SLO Translation to System Metrics via Decomposition Engine
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Solution Overview
Problem
Determining the appropriate computer resources to provision for enterprise systems to meet high-level service level objectives (SLOs) is complex, often resulting in over-provisioning and under-utilization, due to the iterative and time-consuming nature of the process.
Innovation Solution
The translation of SLOs into low-level system metrics is achieved through component profiles and performance models, which determine resource quantities and configuration parameters by simulating workloads and measuring performance parameters across multiple components of a multi-tier system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If system administrators apply domain knowledge to determine resource provisioning manually, then resource allocation can be adjusted to satisfy SLOs, but the process becomes iterative, time-consuming and complex
Solution Approach 1:
The patent replaces manual administrative processes with an automated computational system. The decomposition engine automatically translates SLOs into component-level targets using performance models, eliminating the need for iterative manual resource determination while maintaining SLO satisfaction.
Solution Approach 2:
The patent introduces performance models and decomposition engines as intermediary components between high-level SLOs and low-level resource provisioning. These intermediaries automatically perform the translation and optimization tasks that previously required manual administrative effort.
2Reliability
If resources are over-provisioned to ensure SLOs are met, then service reliability is improved, but resource utilization decreases
Solution Approach 1:
The patent changes the parameters of resource allocation by using performance models to determine precise resource quantities needed to meet SLOs. Instead of over-provisioning with fixed margins, the system calculates optimal resource levels based on workload characteristics and component profiles, achieving both reliability and efficiency.
Solution Approach 2:
The patent implements feedback mechanisms where performance models continuously evaluate the relationship between resource provisioning and SLO achievement. This feedback loop enables dynamic optimization of resource allocation, ensuring resources are neither over-provisioned nor under-provisioned.
3Reliability
If manual iterative staging is used to verify service behavior, then service quality can be ensured, but the deployment process becomes complex and time-consuming
Solution Approach 1:
The patent performs preliminary actions by translating SLOs into component-level targets before actual service deployment. The decomposition engine pre-calculates resource requirements and performance thresholds, allowing for more streamlined staging processes with reduced complexity.
Solution Approach 2:
The patent segments the overall service verification process into component-level evaluations. By decomposing SLOs into specific component targets and using individual component profiles, the system simplifies the verification process while maintaining comprehensive service quality assurance.
Data Source
AI summary
Service level objectives for a multi-tier system are translated to low-level system metrics by determining component profiles. The component profiles include performance parameters calculated as a function of applied resources for each of a plurality of components of the multi-tier system. Performance models including service level parameters are also determined. The performance models are determined as a function of the performance parameters and workload characteristics. The service level objectives are translated to the low-level system metrics using the performance models.