time are occurred in a real large scale dual stack network.

There are techniques representing data mining, information theory and statistical analysis that can bring machine learning capabilities into the analyses of complex networks. The network analysis projects that can be performed by machine learning are the anomaly detection, prediction of problems and Root Cause Analysis for each. ML turned out to be a powerful tool for analysis and management of large scale networks such as social, communication or transportation network. At large-scale networks, machine learning supports in discovering sophisticated patterns, forecasting of net behaviors the performance optimization and anomaly detection. The roles involve using algorithms that learn from or make predictions/decisions based on network data. This may mean understanding network structure, predicted futures, optimization in the use of resources, anomaly detection, and performance improvement. Machine Learning for Network Analysis, or MLNA in short, is a subarea of machine learning interested in developing algorithms and techniques aimed at analysing, comprehending, and predicting complex behaviors in large-scale networks. MLNA brought together ideas from machine learning, graph theory, and network science to gain insights from network data. You can picture any network as a graph, with entities as nodes or vertices, and the connections or interactions between entities as edges or links. This study also looked at how to analyze network structure using ML, ML-based ways to spot unusual patterns in networks how to make ML network analysis work better and faster, and how to use ML network analysis to create visual representations and understand what they mean.

The authors discuss optical network devices and the performances that can be expected. The architectural features of a multiprocessor prototype machine's software tools, at system and user levels, are described. A survey of applications well suited for reconfigurable architectures using large networks built with optical components is also given. The presented optical network allows any link to be connected to any other link without contention. Such optical networks take advantage of the best features of two technologies by using optics for communications and electronics for computation. It is stated that massively parallel architectures using large networks can be reasonably envisaged with optical components. >

Statistical multiplexing at the optical layer has been considered a critical requirement in order to build the next generation of ultra-high capacity optical transport networks in a cost-efficient manner. However, even today, the state of the art of commercially available optical core networks is based on mature wavelength switching and routing technologies, which lack a transport and control plane architecture that can support statistical traffic multiplexing with guaranteed Quality of Service (QoS) across a wide range of QoS parameters even if they can support fast reconfiguration at msec time scales. For several years, most research efforts have focused on the concepts of Optical Burst Switching (OBS) and Optical Packet Switching (OPS), which are based on the hybrid use of electronic nodes and optical switches to exploit Time Division Multiplexing (TDM) in order to achieve statistical multiplexing and dynamic resource reservation over optical networks. While burst switching has been experimentally proven as a technically feasible technique, its performance suffers especially under strict requirements for QoS guarantees. In this paper we evaluate the performance gains that can be achieved exploiting statistical multiplexing over a large scale core optical network and we demonstrate the efficiency of the CANON architecture (Clustered Architecture for Nodes in an Optical Network) as a viable alternative to OBS, which can achieve both targets for statistical multiplexing gains and QoS guarantees at the same time.

As the performance requirements and scale of high-performance computing (HPC) systems grow rapidly, systems with such a large number of computing nodes present significant challenges in network architecture design because their peak performance is increasingly limited by communication rather than computation. Moreover, certain HPC application always shows some specific communication patterns, and different topologies have various performances with these patterns. To implement a traffic pattern adaptive HPC system, we propose a topology reconfigurable hybrid network architecture based on optical circuit switching (OCS), which could be adjusted into different classical topologies. And an algorithm of topology calculating and a permutation matrix-based optical switch configuration mechanism are proposed to construct network. We discuss network performances with limited OCS switches occupation, and results show that traffic pattern adaptive network improves the performance of latency and throughput to a large extent.

Describes a scalable parallel architecture for real-time, large scale neural network simulations. Currently the SIMD architecture is largely adopted for implementing digital neurocomputers. However, it is not efficient for simulating large scale neural networks in real-time because of its limited scalability and flexibility. The authors investigate, as a solution, a wavefront array processing (WAP) architecture based on asynchronous communications. The authors compare both architectures in scalability, performance, and flexibility for simulating multi-layer perceptrons. The authors also briefly discuss implementing a high performance digital neurocomputer based on the WAP.

Various communication services have been provided. They include environmental monitoring and/or control, ad-hoc communication between mobile nodes, and inter-vehicle communication in intelligent transport systems. As a means of facilitating the above advanced communication services, autonomous decentralized networks, such as wireless sensor networks (Akyildiz et al., 2002; Rajagopalan & Varshney, 2006), mobile ad-hoc networks (Perkins & Royer, 1999; Johnson et al., 2003; Clausen & Jaquet, 2003; Ogier et al., 2003), and wireless mesh networks (Yamamoto et al., 2009), have been intensively researched with great interests. Especially, a wireless sensor network, which is a key network to construct ubiquitous information environments, has great potential as a means of realizing a wide range of applications, such as natural environmental monitoring, environmental control in residential spaces or plants, object tracking, and precision agriculture (Akyildiz et al., 2002). Recently, there is growing expectation for a new network service by a wireless sensor network consisting of a lot of static sensor nodes arranged in a service area and a few mobile robots as a result of the strong desire for the development of advanced systems that can flexibly function in dynamically changing environments (Matsumoto et al., 2009). In this chapter, a large scale and dense wireless sensor network made up of many static sensor nodes with global positioning system, which is a representative network to actualize the above-mentioned sensor applications, is assumed. In a large scale and dense wireless sensor network, generally, hundreds or thousands of static sensor nodes limited resources, which are compact and inexpensive, are placed in a service area, and sensing data of each node is gathered to a sink node by inter-node wireless multi-hop communication. Each sensor node consists of a sensing function to measure the status (temperature, humidity, motion, etc.) of an observation point or object, a limited function of information processing, and a simplified wireless communication function, and it generally operates on a resource with a limited power-supply capacity such as a battery. Therefore, a data gathering scheme and/or a routing protocol capable of meeting the following requirements is mainly needed to prolong the lifetime of a large scale and dense wireless sensor network composed of hundreds or thousands of static sensor nodes limited resources. 1. Efficiency of data gathering 2. Balance of communication load among sensor nodes 3. Adaptability to network topology changes