US-12619410-B2 - Systems and methods for automated mesh service-based deployment intelligence

Abstract

Systems, computer program products, and methods for an automated mesh service-based deployment intelligence system are provided. This system is designed to streamline the deployment of software by integrating a processing device and a non-transitory storage device. The storage device contains instructions that, when executed by the processing device, enable the ingestion of data from various sources such as monitoring systems, databases, and application performance management tools. Once ingested, the data is stored and processed to discern deployment patterns and detect any anomalies. Utilizing a machine learning model, the system anticipates potential deployment issues by analyzing this data. It then orchestrates the deployment of software artifacts accordingly, taking into account the insights gained from the machine learning model. Furthermore, the system is capable of real-time optimization of the deployment process to preemptively resolve any predicted issues, thereby enhancing the efficiency and reliability of software deployment operations.

Inventors

• Veerendra Gupta
• Atul Bhaskarrao Dhomne
• Srinivasa Murthy Penubothu
• Jeff Schommer

Assignees

• BANK OF AMERICA CORPORATION

Dates

Publication Date

20260505

Application Date

20240116

Claims (20)

1. 1 . A system for automated mesh service-based deployment intelligence, the system comprising: a memory containing instructions when executed by the system, causes the system to perform the steps of: ingesting data from multiple data sources including at least one of a monitoring system, a database, and an application performance management tool; storing the ingested data in a structured data storage system; processing the ingested data to identify deployment patterns and anomalies; applying a machine learning model to the processed data to generate a predicted potential deployment issue; orchestrating deployment of software artifacts based on the predicted potential deployment issue; and optimizing a deployment process in real-time to address the predicted potential deployment issue prior to the deployment.
2. 2 . The system of claim 1 , wherein the system is further configured to implement a feedback mechanism to refine the machine learning model based on an outcome of a previous deployment.
3. 3 . The system of claim 1 , wherein the system is further configured to preprocess the ingested data by normalizing and cleaning the ingested data to conform to a selected format for analysis by the machine learning model.
4. 4 . The system of claim 1 , wherein the system is further configured to receive real-time data streams and batch process data inputs from the multiple data sources, enabling synchronous and asynchronous data processing.
5. 5 . The system of claim 1 , wherein the system is further configured to employ a service mesh architecture to determine communication with deployment services, managing deployment tasks across multiple environments.
6. 6 . The system of claim 1 , wherein the system is further configured to utilize a runtime error optimization engine to dynamically adjust deployment strategies based on real-time system states and historical error data.
7. 7 . The system of claim 1 , wherein the system is further configured to automatically generate alerts and reports when the machine learning model predicts a deployment failure.
8. 8 . A computer program product for automated mesh service-based deployment intelligence, the computer program product comprising a non-transitory computer-readable medium comprising code causing an apparatus to: ingest data from multiple data sources including at least one of a monitoring system, a database, and an application performance management tool; store the ingested data in a structured data storage system; process the ingested data to identify deployment patterns and anomalies; apply a machine learning model to the processed data to generate a predicted potential deployment issue; orchestrate deployment of software artifacts based on the predicted potential deployment issue; and optimize a deployment process in real-time to address the predicted potential deployment issue prior to the deployment.
9. 9 . The computer program product of claim 8 , wherein the code further causes the apparatus to: implement a feedback mechanism to refine the machine learning model based on an outcome of a previous deployment.
10. 10 . The computer program product of claim 8 , wherein the code further causes the apparatus to: preprocess the ingested data by normalizing and cleaning the ingested data to conform to a selected format for analysis by the machine learning model.
11. 11 . The computer program product of claim 8 , wherein the code further causes the apparatus to: receive real-time data streams and batch process data inputs from the multiple data sources, enabling synchronous and asynchronous data processing.
12. 12 . The computer program product of claim 8 , wherein the code further causes the apparatus to: employ a service mesh architecture to determine communication with deployment services, managing deployment tasks across multiple environments.
13. 13 . The computer program product of claim 8 , wherein the code further causes the apparatus to: utilize a runtime error optimization engine to dynamically adjust deployment strategies based on real-time system states and historical error data.
14. 14 . The computer program product of claim 8 , wherein the code further causes the apparatus to: automatically generate alerts and reports when the machine learning model predicts a deployment failure.
15. 15 . A method for automated mesh service-based deployment intelligence, the method comprising: ingesting data from multiple data sources including at least one of a monitoring system, a database, and an application performance management tool; storing the ingested data in a structured data storage system; processing the ingested data to identify deployment patterns and anomalies; applying a machine learning model to the processed data to generate a predicted potential deployment issue; orchestrating deployment of software artifacts based on the predicted potential deployment issue; and optimizing a deployment process in real-time to address the predicted potential deployment issue prior to the deployment.
16. 16 . The method of claim 15 , wherein the method further comprises: implementing a feedback mechanism to refine the machine learning model based on an outcome of a previous deployment.
17. 17 . The method of claim 15 , wherein the method further comprises: preprocessing the ingested data by normalizing and cleaning the ingested data to conform to a selected format for analysis by the machine learning model.
18. 18 . The method of claim 15 , wherein the method further comprises: receiving real-time data streams and batching process data inputs from the multiple data sources, enabling synchronous and asynchronous data processing.
19. 19 . The method of claim 15 , wherein the method further comprises: utilizing a runtime error optimization engine to dynamically adjust deployment strategies based on real-time system states and historical error data.
20. 20 . The method of claim 15 , wherein the method further comprises: automatically generating alerts and reports when the machine learning model predicts a deployment failure.

