Domain Specific Language for Cloud Metric State Monitoring
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Solution Overview
Problem
Cloud monitoring systems face difficulties in determining which applications on large numbers of cloud-hosted systems are associated with specific problems due to numerous possible states and high complexity in filtering out false positives, especially when using classical web services like REST and OData, which are not intuitive for filtering and combining resources effectively.
Innovation Solution
A domain-specific language (DSL) for cloud-hosted systems with metric-based state is introduced, allowing for human-readable queries to retrieve and filter metrics data from cloud systems, simplifying the process of identifying application issues by translating DSL queries into complex queries and providing auto-completion features to enhance query accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classical web services like REST and OData are used to query cloud systems, then data can be retrieved from multiple cloud-hosted systems, but the complexity of filtering and combining resources increases significantly, leading to difficulty in identifying application issues and high false positive rates
Solution Approach 1:
The patent introduces a domain-specific language (DSL) as an intermediary layer between the user and the complex cloud monitoring data. The DSL provides simplified query constructs that automatically translate into complex filtering operations, reducing the complexity burden on users while maintaining accurate issue identification capabilities.
Solution Approach 2:
The patent changes the parameter of query expression from complex resource filtering syntax to simple metric-based state expressions. By allowing users to query based on application states (e.g., error rates, response times) rather than complex resource combinations, the system reduces filtering complexity while improving reliability of issue identification.
2Reliability
If comprehensive metrics data is collected from hundreds or thousands of cloud-hosted systems, then monitoring coverage is improved, but the difficulty of detecting and measuring specific problem states increases due to the large volume of data
Solution Approach 1:
The patent extracts and focuses on specific metric-based states that are indicative of problem conditions rather than analyzing all possible resource combinations. By extracting relevant metrics (error rates, response times, throughput) and defining problem states based on these metrics, the system reduces the difficulty of detecting issues while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The patent applies local quality by defining specific metric thresholds and states for different parts of the system. Rather than applying uniform complex filtering across all data, the system uses localized metric-based state definitions that are tailored to specific application conditions, making detection and measurement easier while maintaining comprehensive coverage.
3Measurement precision
If complex filtering operations are performed to eliminate false positives, then the precision of problem identification is improved, but the time required to process and analyze data increases
Solution Approach 1:
The patent performs preliminary action by pre-defining metric-based states and problem conditions. Instead of performing complex filtering operations at query time, the system pre-processes and structures data according to defined metric states, allowing for rapid retrieval and precise identification of problems without time-consuming filtering operations.
Data Source
AI summary
Methods and systems are used for providing a domain specific language (DSL) for a cloud hosted system with metric-based state. As an example, a first DSL query request including first DSL query input based on a DSL is received. The first DSL query input is translated to a first set of query instructions based on the first DSL query input, translation rules metadata, and the DSL. The first set of query instructions is executed to retrieve, for each cloud system of a plurality of cloud systems, metrics data associated with an application state of an application deployed on each cloud system stored in a cloud deployment repository. A first set of response output is computed by matching the retrieved metrics data from the cloud deployment repository to the first DSL query input. A first DSL query response including the first set of response output is transmitted to a client system.