Description:FIELD OF THE INVENTION
The present invention relates to the field of internet traffic routing and load balancing. More particularly, the invention pertains to a feature Load balancer system and method to redirect internet traffic for redirecting client internet traffic to target systems based on specific requested features using dynamic feature metadata, intelligent matching mechanisms, and real-time internet traffic redirection.

BACKGROUND
With the advent of the internet, web internet traffic, flow of data transmitted between a user's device and a website or online service, has become a critical component of online systems. Traditionally, Load Balancers have played a crucial role in directing this internet traffic, employing straightforward parameters such as server availability, IP addresses, or URLs to manage requests efficiently.
However, as systems have evolved into more complex architectures like microservices and advanced technologies such as Large Language Models (LLMs), traditional load balancers often face incompetency to adapt. Modern systems require dynamic and intelligent internet traffic management that can optimize resource allocation based on specific workloads. While innovative approaches such as dynamic profiling and task distribution have emerged, the current solutions are still not entirely user-friendly or efficient. The process often remains fragmented, lacks cohesion, and is unable to fully align with the real-time, feature-specific demands of next-generation distributed systems.
PRIOR ART
Prior attempts to address these issues are noteworthy but incomplete. For instance, US6728961B1 describes a distributed computing environment where peer machines dynamically balance tasks by profiling incoming methods and bidding on their execution. While effective for optimizing task execution across a network of interpreters, this approach does not consider feature-specific traffic redirection or metadata-driven routing as required in modern distributed systems. Similarly, US8171385B1 introduces a load balancing system for asymmetric web farms, improving efficiency by eliminating data switching between user and kernel spaces and enabling internal buffer-based load distribution. However, it is designed for static web farm environments and lacks the ability to handle dynamic feature-specific requests or adapt to the demands of microservices and LLMs. The present invention addresses these limitations thereby providing a novel system and method for dynamically interpreting feature-specific requests and redirecting internet traffic.

OBJECTS OF THE INVENTION:
The primary object of the invention is to provide a feature Load balancer system and method to redirect internet traffic.
Another object of the invention is to enable dynamic routing of internet traffic based on client-specified features, rather than static parameters like IP or port, ensuring optimal resource allocation and improved system performance.
Yet another object of the invention is to provide a centralized Feature Registry that simplifies feature registration and routing by utilizing detailed metadata.
Yet another object of the invention is to provide a Feature Matching Engine that interprets client requests and directs them to the most appropriate endpoint.
Yet another object of the invention is to ensure seamless operation across diverse environments, including development, staging, and production, and to support targeted use cases such as API gateways, microservices orchestration, and Large Language Model (LLM) management.

SUMMARY:
Before the present invention is described, it is to be understood that present invention is not limited to particular methodologies and materials described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only, and is not intended to limit the scope of the present invention.
The present invention discloses a Feature Load Balancer (FLB) designed to transform traditional internet traffic routing systems by enabling feature-specific redirection in distributed computing environments. Unlike conventional load balancers that rely on static parameters like IP addresses or server availability, this system dynamically routes client requests based on the specific features or functionalities being requested. At the core of the system is a Feature Registry, which acts as a centralized repository, storing detailed metadata for features, including unique identifiers, input/output schemas, descriptions, and supported environments such as development, staging, or production. The client request handler processes incoming requests by parsing their headers to extract feature-specific details. This information is forwarded to the feature matching engine, which queries the registry and identifies the most suitable endpoint for the request, considering metadata and real-time factors like server load and availability. Once an endpoint is identified, the routing manager redirects the internet traffic securely and efficiently, maintaining communication in a stateful or stateless manner as required. The system includes a monitoring and logging module that continuously tracks performance metrics, request patterns, and errors, facilitating diagnostics and optimization. An admin dashboard provides administrators with an intuitive interface to register features, update metadata, and monitor the system. This innovation is particularly useful in applications like API gateways, microservices orchestration, and Large Language Model (LLM) management, ensuring efficient resource utilization, scalability, and seamless operation in complex environments. By addressing the limitations of traditional load balancers, the FLB offers a sophisticated solution for modern, feature-driven computing systems.

