Data Cluster Request Validation With Token-Based Session Security
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Solution Overview
Problem
Existing systems face challenges in efficiently managing and securing data clusters across diverse geographical regions, ensuring data availability, integrity, and reliability while addressing bottlenecks and potential failures, and maintaining secure network traffic.
Innovation Solution
Implementing a system with self-learning adapters that monitor and identify bottlenecks, securely share load between multiple sites, predict failures using machine learning, and include a security module to filter network traffic and reject malicious requests, along with a reporting console for performance history and forecasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a security module is implemented to filter network traffic and reject malicious requests, then data cluster security is improved, but system complexity increases
Solution Approach 1:
The patent introduces a security module as an intermediary component that sits between the network and the data cluster services. This module filters network traffic and rejects malicious requests before they reach the core systems, thereby improving security without requiring modifications to the underlying complex data cluster architecture. The security module acts as a protective barrier that handles security concerns independently.
2Reliability
If self-learning adapters with machine learning are deployed to predict failures and monitor bottlenecks, then system reliability is improved, but device complexity increases
Solution Approach 1:
The patent implements self-learning adapters that use machine learning algorithms to automatically monitor system performance, identify bottlenecks, and predict failures. These adapters continuously learn from system data and improve their predictive capabilities over time without requiring manual intervention or complex external monitoring systems. The self-learning capability enables the system to service its own monitoring and prediction needs independently.
3Reliability
If load is securely shared between multiple sites across diverse geographical regions, then data availability is improved, but network traffic management complexity increases
Solution Approach 1:
The patent divides the data cluster system into multiple geographically distributed sites, each capable of handling portions of the workload. By segmenting the system across multiple locations, the patent improves data availability through redundancy and distributed processing. The load sharing mechanism automatically distributes requests across sites, managing network traffic complexity through structured segmentation rather than centralized control.
Data Source
AI summary
A method for monitoring requests in data clusters includes: obtaining, by a security management module (LMM) of a data cluster, a session registration request from a client; in response to obtaining the session registration request: generating a token using token generation parameters; generating session information and storing the session information in a database; sending session registration acknowledgement to the client, wherein the session registration acknowledgement comprises the token; and validating requests obtained from the client using the token.