Adaptive AI-Driven Integration Pipelines for Efficient Data and Process Orchestration in Cloud-Native Environments

Tejaswi Bharadwaj Katta

doi:10.15662/IJRAI.2023.0601010

Authors

Tejaswi Bharadwaj Katta Independent Researcher, Dallas, Texas, USA Author

DOI:

https://doi.org/10.15662/IJRAI.2023.0601010

Keywords:

AI-driven pipelines, data orchestration, cloud-native environments, machine learning, process automation, scalability, fault tolerance

Abstract

The increasing complexity of cloud-native systems has created a need to have an effective data and process orchestration solution. The study introduces a flexible AI-based integration pipeline that enhances workflow automation in the cloud-native system and improves orchestration of cloud architecture operations. The system is a combination of machine learning algorithms to detect anomalies and predictive analytics and intelligent automation to make real-time decisions. The pipeline has the architecture based on four layers Data Collection and Ingestion, Data Processing and Transformation, AI-based Decision Engine, and Process Orchestration and Workflow Management. The AI-based solution can scale the resources dynamically, detect anomalies dynamically, and optimize data streams according to the past performance and real-time data streams.

We tested the pipeline on a synthetic dataset which depicted real world data streams between various cloud based services. The findings showed that the AI-based pipeline performed much better in terms of operational efficiency compared to the conventional integration systems in speed of data processing, the use of resources, the level of anomaly detection, efficiency of workflow execution, and fault tolerance. In particular, the AI-powered pipeline made a 50 percent advance in the amount of data being processed, a 30 percent decrease in the number of resources used, and a 20 percent expedition in the duration of workflows. Moreover, it was found to have 92 percent precision and 89 percent recall in anomaly detection, which is very high compared to the traditional system.

This article identifies the promise of AI-assisted solutions to streamline data and process coordination in cloud-native systems to provide a flexible, resilient, and scalable solution to contemporary data integration issues. Future plans will be to improve predictive features of the system, add edge computing to serve latency-sensitive applications, and investigate real-world case studies to further support its application in other industries.

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