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January 24, 2026Naukovij žurnal «Tehnìka ta energetika»Open Access

Performance trade-offs between predictive and reactive autoscaling in stateful microservices

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Authors

KTKhrystyna Terletska

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Overview

Analysis assesses effectiveness of autoscaling strategies in stateful microservices, highlighting implications for cloud stability and resource efficiency.

Key Points

  • The study aims to evaluate the effectiveness of reactive, predictive, and hybrid autoscaling strategies for stateful microservices.
  • Conducted comparative and systems analysis of autoscaling strategies
  • Used modeling to analyze performance and stability metrics
  • Reviewed theoretical frameworks regarding workload nature and latency sensitivity
  • Reactive autoscaling responds quickly but may increase latency and performance fluctuations.
  • Predictive autoscaling can enhance stability and resource efficiency but requires accurate models.
  • Hybrid strategies offer a balance between the benefits of reactive and predictive approaches.

Cite This Study

Khrystyna Terletska (2025) studied this question.

synapsesocial.com/papers/6974616cbb9d90c67120b43ehttps://doi.org/10.31548/machinery/4.2025.21
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Also Consider

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  1. 1OPTIMIZATION OF MICROSERVICE INFRASTRUCTURE FOR REAL-TIME SYSTEMS: BALANCING PERFORMANCE, RESILIENCE, AND COST2025
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  3. 3Self-adaptive, Requirements-driven Autoscaling of Microservices2024 · 18 citations
  4. 4Enhancing Microservices Efficiency: Integrating Adaptive Learning for Automated Scaling in Cloud Environments2024 · 3 citations
  5. 5Adaptive AI Inference Optimization: A Comparative Simulation Study of Static, Reactive, Forecast-Based, and Optimization-Based Autoscaling2026