System for distributed AI content routing in cloud-native service meshes

Abstract

A system (100) for distributed AI-driven content routing within a cloud-native service network, comprising: a. a traffic data collection module configured to continuously collect and aggregate real-time traffic data from services within the service network, including data related to traffic volume, latency, error rates and service health; b. an AI-based traffic analysis and prediction module that uses machine learning algorithms to process the collected traffic data and predict network conditions, detect anomalies, and predict potential congestion or failure points; c. a dynamic content routing module integrated into the service network, configured to dynamically adjust content routing decisions based on the predictions and insights provided by the AI-based traffic analysis module to optimize resource utilization and minimize latency; d. a fault tolerance and self-healing module that continuously monitors the state of the network and, in the event of a failure or disruption, triggers automatic recovery actions, such as rerouting traffic, providing additional resources or activating backup protocols; e. a distributed AI model integration and management module configured to distribute AI processing across multiple nodes in the service mesh, enabling decentralized decision-making and efficient model updating and synchronization; f. a monitoring and feedback module designed to track system performance, collect real-time feedback on routing decisions and troubleshooting actions, and ensure continuous optimization through an iterative feedback loop; g. the system continuously adapts to changing network conditions, thus ensuring optimal content delivery and fault resistance across the entire service network.

Description

[0001] System for Distributed AI Content Routing in Cloud-Native Service Meshes - The present invention relates to a system for distributed AI-driven content routing in cloud-native service meshes. It utilizes advanced machine learning algorithms to dynamically manage and route content across distributed services, ensuring optimal performance, scalability, and fault tolerance. The system improves the efficiency of service communication and decision-making processes in cloud environments and enables the seamless integration of AI into service mesh architectures.

[0002] The increasing complexity of modern cloud-native architectures has led to the widespread adoption of service meshes to manage communication between microservices. Service meshes provide a robust framework for handling traffic routing, service discovery, and security, but managing content routing in dynamic and scalable environments remains challenging. As applications become increasingly distributed and traffic patterns become more unpredictable, traditional routing mechanisms struggle to ensure optimal performance and efficiency.

[0003] To address these challenges, artificial intelligence (AI) has been increasingly integrated into cloud-native systems to enable adaptive and intelligent decision-making. AI models can analyze large amounts of real-time data, detect traffic patterns, and predict potential bottlenecks or outages in the network. However, seamlessly integrating AI into service mesh architectures for content routing remains an unsolved problem, requiring sophisticated mechanisms for effective deployment.

[0004] Existing content routing solutions rely heavily on preconfigured rules and static models that cannot adapt to changing network conditions. These systems lack the ability to self-optimize or learn from ongoing traffic, resulting in suboptimal performance during peak loads or when new services are introduced. Furthermore, most AI-driven approaches are not designed to operate efficiently in service mesh environments that require low-latency, high-throughput communication.

[0005] The present invention aims to overcome these limitations by presenting a system that integrates AI-driven content routing with cloud-native service meshes. The proposed system leverages reinforcement learning and other machine learning techniques to dynamically adjust routing decisions based on real-time traffic analysis, optimize data flow, and ensure efficient resource utilization. This innovation enables smarter, more scalable communication between services while maintaining overall system reliability and performance.

[0006] Additionally, the system leverages distributed AI models to ensure content routing decisions are made decentralized, minimizing latency and increasing fault tolerance. By continuously learning from the data generated by the service network, the AI system can predict potential problems, adapt to changing conditions, and proactively optimize content delivery.

[0007] One objective of this disclosure is to enable dynamic, real-time content routing decisions based on AI predictions and to optimize traffic flow.

[0008] Another objective of this disclosure is to improve the efficiency of the service network by reducing latency and improving resource utilization.

[0009] Another objective of this disclosure is to provide fault tolerance and self-healing capabilities to ensure continuous availability of services.

[0010] Another goal of this disclosure is to seamlessly scale with large, distributed cloud-native environments and support decentralized AI decision-making.

