Dynamic SLA Risk Mitigation via XAI
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Solution Overview
Problem
Existing service level agreements (SLAs) struggle to account for dynamically evolving infrastructure environments, leading to risks for service providers and increased costs for customers due to unforeseen changes in IT infrastructure.
Innovation Solution
A computer-implemented method using explainable artificial intelligence (XAI) models to identify relationships between infrastructure and applications, assess changes in the technological environment, establish baselines for service level objectives (SLOs), determine risk impacts, generate solution options, and update SLAs accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If SLAs are based on static assumptions and estimates, then the agreement structure is simple and easy to manage, but the service provider cannot adapt to dynamically evolving infrastructure environments leading to increased risk
Solution Approach 1:
The patent implements dynamic SLA management by continuously monitoring infrastructure changes and automatically adjusting SLOs and risk assessments. The system transitions from static, assumption-based SLAs to dynamic SLAs that adapt in real-time to infrastructure evolution, enabling service providers to respond to changing conditions without manual renegotiation.
Solution Approach 2:
The system establishes feedback loops that continuously collect infrastructure data, assess changes against current SLOs, and adjust SLA parameters accordingly. This feedback mechanism ensures the SLA remains aligned with actual infrastructure state, reducing risk while maintaining adaptability.
2Reliability
If static SLOs are used without continuous monitoring, then the SLA is easier to administer, but unforeseen infrastructure changes cause service level breaches and increased costs
Solution Approach 1:
The system performs preliminary risk assessment by continuously monitoring infrastructure changes and proactively identifying potential SLO breaches before they occur. This enables preventive actions to be taken, adjusting SLOs or alerting stakeholders in advance, thereby maintaining reliability without waiting for breaches to manifest.
Solution Approach 2:
The patent implements continuous monitoring and assessment of infrastructure changes against SLOs, ensuring that reliability is maintained through ongoing evaluation rather than periodic checks. This continuous action allows the system to respond immediately to infrastructure evolution, preventing service level degradation.
3Measurement precision
If manual SLA updates are performed for each infrastructure change, then the SLA remains accurate, but the process is time-consuming and costly
Solution Approach 1:
The system performs self-service SLA updates by automatically detecting infrastructure changes, assessing their impact on SLOs, and adjusting SLA parameters without manual intervention. This automated self-adjustment mechanism maintains measurement precision while dramatically improving update efficiency and reducing operational costs.
Solution Approach 2:
The patent replaces manual mechanical SLA update processes with automated computer-based systems that continuously monitor infrastructure, assess risks, and adjust SLAs automatically. This substitution eliminates time-consuming manual procedures while maintaining accurate SLA metrics through systematic computational analysis.
Data Source
AI summary
A method of mitigating risks in a service level agreement (SLA), including: identifying relationships between an infrastructure and an application associated with the SLA; identifying changes to a technological environment of the SLA based on collected data associated with the SLA and the identified relationships, the collected data including the infrastructure and the application; establishing a baseline for a service level objective (SLO) of the SLA by analyzing the collected data of the infrastructure and the application; determining risk impact to the SLA based on an assessment of requirements to the SLO and the changes to the technological environment of the SLA; generating a solution option for the SLA by applying an explainable artificial intelligence (XAI) model based on processing the risk impact with the baseline in the XAI model; and updating the SLA based on the solution option.