BRIEF DESCRIPTION OF DRAWINGS:
A complete understanding of the present invention may be made by reference to the following detailed description which is to be taken in conjugation with the accompanying drawing. The accompanying drawing, which is incorporated into and constitutes a part of the specification, illustrates one or more embodiments of the present invention and, together with the detailed description, it serves to explain the principles and implementations of the invention.
Fig. 1. illustrates an overview of Feature Load Balancer Process
Fig. 2. Illustrates a Feature Matching and Routing Workflow
Fig. 3. Illustrates an administrative management and optimization workflow.

DETAILED DESCRIPTION OF INVENTION:
Before the present invention is described, it is to be understood that this invention is not limited to methodologies described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention. Throughout this specification, the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the invention to achieve one or more of the desired objects or results. Various embodiments of the present invention are described below. It is, however, noted that the present invention is not limited to these embodiments, but rather the intention is that modifications that are apparent are also included.
To understand the invention clearly, the various components of the system are referred as below:
No. Name
100 Load balancer system
110 Admin Dashboard
120 Feature Registry
130 Client Request Handler
140 Feature Matching Engine
150 Routing manager
160 Monitoring logging and module

The present invention discloses a feature load balancer system (100) comprises of a dashboard (110), a feature registry (120), a client request handler (130), a feature matching engine (140), a routing manager (150) and a monitoring and logging module (160); and method to redirect internet traffic for redirecting client internet traffic to target systems based on specific requested features using dynamic feature metadata, intelligent matching mechanisms, and real-time internet traffic redirection specifically designed for distributed computing environments. Unlike traditional load balancers that route traffic based on static parameters like server IPs or availability, this system (100) dynamically directs traffic based on specific features requested by the client, improving efficiency and resource utilization. The several components of the present system (100) work together seamlessly to enable feature-specific routing, real-time monitoring, and system (100) optimization.
According to a preferred embodiment, the feature registry (120) acts as a centralized repository for all registered features. The feature registry (120) stores critical metadata for each feature, including a unique identifier, detailed descriptions, input/output schemas, and the environments in which the features are supported (e.g., development, staging, or production); wherein the metadata provides a clear understanding of what each feature entails and enables the system (100) to make intelligent routing decisions. The feature registry (120) essentially forms the backbone of the system (100) by maintaining an organized record of all available features and their specifications.
According to another embodiment, the client request handler (130) processes incoming client requests; such that when a client sends a request to the system (100), the client request handler (130) parses the request headers to extract feature-specific information, wherein the headers contain details about the desired functionality that the client is requesting. Instead of treating the request as a generic one, the client request handler (130) ensures that the system (100) understands the nature of the feature being sought, which is essential for accurate routing.
According to yet another embodiment, the feature-specific information extracted by the client request handler (130) is passed to the feature matching engine (140); wherein the system (100) performs its intelligent decision-making process. The feature matching engine (140) queries the feature registry (120) using the extracted information to identify the most suitable endpoint that can fulfil the request. During this process, the feature matching engine (140) takes into account not only the metadata of the feature, but also the current system (100) state, including the parameters such as server availability, real-time load conditions, and resource health are evaluated to ensure the traffic is routed to the most optimal endpoint. This capability sets the system (100) apart, as it intelligently balances the load while ensuring that feature-specific needs are met.
According to yet another embodiment, once the appropriate endpoint is identified, the routing manager (150) facilitates the actual redirection of the client’s traffic. The routing manager (150) ensures that the request is securely and efficiently directed to the matched endpoint while maintaining seamless communication between the client and the endpoint. Depending on the nature of the request or feature, the system (100) can maintain communication in a stateful or stateless manner. For example, if the feature requires maintaining session continuity, the system (100) operates state fully; otherwise, it can opt for stateless communication to optimize resource use.
According to a next embodiment, the invention incorporates a monitoring and logging module (160) to ensure reliability and system (100) optimization. This component continuously tracks key performance metrics such as request patterns, endpoint response times, system errors, and overall performance. For instance, it records how frequently specific features are requested, how well endpoints are performing under load, and whether any errors occur during request processing. This real-time data is logged and stored for analytics and troubleshooting purposes, providing system (100) administrators with valuable insights into system (100) health, bottlenecks, and areas requiring improvement.
In yet another embodiment, the admin dashboard (110) provides the administrators with an intuitive user interface for managing the system (100); wherein the dashboard (110) refers to but is not limited to a screen, a user interface, a computer, a desktop, a PC or a mobile device. The administrators can register new features, update feature metadata, monitor system (100) activity, and troubleshoot performance issues using the said dashboard (110). For example, if a new feature is introduced into the system (100), administrators can easily provide the necessary metadata, including input/output details and supported environments, ensuring that the system (100) recognizes the feature and can route relevant requests to it.
