Tenant-Specific Changelog Filtering in Multi-Tenant Cloud Services
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Solution Overview
Problem
In large-scale multi-tenant cloud services, it is challenging for engineers and tenants to determine the source of anomalies following changes to hardware or software components, as these changes can impact the service unpredictably and result in difficulty in identifying relevant information among thousands of changes.
Innovation Solution
The implementation of a system that collects and stores changelogs describing changes to hardware or software components, allowing for the generation of a user interface that presents only relevant changes to specific tenants, using machine learning-based classification and confidence scoring to filter and prioritize changelogs, and incorporating expert corrections and tenant interaction data to refine models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all changelogs from thousands of components are collected and presented to tenants, then complete information about service changes is provided, but the complexity of information processing and the burden on tenants increases significantly
Solution Approach 1:
The system segments the large set of all changelogs into tenant-specific subsets based on relevance to each tenant's service components. This segmentation allows the system to maintain complete changelog records centrally while presenting only relevant portions to each tenant, thereby reducing information overload without sacrificing completeness.
Solution Approach 2:
The system introduces an intermediary classification service that acts as a mediator between the complete changelog data and tenant users. This intermediary applies machine learning models to filter and prioritize changelogs, transforming the raw comprehensive data into curated, relevant information for each tenant without losing the underlying completeness.
2Ease of operation
If machine learning classification is applied to filter changelogs for each tenant, then relevant information is prioritized and presented, but computational resources and processing time are consumed
Solution Approach 1:
The system performs preliminary classification of changelogs in advance, before tenants need to review them. By pre-processing and organizing changelogs according to tenant relevance using machine learning models, the system reduces the computational burden during tenant access and enables faster retrieval of relevant information without repeated heavy processing.
Solution Approach 2:
The system dynamically adjusts classification parameters and model complexity based on the specific context, tenant requirements, and available computational resources. This allows the system to optimize the balance between classification accuracy and energy consumption, applying more sophisticated models only when necessary.
3Loss of information
If extensive changelogs are stored and transmitted to all tenants, then complete change history is available, but network bandwidth and storage resources are consumed
Solution Approach 1:
The system applies local quality by storing complete changelog information centrally with full detail, while providing tenant-specific views that contain only the locally relevant portion for each tenant. This allows the system to maintain complete change history for audit and analysis purposes while transmitting minimal data to each tenant, reducing network and storage overhead.
Data Source
AI summary
Technologies are described herein for identification and presentation of changelogs relevant to a tenant of a multi-tenant cloud service. Change feature extraction is performed on changelogs associated with a tenant of the multi-tenant cloud service to identify features associated with the changelogs. Machine learning based classification can then be performed on the changelogs to classify the changelogs. Misclassification correction might also be performed on the classified changelogs. Machine learning can also be utilized to identify a subset of the changelogs as being relevant to the tenant. A user interface (UI) can then be generated and provided to the tenant that includes the subset of the changelogs. The tenant's interaction with the changelogs presented in the UI can be monitored and data describing the interaction can be used to modify machine learning models utilized for machine learning change classification and for determining the relevance of a changelog to the tenant.