Attribute Discovery for Multi-Cloud Operation Objects
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Solution Overview
Problem
In hybrid and multi-cloud computing environments, existing technologies face challenges in efficiently managing and monitoring workloads across multiple platforms, requiring manual intervention and lacking real-time attribute discovery capabilities for operation objects.
Innovation Solution
A computer-implemented method and system for attribute discovery in operation data, which segments operation object names, performs similarity comparisons, calculates statistical metrics, and mines sequences to automatically identify attributes without prior knowledge of field names or relationships, enhancing monitoring and error detection across multiple clouds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used for monitoring workloads across multiple cloud platforms, then operational control can be maintained, but real-time attribute discovery capabilities are lost and efficiency decreases
Solution Approach 1:
The system performs self-service by automatically discovering attributes of operation objects through analyzing operation data from multiple cloud platforms. The attribute discovery module autonomously extracts field names, data types, and relationships without requiring manual configuration or intervention, enabling the system to serve itself in terms of metadata acquisition and workload monitoring setup.
2Extent of automation
If automated attribute discovery is implemented without predetermined field information, then real-time monitoring capability is improved, but system complexity increases
Solution Approach 1:
The system segments the attribute discovery process into distinct functional modules: an operation data reception module that collects data from multiple cloud platforms, an attribute discovery module that analyzes the data to extract field information, and a workload monitoring module that utilizes the discovered attributes. This segmentation reduces overall system complexity by creating manageable, specialized components with clear interfaces.
Solution Approach 2:
The attribute discovery module acts as an intermediary between the raw operation data from multiple cloud platforms and the workload monitoring functions. It mediates by automatically extracting and structuring field information, data types, and relationships from unstructured operation data, transforming it into a format suitable for monitoring without requiring the monitoring system to directly handle the complexity of multiple cloud data formats.
3Measurement precision
If real-time automated attribute discovery is performed, then monitoring accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by continuously discovering and storing attribute information about operation objects in advance of actual monitoring needs. As operation data flows through the system, the attribute discovery module proactively extracts field names, data types, and relationships, preparing the metadata structure before it is needed for workload analysis and monitoring, thus reducing processing delays when monitoring is required.
Data Source
AI summary
A system and method for attribute discovery for operation objects from operation data includes segmenting a name of each of a plurality of operation objects based on one or more special characters used in the name of each operation object. A similarity comparison of the operation objects is performed by extracting common subsequences from substrings in operation data in a same log as a target object, and a string similarity is computed of the extracted common subsequences. Numerical attributes are determined by calculating statistical metrics for fields in the log, and additional information of the operation objects is discovered based on the determined numerical attributes.