In another preferred embodiment of the invention, a method to redirect internet traffic using a feature load balancer system (100) is provided. The method comprises the following steps:
1. Receiving the Client Request:
The process begins with a client device transmitting a request to the system (100) using an input device; wherein the request includes specific information about the feature or functionality desired by the client.
2. Processing by client request handler (130):
The client request handler (130) receives the incoming request and parses its contents; thereby extracting feature-specific information embedded in the request headers, which serves as input for the subsequent routing process.
3. Forwarding to feature matching engine (140):
The extracted feature information is forwarded to the feature matching engine (140); which evaluates the feature requirements by referencing a centralized metadata repository.
4. Querying the feature registry (120):
The feature matching engine (140) queries the feature registry (120), a centralized repository that stores metadata for all registered features where the metadata includes unique feature identifiers, detailed descriptions, input/output schemas, and environment specifications where the features are supported.
5. Identifying Optimal Endpoint:
Based on the metadata retrieved from the feature registry (120) and considering real-time factors such as server availability, system (100) load, and performance metrics, the feature matching engine (140) identifies the most suitable endpoint to process the client request.
6. Redirecting by the Routing Manager (150):
Upon identification of the optimal endpoint, the Routing Manager facilitates the redirection of the client request to the selected endpoint; in a manner that the redirection is executed securely and efficiently, ensuring seamless communication between the client and the endpoint.
7. Tracking by monitoring and logging module (160):
As the request is processed, the monitoring and logging module (160) captures system (100) performance metrics, including request patterns, endpoint response times, and error logs; such that the said metrics are stored for real-time troubleshooting and system (100) optimization.
8. Administrative Control via admin dashboard (110):
The admin dashboard (110) enables system (100) administrators to manage and oversee the feature load balancer (100) by providing functionalities to register new features, update feature metadata, and monitor system performance.
9. Updating feature registry (120) through the Feedback Loop:
Administrators can update the feature registry (120) through the admin dashboard (110), ensuring that the metadata reflects the latest feature specifications and aligns with the system’s (100) operational needs.
The present invention provides certain advantages as the invention revolutionizes traffic routing in distributed environments by introducing feature-based routing instead of relying on static server configurations. Through components like the feature registry (120), client request handler (130), feature matching engine (140), routing manager (150), and monitoring and logging module (160), the system (100) ensures intelligent decision-making, optimal resource allocation, seamless traffic redirection, and continuous system monitoring. This addresses the limitations of traditional load balancers and provides a robust solution for modern applications, such as microservices orchestration, API management, and Large Language Model (LLM) handling.
While considerable emphasis has been placed herein on the specific elements of the preferred embodiment, it will be appreciated that many alterations can be made and that many modifications can be made in preferred embodiment without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the invention and not as a limitation. , Claims:CLAIMS:
We claim,
1. A feature load balancer system (100) and method to redirect internet traffic, comprising a dashboard (110), feature registry (120), a client request handler (130), a feature matching engine (140), a routing manager (150) and a monitoring and logging module (160);
characterized in that:
the admin dashboard (110) provides administrators with an intuitive user interface for managing the system (100); wherein the administrators register new features, update feature metadata, monitor system (100) activity, and troubleshoot performance issues;
the feature registry (120) is a central repository; that stores metadata for each feature in the system (100) including unique feature ID, detailed descriptions of the feature's purpose, input/output schemas that specify how data is processed, and supported environments to ensure the feature is executed in the correct context;
the client request handler (130); which receives incoming requests and extracts the necessary information from headers or payloads, identifying the specific feature the client is requesting thereby ensuring that the system (100) understands the feature requirement before further processing;
the feature matching engine (140); which finds the most appropriate system endpoint that processes the requested feature by considering the metadata in the Feature Registry and runtime conditions such as system load and resource availability, to ensure that internet traffic is routed to the endpoint best equipped to handle the request;
the routing manager (150); which takes the output from the feature matching engine (140) and handles the actual redirection of client internet traffic to the matched endpoint, ensuring that the communication is secure, efficient, and appropriate for the system's architecture, handling both stateful and stateless interactions;
the monitoring and logging module (160) continuously tracks key performance metrics such as request patterns, endpoint response times, system errors, and overall performance; to ensure reliability and system (100) optimization.
2. The system as claimed in claim 1, wherein the feature metadata includes descriptions, input/output schemas, and supported environments whereby the metadata associated with each feature provides a comprehensive description of the feature's functionality, specifying input handling, expected outputs, and the various environments in which the feature can operate; ensuring that the system (100) can effectively manage and route internet traffic to the correct endpoint, based on the context in which the feature is being requested.

