Author(s): Sridhar Nomula

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 ("Red," "Amber," "Green") offering intuitive understanding of focus areas and primary actions needed to improve workload health. This gives intuitive understanding on the focus areas and prime actions to bring it back to track. Additionally, hierarchical aggregation of health scores provides a comprehensive view for targeted optimization. This transparent and intuitive framework empowers organizations to make informed decisions regarding cloud workloads deployment, resource allocation, and security measures.

DOI: 10.33545/27075907.2024.v5.i2b.73

Pages: 85-92 | Views: 548 | Downloads: 127

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