[0011] Another objective of this disclosure is to continuously adapt to changing network conditions through real-time data analysis and feedback loops.

[0012] Another objective of this disclosure is to minimize downtime by automatically rerouting traffic in the event of network failures or interruptions.

[0013] Another objective of this disclosure is to integrate with third-party monitoring tools for enriched traffic data and improved decision making Another objective of this disclosure is to improve overall system performance and reliability, thereby making content delivery in complex cloud infrastructures more efficient.

[0014] The present invention is generally a distributed AI-driven content routing system for cloud-native service networks designed to optimize traffic flow and resource utilization. It leverages real-time data collection and machine learning algorithms for dynamic, intelligent routing decisions.

[0015] An Embodiment of the Present Invention A key feature of the system is its ability to analyze and predict network conditions, such as congestion and outages, through advanced AI-based traffic analysis. This ensures that content is routed efficiently even in highly dynamic environments.

[0016] In another embodiment of the invention, the system's dynamic content routing module automatically adjusts routing paths based on AI predictions, thereby reducing latency and improving service efficiency. It ensures that content delivery is optimized and resources are used effectively.

[0017] Another embodiment of the invention includes a fault tolerance and self-healing module to increase reliability. This module detects failures or malfunctions and takes corrective action, such as rerouting traffic or providing additional resources, to ensure minimal downtime.

[0018] Another embodiment of the invention is focused on scalability and uses a distributed AI model integration approach. This allows the system to operate efficiently in large service mesh environments where decision-making is decentralized across multiple nodes.

[0019] Another embodiment of the invention is the continuous monitoring and embedding of feedback mechanisms into the system to track performance and optimize routing decisions. Feedback is used to refine AI models to ensure that the system continuously adapts to changing network conditions.

[0020] Another embodiment of the invention is that the fault tolerance module enables proactive service discovery and load balancing, ensuring that traffic is always directed to available services in the event of failures. This guarantees consistent and reliable content delivery in the event of interruptions.

[0021] Another embodiment of the invention is that the invention provides a robust, adaptive solution for intelligent content routing in cloud-native service networks that improves performance, fault resilience, and scalability. It optimizes communication efficiency and is thus suitable for complex, distributed cloud environments.

[0022] The invention is explained again below with reference to the figure. It shows: Fig. : a system (100) for distributed AI-driven content routing within cloud-native service networks.

[0023] The present invention relates to a system (100) for distributed AI-driven content routing in cloud-native service networks, as described in Fig. which consists of several key modules designed to work synergistically to optimize the performance, scalability, and reliability of content delivery. Traffic data acquisition module

[0024] This module is responsible for collecting and aggregating real-time traffic data from the various services within the cloud-native environment. It collects data such as traffic volume, latency, error rates, and service health, which are critical for analyzing network conditions. The data is continuously monitored and processed to ensure the AI system receives up-to-date information for decision-making. This module forms the foundation of the system by providing the necessary data inputs for the AI algorithms. AI-based module for traffic analysis and prediction

[0025] This module analyzes real-time traffic data using advanced machine learning techniques. The AI algorithms process patterns in the data to predict traffic behavior, detect anomalies, and identify potential congestion or failure points in the service network. It uses techniques such as reinforcement learning to continuously improve its predictions and decisions based on past network conditions and performance metrics. This predictive capability is critical to ensuring the system can adapt to changing traffic loads and network topologies. Dynamic content routing module

[0026] This module, the heart of the invention, uses insights gained from AI traffic analysis to dynamically adjust content routing decisions. Based on real-time traffic forecasts and historical data, the system can reroute content to less congested or more efficient paths, thereby reducing latency and optimizing resource utilization. This module is fully integrated into the service mesh architecture, allowing it to seamlessly change the routing paths of microservices without requiring manual intervention or preconfigured rules. It ensures that content is routed to maximize performance and minimize interruptions. Fault tolerance and self-healing module