Description

TECHNOLOGICAL FIELD Example embodiments of the present disclosure relate to systems and methods for automated mesh service-based deployment intelligence. BACKGROUND The field of software development, particularly within the operations aspect of the Software Development Life Cycle (SDLC), is encountering increasing challenges in managing the deployment of software artifacts, especially during periods of high deployment volume. These challenges include issues with software deployment dependencies, the need to coordinate with multiple teams, and the necessity to multitask using a variety of tools. Moreover, the deployment process is further complicated by time-dependent approval processes, leading to inefficiencies, errors, and delays that adversely affect software delivery. Despite advancements in automation and orchestration tools, there remains a gap in addressing the specific needs of operations teams. These unmet needs include the complexities in managing software deployment dependencies, early error detection, and the ability to deploy software concurrently across multiple environments. Current tools in the market, particularly orchestration tools, are limited in their capabilities. They offer low configurational competencies and primitive scheduling options, lacking the necessary support to configure software components' dependencies for deployment. There is also an absence of features to manage deployments on demand and in parallel. Furthermore, these tools do not provide the ability to configure approval workflows at different stages of the deployment process. They are often inflexible in terms of scheduling recurring deployments. This results in inefficiencies, errors, and delays in the deployment process. Additionally, there is a notable lack of mechanisms for self-detecting runtime errors early in the deployment process. These limitations highlight the need for an innovative solution that addresses these specific challenges in software deployment within the SDLC, improving efficiency, accuracy, and the overall speed of software delivery. As such, Applicant has identified a number of deficiencies and problems associated with automated mesh service-based deployment intelligence. Through applied effort, ingenuity, and innovation, many of these identified problems have been solved by developing solutions that are included in embodiments of the present disclosure, many examples of which are described in detail herein. BRIEF SUMMARY Systems, methods, and computer program products are provided for automated mesh service-based deployment intelligence. The proposed solution, titled is a groundbreaking system that integrates mesh service architecture capabilities with advanced artificial intelligence (AI). The core of this invention lies in its unique combination of a self-serve distributed engine processor and unique AI application. This approach is designed to autonomously manage deployment dependencies, control dark and live servers, enable self-service scheduling, facilitate parallel processing, and implement self-retry mechanisms. Additionally, it provides the capability for distributed locking and the automatic detection of runtime errors or anomalies during a deployment stage. This system can be accessed and controlled through user-friendly interfaces, either via a direct user interface (UI) or application programming interface (API), making it highly accessible for end users. The system is engineered to seamlessly integrate with existing orchestration and configuration management tools. This ensures that the adoption of the new system does not disrupt existing workflows and processes within organizations. The architecture underpinning this solution is a testament to its design philosophy, emphasizing scalability, flexibility, and maintainability. These characteristics are crucial for adapting to the evolving needs and complexities of modern software deployment environments. From a technological standpoint, the solution utilizes a combination of cutting-edge technologies including AI and machine learning, deep learning, mesh service architecture, microservices, Shedlock for distributed locking, along with the use of established orchestration and configuration management tools like Ansible. This amalgamation of technologies ensures that the solution is not only robust and efficient but also at the forefront of innovation in software deployment and management. The system represents a significant step forward in addressing the complexities and challenges of modern software deployment in the SDLC, promising to enhance efficiency, reduce errors, and accelerate the overall software delivery process. Embodiments of the invention relate to systems, methods, and computer program products for automated mesh service-based deployment intelligence, the invention including: ingest data from multiple data sources including at least one of a monitoring system, a database, and an application performance management too