Inter-Cluster Dependency Mapping for Microservice Prioritization
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Solution Overview
Problem
Existing systems lack comprehensive methods to provide inter-cluster dependency information and prioritize services based on health scores, business values, risk scores, and root-cause scores, leading to suboptimal decision-making in service revisions and upgrades.
Innovation Solution
A system determines health scores, business values, risk scores, and root-cause scores using microservice management tools and Application Performance Monitoring agents, and generates user interfaces to provide these metrics, along with inter-cluster dependency information, enabling informed prioritization of services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If health scores are provided to users for service prioritization, then users can determine which services need attention, but users lack additional contextual information (inter-cluster dependencies, business values, risk scores, root-cause scores) needed for optimal decision-making
Solution Approach 1:
The patent combines multiple separate scoring systems (health scores, business values, risk scores, root-cause scores, and inter-cluster dependencies) into a unified service prioritization interface. This merging provides comprehensive information to users without requiring them to access multiple separate systems, thereby reducing information loss while managing complexity through integration.
Solution Approach 2:
The management tool is designed to perform multiple functions: calculating health scores, determining business values, assessing risk scores, analyzing root-cause scores, and evaluating inter-cluster dependencies. This multi-functional approach ensures all necessary information is available within a single system, addressing the information completeness need while consolidating rather than increasing overall system complexity.
2Measurement precision
If multiple metrics (health scores, business values, risk scores, root-cause scores) are calculated and provided, then decision-making quality improves, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary calculations of health scores, business values, risk scores, and root-cause scores in advance, before users need to make prioritization decisions. By pre-computing these metrics and making them available in the management interface, the system reduces the time required at the moment of decision-making while maintaining high measurement precision through thorough prior analysis.
Solution Approach 2:
The management tool automatically calculates and updates all scoring metrics without requiring manual intervention. The system continuously monitors services, computes the various scores, and presents them in the interface, eliminating time-consuming manual calculations while ensuring accurate, up-to-date information is always available for prioritization decisions.
Data Source
AI summary
This disclosure describes techniques for providing information associated with an inter-cluster segment. For instance, system(s) may determine dependencies for first services associated with a first cluster and second dependencies for second services associated with a second cluster. The system(s) may then determine information for interconnections between the first cluster and the second cluster. The information may include at least dependencies for third services included in the inter-cluster segment and/or performance information for the third services. The system(s) may then generate a user interface that includes the first dependencies for the first services, the second dependencies for the second services, and the information for the inter-cluster segment. This way, a user is able to use the user interface to identify both problems occurring within the clusters and/or problems that are caused by the third services in the inter-cluster segment.