Selective Service Discovery via Dynamic Filtering
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Solution Overview
Problem
Current CMDB platforms lack a selective discovery process for services and service groups, leading to inefficient data collection and management in cloud computing environments, where filters and wildcard matching capabilities are limited, resulting in incomplete or inaccurate data sets.
Innovation Solution
A graphical user interface (GUI) is introduced that enables designers to create and configure service-based discovery schedules with filters for defining attributes and attribute values, allowing partial matching and wildcard filters, ensuring that only qualifying services or service groups are discovered, thereby improving the precision and completeness of data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a discovery process is executed without selective filters, then all services are discovered ensuring completeness, but data collection efficiency decreases and resource consumption increases
Solution Approach 1:
The system performs preliminary filtering of services based on discovery criteria before executing the discovery process. This preliminary action identifies and selects only those services that match the specified filters, ensuring that the discovery process is executed only on relevant services, thus maintaining completeness while improving efficiency.
Solution Approach 2:
Instead of discovering all services without distinction, the system applies partial action by selectively discovering only a subset of services that meet specific criteria. This partial discovery approach avoids the excessive resource consumption of discovering all services while ensuring that all relevant services are captured through the filter mechanisms.
2Productivity
If selective filters are applied to discover only specific services, then data collection efficiency improves, but the risk of incomplete data sets increases
Solution Approach 1:
The filter mechanism is designed to be universal and multi-functional, supporting multiple filter types including exact match, partial match, and wildcard filters. This universality ensures that regardless of which filtering approach is used, the system can comprehensively capture all relevant services without missing any that meet the criteria, thus maintaining completeness while improving efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms where the discovery process can be reviewed and filters can be adjusted based on results. This allows verification that the selective filtering has not inadvertently excluded relevant services, ensuring completeness is maintained while benefiting from the efficiency gains of selective discovery.
3Measurement precision
If wildcard matching and partial matching capabilities are added to filters, then filtering precision improves, but system complexity increases
Solution Approach 1:
The system enhances filtering precision by changing the parameters of the filter matching mechanism to support multiple matching modes (exact match, partial match, wildcard match). By adjusting the matching parameter rather than adding entirely separate filtering systems, the solution achieves high precision while controlling the increase in system complexity.
4Measurement precision
If multiple filter types and matching options are implemented, then service selection accuracy improves, but ease of operation decreases
Solution Approach 1:
The filter configuration system is designed to be dynamic and adaptive. It automatically adjusts the matching behavior based on the filter criteria specified, and provides flexible configuration options that can be modified without requiring deep system knowledge. This dynamic approach maintains high service selection accuracy while preserving ease of operation through user-friendly configuration.
Data Source
AI summary
A system and a process are disclosed for selective discovery of services. Present embodiments include a graphical user interface (GUI) that enables a designer to create and configure a discovery schedule that includes one or more filters. These filters enable the designer to define particular attributes and attribute values of services or service groups. Each time the discovery schedule is executed, the discovery scheduler again determines which currently defined services satisfy the conditions of the filters, and then instructs a discovery server to discover only the qualifying services associated with a client network.