AI-Powered Multilayer IP Optical Traffic and Energy Co-Optimization System

Abstract

The invention is an AI-supported multilayer IP optical traffic and energy co-optimization system that includes IP network layer (1), which enables data transmission over the IP layer, optical network layer (2), which carries high-capacity data over the optical layer, multi-layer model (3), which enables the evaluation of IP and optical layers together, traffic monitoring module (4), which collects traffic and usage data on the network, energy measurement module (5), which calculates the energy consumption of different paths, optimization engine (6), which makes the most suitable layer and routing decision, dynamic routing engine (7) which directs traffic to the determined path, and operation interface (8) which enables monitoring and information of the system;It is an AI-supported multi-layer IP-optical traffic and energy co-optimization system that offers an optimization approach that considers the IP and optical layers together, enables the evaluation of the IP layer, optical layer, and energy consumption together, allows the system to make a decision including layer selection for each traffic, and ensures more balanced use of network resources, reduction of unnecessary energy consumption, better distribution of traffic density, and maintenance of service quality.

Description

1 TARIFF AI-Powered Multilayer IP Optical Traffic and Energy Co-Optimization System Technical Area 5 The invention relates to telecommunications operators' IP / MPLS backbone networks, metro networks, and optical networks. Designed for use in transportation systems, it improves both the performance of traffic carried on the network. AI-powered systems that also enable more energy-efficient management. Layered IP relates to optical traffic and energy co-optimization systems. 10 State of the Art Today, IP routing (OSPF, BGP, MPLS-TE) uses a fixed wavelength at the optical layer and path usage, manual interventions via network management systems and traffic 15 Methods such as limited optimization based on density are used. However, these methods... It has some shortcomings. The IP and optical layers operate independently. Traffic routing. The decisions do not take energy consumption into account. The same traffic can always be transported through the same layer. Even if a more efficient alternative exists, it won't be used. A holistic optimization across the entire network is needed. It cannot be done. 20 The same traffic can be transported through different paths on a network. In some cases, the optical layer is less important. When it comes to energy consumption, the IP layer may be more suitable in some cases. However, current systems... It cannot dynamically decide which layer is more suitable. Therefore, it is unnecessary. Energy consumption occurs, traffic becomes congested in certain areas, and network resources are not used efficiently. 25 In conclusion, due to the negative aspects described above and the current solutions being the subject of discussion... Due to its shortcomings, it has become necessary to make improvements in the relevant technical field. Purpose of the Invention 30 The invention was created by drawing inspiration from existing situations and overcoming the aforementioned drawbacks. It aims to solve the problem. The main goal of the invention is AI 35, which offers an optimization approach that considers both the IP and optical layers together. The goal is to provide a multi-layered IP supported optical traffic and energy co-optimization system. 2 Another aim of the invention is to ensure a more balanced use of network resources and to reduce unnecessary energy consumption. reducing consumption, better distribution of traffic congestion and improving service quality The aim is to ensure its protection. Another aim of the invention is to achieve a combined 5% efficiency for the IP layer, the optical layer, and energy consumption. The aim is to provide an evaluation; the system makes a decision including layer selection for each traffic. The goal is to enable it to give. To achieve the purposes described above, the invention enables data transmission over the IP layer. IP network layer, which provides high-capacity data transmission over the optical layer, optical 10 The multi-layer model, which allows the network layer, IP layer, and optical layer to be considered together, The traffic monitoring module, which collects traffic and usage data, analyzes the energy consumption of different roads. The energy measurement module calculates and determines the most suitable layer and orientation. optimization engine, dynamic routing engine that directs traffic to the designated path, and AI-powered multi-system with an operational interface that enables monitoring and reporting. IP is a layered optical traffic and energy co-optimization system, characterized by its combination of IP and optical layers. It offers an optimization approach that considers the IP layer, optical layer, and energy layer together. enabling the combined assessment of consumption, and allowing the system to select a layer for each type of traffic. enabling decision-making that includes more balanced use of network resources and avoiding unnecessary ones. reducing energy consumption, better distribution of traffic congestion and service 20 AI-powered multilayer IP optical traffic and energy partnership that ensures quality preservation. It is an optimization system. The structural and characteristic features and all the advantages of the invention are given in the figures below. Thanks to the detailed explanation written with references to the figures, it is clearer than ever. 25 It will be understood. Figures that will help understand the invention. Figure 1 shows a general representation of the system that is the subject of the invention. Explanation of Part References 1. IP network layer 2. Optical network layer 3. Multi-layer model 4. Traffic monitoring module 35 5. Energy measurement module 3 6. Optimization engine 7. Dynamic steering motor 8. Operating Interface Detailed Description of the Invention 5 This detailed explanation describes the AI-powered multilayer IP optical traffic and energy that is the subject of the invention. The preferred structures of the collaborative optimization system are not only better at the subject. It is explained in a way