3. The system as claimed in claim 1, wherein the client request specifies the desired feature using the header or payload including a specification of the desired feature, communicated through request headers or payload data; whereby embedding the feature ID or related information in the request, the client ensures that the system (100) can accurately interpret and route the request to the relevant feature endpoint, facilitating precise and dynamic load balancing.

4. The system as claimed in claim 1, wherein the monitoring and logging module (160) tracks the system (100) performance and errors. using the metrics such as request frequency, endpoint performance (response time, uptime), and errors that may occur during the request processing; thereby allowing real-time troubleshooting and analysis, providing valuable insights to optimize system (100) performance and ensure continuous reliability.

5. The system as claimed in claim 1, wherein the system (100) is adaptable for managing feature-based internet traffic redirection in environments, where complex, specific tasks need to be routed to specialized endpoints, ensuring efficient and optimized use of resources across different architecture is applicable to APIs, microservices, and LLMs for feature-based internet traffic redirection, broadens the system's applicability to diverse technologies, including APIs, microservices, and Large Language Models .

6. The method as claimed in claim 1; wherein the method comprises the steps of;
a. receiving the client request; where a client device transmits a request to the system (100) using an input device; the said request including specific information about the feature or functionality desired by the client;
b. processing by client request handler (130); where the client request handler (130) receives the incoming request and parses its contents; thereby extracting feature-specific information embedded in the request headers, which serves as input for the subsequent routing process;
c. forwarding to feature matching engine (140); where the extracted feature information is forwarded to the feature matching engine (140); which evaluates the feature requirements by referencing a centralized metadata repository;
d. querying the feature registry by the feature matching engine (140); where the feature registry, a centralized repository that stores metadata for all registered features where the metadata includes unique feature identifiers, detailed descriptions, input/output schemas, and environment specifications where the features are supported;
e. identifying optimal endpoint; where the feature matching engine (140) identifies the most suitable endpoint to process the client request based on the metadata retrieved from the feature registry and considering real-time factors such as server availability, system load, and performance metrics;
f. redirecting by the routing manager (150); whereupon identification of the optimal endpoint, the routing manager (150) facilitates the redirection of the client request to the selected endpoint; executed securely and efficiently, ensuring seamless communication between the client and the endpoint;
g. tracking by monitoring and logging module (160); As the request is processed, the monitoring and logging module (160) captures system (100) performance metrics, including request patterns, endpoint response times, and error logs; such that the said metrics are stored for real-time troubleshooting and system (100) optimization.
h. administrative controlling via admin dashboard (110); that enables system (100) administrators to manage and oversee the feature load balancer by providing functionalities to register new features, update feature metadata, and monitor system (100) performance;
i. updating feature registry through the feedback loop; where administrators can update the feature registry through the admin dashboard (110), ensuring that the metadata reflects the latest feature specifications and aligns with the system’s (100) operational needs.

Dated this 20th day of December, 2024.