Adaptive Workload Forecasting and Intelligent Resource Scheduling for SAP-Integrated Cloud Enterprise Platforms

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Published: 2023-12-19
DOI: https://doi.org/10.15662/IJRPETM.2023.0606029
Keywords:
Adaptive Workload Forecasting, Intelligent Resource Scheduling, SAP Cloud Platforms, Machine Learning, Predictive Resource Allocation, Cloud Resource Optimization

Main Article Content

Dr. S. Jagadeesh Soundappan
Independent Researcher, USA

Abstract

SAP-integrated cloud enterprise platforms support critical business operations, including financial processing, supply chain management, customer relationship management, analytics, and enterprise resource planning. As enterprise workloads become increasingly dynamic, conventional resource allocation approaches often struggle to maintain optimal performance, scalability, and cost efficiency. This paper presents an adaptive workload forecasting and intelligent resource scheduling framework designed to optimize cloud infrastructure resources for SAP-integrated enterprise applications. The proposed framework combines historical workload analysis, time-series forecasting, machine learning, and intelligent scheduling algorithms to predict future resource requirements and allocate computing capacity proactively. Workload indicators such as CPU utilization, memory consumption, transaction volume, database response time, network traffic, and batch-processing duration are incorporated into the forecasting process. Predictive outputs guide resource scaling, workload placement, and scheduling decisions across distributed cloud environments. The framework also considers service-level objectives, workload priorities, infrastructure costs, and application dependencies to balance performance and resource utilization. Continuous feedback enables the scheduling mechanism to adapt to changing demand patterns and operational conditions. The proposed approach establishes a methodological foundation for evaluating forecasting accuracy, scheduling efficiency, response latency, resource utilization, and infrastructure expenditure. Experimental validation using representative SAP workload traces and controlled cloud simulations is required to establish its effectiveness. The framework aims to support reliable, scalable, and cost-aware resource management for modern SAP-integrated cloud enterprise platforms.
 
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Article Details

Issue

Vol. 6 No. 6 (2023): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Adaptive Workload Forecasting and Intelligent Resource Scheduling for SAP-Integrated Cloud Enterprise Platforms. (2023). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(6), 9877-9885. https://doi.org/10.15662/IJRPETM.2023.0606029

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