AI-Driven Performance Monitoring and Optimization in SAP BW/4HANA And AWS Hybrid Architectures
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I3P114Keywords:
SAP BW/4HANA, AWS, Hybrid Architecture, Artificial Intelligence, Machine Learning, Performance Monitoring, Cloud Optimization, Predictive Analytics, Resource Management, Enterprise Systems, SAP BW, performance optimizationAbstract
The accelerated development of enterprise data ecosystems has led to the need to combine the sophisticated analytics systems with scalable cloud platforms. Next-generation data warehousing solution SAP BW/4HANA provides more in-memory processing and performance, whereas Amazon Web Services (AWS) offers distributed, elastic, cloud computing abilities. Nevertheless, hybrid implementations that use on-premise SAP systems with AWS environments are subject to introducing complexities in terms of performance monitoring, workload optimization and resource allocation. The conventional monitoring devices that are mostly rule-based and reactive are not adequate to deal with the dynamic and heterogeneous character of such systems.The current paper features an AI-based performance monitoring and optimization framework in an SAP BW/4HANA environment deployed on the AWS hybrid environments. The solution proposed depends on machine learning algorithms (supervised regression models, data clustering, and reinforcement learning approaches) to make predictive analytics, anomaly detection, and automated optimization possible. The system will combine SAP application server, SAP HANA database, and Amazon EC2, S3, and CloudWatch telemetry data.Multi-layered architecture is presented, which comprises of data acquisition, feature engineering, model training and decision orchestration layers. The KPIs of seek response time, CPU response and memory response and the I/O throughput are constantly monitored and analyzed. The system bottlenecks are predicted using the predictive models and resource provisioning and query execution strategy are dynamically changed as part of the optimization policy.The study measures the performance of the suggested framework through simulated workload of enterprises. Findings show that there have been great advancements in the performance of systems which are lower latency, improved throughput, and better resource utilization. Proactive system control with the combination of AI-based insights helps to reduce downtimes and service cost.The research paper makes a contribution to the emerging area of intelligent enterprise systems through the optimization of the hybrid architecture on a scale, adjustable, and automated. Those results highlight a critical role of integrating AI methods and business-scale data systems to attain effective and stable system performance.
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