Resilient Auto-Scaling of Microservice Architectures with Efficient Resource Management
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Abstract (arXiv)
Horizontal Pod Auto-scalers (HPAs) are crucial for managing resource allocation in microservice architectures to handle fluctuating workloads. However, traditional HPAs fail to address resource disruptions caused by faults, cyberattacks, maintenance, and other operational challenges. These disruptions result in resource wastage, service unavailability, and HPA performance degradation. To address these challenges, we extend our prior work on Smart HPA and propose SecureSmart HPA, which offers resilient and resource-efficient auto-scaling for microservice architectures. SecureSmart HPA monitors microservice resource demands, detects disruptions, evaluates resource wastage, and dynamically adjusts scaling decisions to enhance the resilience of auto-scaling operations. Furthermore, SecureSmart HPA enables resource sharing among microservices, optimizing scaling efficiency in resource-constrained environments. Experimental evaluation at varying disruption severities, with 25%, 50%, and 75% resource wastage, demonstrates that SecureSmart HPA performs effectively across different levels of disruptions. It achieves up to a 57.2% reduction in CPU overutilization and a 51.1% increase in resource allocation compared to Smart HPA, highlighting its ability to deliver resilient and efficient auto-scaling operations in volatile and resource-constrained environments.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jun 2025.
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Citing papers per year: 2025: 1; 2026: 5.
6 papers.
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- 2026AgenticVM: Agentic AI for Adaptive Software Vulnerability Managementcited by 2
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- 2026CausalNav: Reliability-Certified Causal World Models for Control under Physical-Parameter Shiftcited by 0
- 2026CausalNav: Reliability-Certified Causal World Models for Control under Physical-Parameter Shift7 Aug 2026
- 2026AgenticVM: Agentic AI for Adaptive Software Vulnerability Management3 May 2026
- 2026Comparative Analysis of Large Language Models in Healthcare11 Apr 2026
- 2026Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning6 Apr 2026
- 2026Australian Bushfire Intelligence with AI-Driven Environmental Analytics3 Jan 2026
- 2025RegimeFolio: A Regime Aware ML System for Sectoral Portfolio Optimization in Dynamic Markets14 Sep 2025