[0027] To ensure reliability and availability, the invention includes a fault-tolerance and self-healing module that continuously monitors the network's health. In the event of network outages, service outages, or other disruptions, this module collaborates with the AI system to quickly detect the problem and perform recovery actions. It can redirect traffic to available services, provision new resources, or trigger preconfigured backup protocols. The system is designed to minimize downtime and ensure that content delivery is uninterrupted, even in the event of dynamic failures within the service network. Module for integrating and managing distributed AI models

[0028] This module is responsible for managing the distributed AI models used throughout the system. Instead of relying on a centralized AI model, this module enables AI workloads to be distributed across different nodes in the service mesh. This decentralized approach ensures low-latency decision-making and increases the scalability of the system. The module also handles model training, updating, and synchronization, ensuring that all nodes in the system use the most up-to-date AI models. It optimizes both the computational efficiency and fault tolerance of the system by distributing the workload across multiple distributed nodes. Monitoring and feedback module

[0029] The monitoring and feedback module tracks the performance of the entire system and provides continuous insights into its operations. It collects feedback from the dynamic content routing and fault tolerance modules, enabling continuous evaluation of the AI system's decision accuracy. This feedback loop is critical for refining AI models and ensuring they evolve to meet changing network demands. The module also allows operators to view detailed reports and analytics that help them understand system performance, identify optimization opportunities, and intervene as needed.

Claims

[1] A system (100) for distributed AI-driven content routing within a cloud-native service network, comprising: a. a traffic data collection module configured to continuously collect and aggregate real-time traffic data from services within the service network, including data related to traffic volume, latency, error rates and service health; b. an AI-based traffic analysis and prediction module that uses machine learning algorithms to process the collected traffic data and predict network conditions, detect anomalies, and predict potential congestion or failure points; c. a dynamic content routing module integrated into the service network, configured to dynamically adjust content routing decisions based on the predictions and insights provided by the AI-based traffic analysis module to optimize resource utilization and minimize latency; d. a fault-tolerance and self-healing module that continuously monitors the state of the network and, in the event of a failure or disruption, triggers automatic recovery actions, such as rerouting traffic, providing additional resources, or activating backup protocols; e. a distributed AI model integration and management module configured to distribute AI processing across multiple nodes in the service mesh, enabling decentralized decision-making and efficient model updating and synchronization; f. a monitoring and feedback module designed to track system performance, collect real-time feedback on routing decisions and troubleshooting actions, and ensure continuous optimization through an iterative feedback loop; g. the system continuously adapts to changing network conditions, thus ensuring optimal content delivery and fault resilience across the entire service network. [2] The system (100) of claim 1, wherein the AI-based traffic analysis and prediction module uses reinforcement learning algorithms to improve the accuracy of routing decisions over time based on historical traffic data and network performance metrics. [3] The system (100) of claim 1, wherein the dynamic content routing module automatically routes content to alternative services or paths within the service network in response to predicted congestion or detected service failures. [4] The system (100) of claim 1, wherein the fault tolerance and self-healing module is further configured to perform automatic service discovery and load balancing to ensure that traffic is rerouted to available services when a service failure occurs. [5] The system (100) of claim 1, wherein the distributed AI model integration and management module further enables decentralization of AI model training so that each node in the service network can contribute to model training and ensure scalability and low-latency decision making. [6] The system (100) of claim 1, wherein the monitoring and feedback module is configured to generate real-time analytical reports on system performance, including metrics related to traffic volume, routing efficiency, and troubleshooting, and to provide insights for system optimization. [7] The system (100) of claim 1, wherein the traffic data collection module further comprises integration with third-party monitoring tools or services to enrich traffic data with additional context, such as user behavior analysis or application-specific performance metrics. [8] The system (100) of claim 1, wherein the AI-based traffic analysis and prediction module includes an error detection component that automatically triggers traffic rerouting when it detects abnormal network conditions, such as excessive latency, error rates, or network congestion.

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