An empirical approach to cloud workload health scoring framework: Enhancing performance, cost optimization, and security

Abstract

Cloud workloads require continuous monitoring and optimization for optimal performance, cost efficiency, and security. Existing Cloud Workload Health Scoring methods often have limitations like focus on specific aspects or lack a granular scoring mechanism. This paper proposes a novel, empirical approach that addresses these limitations. Our framework integrates recommendations from diverse sources and KPIs, assigning configurable weights for prioritization. A parameterized hyperbolic tangent function transforms scores into a clear health indication (