Cloud-Native Software Engineering Under Extreme Load: Elasticity, Consistency Trade-Offs, and System Integrity
Abstract
Cloud-native software systems are engineered to operate in distributed, elastic environments where compute resources scale dynamically in response to demand. While elasticity enables responsiveness under fluctuating workloads, extreme load conditions expose architectural tensions between availability, consistency, and system integrity. Under sustained high throughput, naive scaling strategies may amplify race conditions, replication lag, cascading failures, or state divergence. This paper develops a comprehensive architectural analysis of cloud-native systems operating under extreme load. It examines elasticity as a first-class design primitive, analyzes auto-scaling failure modes, and evaluates consistency trade-offs through the lens of distributed systems theory. Particular emphasis is placed on maintaining system integrity amid rapid scaling events, partial network failures, and high concurrency saturation. By integrating elasticity engineering, consistency modeling, and resilience design, the study proposes an integrity-centric cloud-native framework capable of sustaining correctness and availability under extreme operational stress.
Keywords
Cloud-Native Architecture; Extreme Load; Elastic Scaling; Consistency Models; CAP Theorem; Distributed Systems; System Integrity; Replication; Resilience Engineering
Citations
IRE Journals:
Caglar Cakar "Cloud-Native Software Engineering Under Extreme Load: Elasticity, Consistency Trade-Offs, and System Integrity" Iconic Research And Engineering Journals Volume 8 Issue 5 2024 Page 1554-1564 https://doi.org/10.64388/IREV8I5-1715573
IEEE:
Caglar Cakar
"Cloud-Native Software Engineering Under Extreme Load: Elasticity, Consistency Trade-Offs, and System Integrity" Iconic Research And Engineering Journals, 8(5) https://doi.org/10.64388/IREV8I5-1715573
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