Multi-Tenant Bot Transport via Compressed File Export
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Solution Overview
Problem
In multi-tenant cloud environments, there is no straightforward method to transfer a bot program, such as a chatbot, from one tenant to another, requiring organizations to rebuild and retrain the bot separately in each tenant, leading to inconsistent models and redundant resource consumption.
Innovation Solution
A host system that enables the transportation of a bot program, including machine learning models, training data, and configurations, by exporting these components into a compressed file, allowing for secure import and installation across different tenants, while maintaining consistency and reducing redundant development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If organizations build and train bots separately in each tenant, then tenant isolation and security are maintained, but development time and resource consumption increase significantly
Solution Approach 1:
The patent implements bot copying across tenants by exporting bot definitions, configurations, and trained models from a source tenant and importing them into target tenants. This allows organizations to replicate successfully trained bots across multiple tenants without retraining, significantly reducing development time while maintaining tenant isolation through secure import/export mechanisms and appropriate permission controls.
2Reliability
If organizations build and train bots separately in each tenant, then tenant security boundaries are preserved, but resource redundancy increases
Solution Approach 1:
Instead of training bots independently in each tenant, the system copies pre-trained bot models and definitions across tenants. This eliminates redundant training computations and resource consumption while preserving tenant security boundaries through controlled access permissions and isolated execution environments for each tenant.
Solution Approach 2:
The bot is trained once in advance in a source tenant where computational resources are available, and then the trained model is copied to multiple target tenants. This preliminary training action avoids repeated resource-intensive training processes in each tenant, significantly reducing overall resource consumption while maintaining security isolation.
3Reliability
If cloud providers do not offer bot transport functionality, then tenant isolation is maintained, but bot deployment flexibility is reduced
Solution Approach 1:
The system introduces an intermediary export/import mechanism that enables bot transport between tenants while maintaining isolation. The bot definition and model are exported from the source tenant through a controlled interface, transmitted securely, and imported into target tenants with appropriate permission validation, thus providing deployment flexibility without compromising tenant isolation.
Data Source
AI summary
Provided are systems and methods for transporting a bot from runtime of a first tenant into a runtime of a second tenant. The transport process can include transferring bot configurations, machine learning models, training data, and the like. In one example, a method may include exporting a file from a first tenant in a multi-tenant environment, wherein the file comprises a chatbot program, one or more machine learning models for generating a response from the chat bot program, and training data used to train the one or more machine learning models for generating the response, importing the file into a second tenant of the multi-tenant environment, and installing the bot program, the one or more machine learning models, and the training data within a directory of the second tenant of the multi-tenant environment.