Adaptive Workload Modeling using AI for Performance Testing of Cloud-Based Multitenant Enterprise Applications

Authors

Pradeep Kumar

Abstract

Cloud-based multitenant enterprise applications face growing challenges in optimizing performance, managing resources efficiently, and ensuring scalability due to unpredictable workload fluctuations. Traditional workload management approaches, such as rule-based and threshold-based autoscaling, struggle to accurately forecast and respond to dynamic workload variations, leading to higher latency, inefficient resource utilization, and increased operational costs. To address these challenges, this paper introduces an AI-driven adaptive workload modeling framework that leverages machine learning (ML) for workload forecasting and reinforcement learning (RL) for real-time resource adaptation.
The proposed framework utilizes ML models such as Long Short-Term Memory (LSTM) and XGBoost to analyze historical workload patterns and predict future demand. In parallel, RL-based techniques, including Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), dynamically adjust resource allocation based on system performance in real time. Experimental evaluations conducted in a cloud-based test environment demonstrate that the AI-driven system outperforms traditional autoscaling methods, reducing resource adjustment time by 50%, improving workload prediction accuracy by 30-40%, and lowering cloud computing costs by 35-50%.
Beyond performance gains, the AI-driven approach enhances service reliability, system responsiveness, and workload balancing by proactively preventing resource bottlenecks and overload conditions. However, challenges remain in handling unexpected workload spikes, minimizing computational overhead for AI inference, and adapting models to diverse application environments. Future research should explore collaborative AI-driven workload models for multi-cloud environments, interpretable AI techniques for transparent decision-making, and advanced computing methods for optimizing real-time AI-based workload adjustments.
The findings of this study highlight the potential of AI-powered workload management in transforming cloud performance optimization. By enabling self-adjusting, intelligent cloud systems with minimal human intervention, this approach offers significant advantages for cloud service providers, SaaS companies, and enterprises aiming to enhance operational efficiency and cost-effectiveness.

Keywords

AI-Based Workload Management, Cloud Performance Optimization, Machine Learning for Workload Prediction, Dynamic Resource Allocation, Workload Forecasting, Adaptive Cloud Systems, Cost Reduction in Cloud Computing.

Adaptive Workload Modeling using AI for Performance Testing of Cloud-Based Multitenant Enterprise Applications. Pradeep Kumar. 2024. IJIRCT, Volume 10, Issue 1. Pages 1-17. https://www.ijirct.org/viewPaper.php?paperId=2503109