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June 20, 2026Open Access

Adaptive Autoscaling Using Workload Forecasting

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Authors

VRVishnu RamineniSASeema G. Aarella

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Overview

Randomized trial demonstrates improved resource efficiency in cloud environments, indicating the benefits of proactive management.

Key Points

  • To develop a predictive and adaptive resource management framework for cloud computing that enhances service reliability and efficiency.
  • Combines workload forecasting with policy-driven orchestration for resource scaling.
  • Evaluates the framework using multi-tenant cloud workload traces against conventional autoscaling techniques.
  • Analyzes the performance metrics such as resource utilization and application responsiveness.
  • Demonstrated improvements in resource utilization and provisioning efficiency compared to static autoscaling techniques.
  • Maintained high service availability during infrastructure disruptions.
  • Showed strong scalability across distributed environments.

Cite This Study

Ramineni et al. (2021) studied this question.

synapsesocial.com/papers/6a362f11db0793dc1a536ac6https://doi.org/10.5281/zenodo.20740547
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