Distributed data tracing

Patent metadata

Field	Value
Publication number	US-12393501-B2
Application number	US-202418413956-A
Country	US
Kind code	B2
Filing date	Jan 16, 2024
Priority date	Jan 16, 2024
Publication date	Aug 19, 2025
Grant date	Aug 19, 2025

Abstract

Innovative distributed tracing system(s) and process(es) integrate automated code instrumentation, AI-driven quantum computing, particularly Photonic Quantum Computing, and real-time analysis algorithms. This system autonomously identifies and monitors network components such as gateways, service meshes, message queues, databases, and proxy servers, seamlessly capturing trace data across various communication protocols. It features dynamic scaling capabilities to handle fluctuating network loads and employs automatic tagging for precise identification of each network element and transaction. Utilizing advanced machine learning algorithms, the system efficiently analyzes trace data, identifying patterns, dependencies, and anomalies. It is equipped to intervene in operations by halting transactions under specific conditions. The incorporation of Photonic Quantum Computing allows for unparalleled anomaly detection and data analysis speed and accuracy. This system enhances trace data analysis, improves anomaly detection, and optimizes overall system performance, offering a robust, secure, and efficient approach for managing complex distributed systems.

First claim

The invention claimed is:

1. A system for distributed data tracing in a computing environment, the system comprising:
one or more hardware processors configured to execute:
an intelligent tracing engine, implemented on the one or more hardware processors, configured to autonomously identify and monitor network components including gateways, service meshes, message queues, databases, and proxy servers, and further configured to aggregate trace data from the network components to form a comprehensive transaction path, including temporal and sequential data representing a lifecycle of transactions across the computing environment;
a data capturing mechanism, implemented on the one or more hardware processors, configured to intercept and log trace data from various communication protocols in a protocol-agnostic manner;
a dynamic scaling mechanism, implemented on the one or more hardware processors, for adjusting monitoring and storage capacities in response to fluctuating network loads;
an automated tagging system, implemented on the one or more hardware processors, employing a cryptographic hashing algorithm to generate unique and consistent identifiers for each of said transactions;
a machine learning module, implemented on the one or more hardware processors, integrated with said intelligent tracing engine, configured to analyze the captured trace data to identify patterns and anomalies within the trace data using a clustering algorithm to group similar trace data;
a quantum computing environment comprising a Photonic Quantum Computing system including Quantum Computing hardware, operatively coupled to the machine learning module, configured to enhance anomaly detection and data analysis utilizing quantum algorithms and entanglement-based analysis for correlation of said trace data across the network components that are disparate, including a quantum error correction protocol to ensure integrity of said anomaly detection;
a distributed database configured to store said trace data, operatively coupled to the quantum computing environment;
a real-time analysis module, implemented on the one or more hardware processors, configured to receive said trace data from the distributed database and employ real-time algorithms to process said trace data, detect patterns, identify anomalies, and provide insights instantaneously using a time-series forecasting algorithm to predict future system behavior based on historical trace data;
an anomaly detection algorithm process, implemented on the one or more hardware processors, configured to receive data from said real-time analysis module and perform deep analysis on flagged transactions to confirm suspicions of anomalies within said transactions using a statistical thresholding method to determine the suspicion level of anomalies;
an alerting system, implemented on the one or more hardware processors, configured to generate and communicate alerts based on outputs from the anomaly detection algorithm process and further configured to prioritize alerts based on a severity assessment of the detected anomalies; and
a reporting module, implemented on the one or more hardware processors, configured to generate reports for system administrators based on outputs from the anomaly detection algorithm process, wherein said reports include actionable insights into system performance, detected anomalies, quantum computing outcomes, and optimization recommendations for system performance enhancement.

2. A system for distributed data tracing in a computing environment, the system comprising:
one or more hardware processors configured to execute:
an intelligent tracing engine, implemented on the one or more hardware processors, configured to autonomously identify and monitor network components including gateways, service meshes, message queues, databases, and proxy servers;
a data capturing mechanism, implemented on the one or more hardware processors, configured to intercept and log trace data from various communication protocols in a protocol-agnostic manner;
a dynamic scaling mechanism, implemented on the one or more hardware processors, for adjusting monitoring and storage capacities in response to fluctuating network loads;
an automated tagging system, implemented on the one or more hardware processors, for assigning unique and consistent identifiers to each network element and transaction;
a machine learning module, implemented on the one or more hardware processors, integrated with said intelligent tracing engine, configured to analyze the captured trace data to identify patterns and anomalies within the trace data;
a quantum computing environment comprising a Photonic Quantum Computing system including Quantum Computing hardware, operatively coupled to the machine learning module, configured to enhance anomaly detection and data analysis utilizing quantum algorithms;
a distributed database configured to store said trace data, operatively coupled to the quantum computing environment;
a real-time analysis module, implemented on the one or more hardware processors, configured to receive said trace data from the distributed database and employ real-time algorithms to process said trace data, detect patterns, identify anomalies, and provide insights instantaneously;
an anomaly detection algorithm process, implemented on the one or more hardware processors, configured to receive data from said real-time analysis module and perform deep analysis on flagged transactions to confirm suspicions of anomalies within said transactions;
an alerting system, implemented on the one or more hardware processors, configured to generate and communicate alerts based on outputs from the anomaly detection algorithm process; and
a reporting module, implemented on the one or more hardware processors, configured to generate reports for system administrators based on outputs from the anomaly detection algorithm process, wherein said reports include actionable insights into system performance, detected anomalies, and quantum computing outcomes.

3. The system of claim 2 , wherein said intelligent tracing engine utilizes automated code instrumentation to collect said trace data, enabling seamless tracing across the computing environment.

4. The system of claim 3 , wherein said quantum computing environment utilizes quantum key distribution protocols to establish secure and tamper-resistant communication channels between distributed components of the tracing system.

5. The system of claim 4 , wherein the intelligent tracing engine is further configured to aggregate said trace data from multiple network components to form a comprehensive transaction path.

6. The system of claim 5 , wherein the aggregated transaction path includes temporal and sequential data representing a lifecycle of said transactions across the computing environment.

7. The system of claim 6 , wherein the automated tagging system employs a cryptographic hashing algorithm to generate the unique and consistent identifiers for each of said transactions.

8. The system of claim 7 , wherein the machine learning module includes a clustering algorithm to group similar trace data and facilitate identification of pattern-based anomalies.

9. The system of claim 8 , wherein the quantum computing environment performs entanglement-based analysis for correlation of said trace data across the network components that are disparate.

10. The system of claim 9 , wherein the anomaly detection algorithm process includes a statistical thresholding process to determine the suspicion level of anomalies.

11. The system of claim 10 , wherein the alerting system is further configured to prioritize alerts based on a severity assessment of the d

Assignees

• Bank Of America

Inventors

• Adivi Suryanarayana
• Taneja Pushkar
• Kutthumolu Durga Prasad
• Singh Shailendra
• Sai Govinda Ramam Gonna Satya

Classifications

• H04L9/3236 Primary

  using cryptographic hash functions · CPC title

• G06F11/3409

  for performance assessment · CPC title

• G06F11/3006

  where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems (multiprogramming arrangements G06F9/46; allocation of resources G06F9/50) · CPC title

• G06F11/3065 Primary

  Monitoring arrangements determined by the means or processing involved in reporting the monitored data (error or fault reporting or logging G06F11/0766) · CPC title