that facilitates understanding. AI-powered multilayer IP optical traffic and energy co-optimization system, IP layer IP network layer (1), which enables data transmission over the optical layer, high Optical network layer (2) which carries high-capacity data, IP and optical layer together multi-layer model (3), which provides traffic monitoring that collects traffic and usage data on the network. module (4), energy measurement module (5) which calculates the energy consumption of different ways, the most suitable 15 The optimization engine (6) which makes the layer and routing decision, directs the traffic to the determined path. dynamic routing engine (7) and monitoring and informing of the system It includes the operating interface (8) that provides. AI-powered multilayer IP optical traffic and energy co-optimization system, IP and optical 20 It offers an optimization approach that considers the layers together. The system includes the IP layer and the optical layer. It enables the simultaneous evaluation of energy consumption and traffic levels. The system provides this for each traffic flow. It makes a decision that includes layer selection. This ensures a more balanced and efficient network overall. Efficient use is ensured. AI-powered multilayer IP optical traffic and energy co-optimization system, network resources more balanced use, reduction of unnecessary energy consumption, traffic congestion This ensures better distribution and maintains service quality. AI-powered multilayer IP optical traffic and energy co-optimization system, IP routing 30 existing systems, optical transport systems and network management systems work together It provides optimization without changing the structure. AI-powered multilayer IP optical traffic and energy co-optimization system, on the network It constantly updates its decisions according to the changes. 35 4 AI-powered multilayer IP optical traffic and energy co-optimization system, IP and optical It offers an optimization approach that considers the layers together. The system consists of three basic structures. It consists of: The multi-layer model (3) allows for the evaluation of the IP and optical layers together. In this model IP node and connections, optical paths (lightpath), traffic density and capacity information are all included in a 5 It is being evaluated periodically. Traffic and energy analysis enables the system to analyze the data it collects from the network. The system analyzes traffic density, latency, bandwidth usage, and link-based energy. It uses consumption data. Using this information, the most suitable option for each traffic type is determined. It can be determined. Dynamic layer selection and routing allows the system to make decisions for each traffic flow. The system determines whether traffic will pass through the IP layer or the optical layer. It can decide which lane to pass through. For example, traffic requiring high bandwidth 15 It can be routed to the optical layer. Traffic requiring lower latency is routed to the IP layer. It can be stopped. Once this decision is made, traffic is redirected to the appropriate route. AI-powered multilayer IP optical traffic and energy co-optimization system, IP layer, optical It enables the simultaneous evaluation of energy consumption and energy levels. In current systems, 20 These structures operate separately. AI-powered multilayer IP, optical traffic, and energy are shared. In an optimization system, the system makes a decision that includes layer selection for each traffic type. This means that traffic will not only pass through a single road, but also through which layer it will pass. It is determined and directed accordingly. This ensures a more balanced and efficient usage across the network. is provided. 25 AI-powered multilayer IP optical traffic and energy co-optimization system, IP network layer (1) and optical network layer (2) collects traffic and usage data. This process Traffic monitoring is performed by the module (4). The collected data is collected using a multi-layer model. (3) is transferred to it. This model allows for joint assessment of the overall state of the network. 30 The energy measurement module (5) shows how much energy different paths consume. It calculates the traffic density, delay and energy information. The optimization engine (6) calculates the traffic density, delay and energy information. By evaluating them together, it makes the most appropriate layer and orientation decision. Dynamic The routing engine (7) directs traffic to the designated road by implementing this decision. The system is monitored via the operation interface (8) and team 35 is called upon when necessary. They are being informed.

Claims

REQUESTS 1. Telecommunications operators' IP / MPLS backbone networks, metro networks, and optical networks. AI that optimizes both traffic performance and energy consumption in transportation systems. It is a multi-layered IP optical traffic and energy co-optimization system with support for 5 features. IP network layer (1), which enables data transmission over the IP layer, Optical network layer (2) which carries high-capacity data over the optical layer, IP network layer (1) and optical network layer (2) topology, traffic load, capacity multilayer models the state, connection information, and inter-layer relationships together. model (3), 10 Traffic density, bandwidth over IP network layer (1) and optical network layer (2) collecting bandwidth usage, latency, connection occupancy rate, and quality of service data traffic monitoring module (4), There are different ways that can be used on the IP network layer (1) and optical network layer (2) and Energy measurement module (5) which calculates the energy consumption of the layer options, 15 data from traffic monitoring module (4) and energy measurement module (5) multilayer by evaluating on model (3) the most suitable layer, path and routing decision generating optimization engine (6), According to the decision determined by the optimization engine (6), the traffic is IP network layer (1) or dynamic routing which directs to the appropriate path via optical network layer (2) 20 engine (7), system operating status, optimization decisions, energy saving information, traffic Operation interface that makes distribution and alerts traceable (8) It includes.

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