AI-Driven Auto-Scaling for Cloud Applications

Evelyn Carter-Brooks

doi:10.36676/jrps.v16.i4.328

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JOURNAL ARTICLE

AI-Driven Auto-Scaling for Cloud Applications

Evelyn Carter-Brooks

Year: 2025 Journal: International Journal for Research Publication and Seminars Vol: 16 (4)Pages: 95-100

DOI: 10.36676/jrps.v16.i4.328

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Abstract

Traditional rule-based auto-scaling often struggles with complex cloud workloads. This study develops a reinforcement-learning-based auto-scaling agent that dynamically adjusts compute resources based on predicted demand. Experiments show improved cost efficiency and reduced latency under unpredictable load patterns. The results highlight the potential of AI-based scaling for high-traffic cloud systems.

Keywords:

Cloud computing Latency (audio) Scaling Scheduling (production processes) Key (lock)

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Topics

Cloud Computing and Resource Management

Physical Sciences → Computer Science → Information Systems

Software System Performance and Reliability

Physical Sciences → Computer Science → Computer Networks and Communications

IoT and Edge/Fog Computing

Physical Sciences → Computer Science → Computer Networks and Communications

AI-Driven Auto-Scaling for Cloud Applications

Abstract

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