Low-Code App Assembly With Tenant-Specific Observability Injection
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Solution Overview
Problem
The challenge of maintaining observability in low-code applications, particularly those hosted outside the enterprise's security perimeter, is exacerbated by the lack of visibility during creation and deployment, leading to issues like unmanaged resource consumption, data leakage, and compliance challenges.
Innovation Solution
Implementing a method to determine tenant-specific policies for low-code applications, dynamically computing injectable tasks, and injecting observability tasks at creation time to enforce runtime observability requirements, ensuring compliance and visibility through a custom observability policy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If low-code applications are hosted outside enterprise security perimeter to enable rapid development and deployment, then productivity and ease of operation are improved, but observability and compliance control deteriorate
Solution Approach 1:
The system performs preliminary actions by determining tenant-specific policies and dynamically computing observability requirements before the low-code application is created. This allows observability tasks to be pre-configured and injected at creation time, ensuring visibility is established upfront rather than attempting to add it later to applications already deployed outside the security perimeter.
Solution Approach 2:
The patent introduces an intermediary mechanism that acts as a bridge between the low-code application platform and enterprise observability requirements. This intermediary dynamically computes observability tasks based on tenant policies and injects them into applications, enabling compliance control without directly restricting application deployment locations or development processes.
2Reliability
If observability tasks are added after application deployment, then compliance monitoring can be implemented, but resource consumption increases and application performance deteriorates
Solution Approach 1:
Observability tasks are determined and computed in advance based on tenant-specific policies before the application is created and deployed. This preliminary configuration ensures that monitoring capabilities are built-in from the start, eliminating the need to add heavy monitoring agents or tasks after deployment, thus avoiding post-deployment resource overhead.
Solution Approach 2:
The system dynamically computes observability tasks by changing parameters based on tenant-specific policies and application characteristics. This allows the observability configuration to be optimized for each application's specific needs rather than applying a one-size-fits-all monitoring approach, reducing unnecessary resource consumption while maintaining compliance monitoring effectiveness.
3Ease of manufacture
If generic observability policies are applied to all low-code applications, then implementation simplicity is improved, but adaptability to individual enterprise needs deteriorates
Solution Approach 1:
The system transitions from static generic policies to dynamic tenant-specific policies. The policy determination process adapts to each tenant's unique requirements by dynamically computing observability tasks based on specific enterprise policies, application parameters, and compliance needs. This dynamic approach maintains simplicity through automation while achieving high adaptability to individual enterprise contexts.
Solution Approach 2:
Instead of applying uniform observability policies across all applications, the system implements local quality by tailoring observability requirements to each tenant and application specifically. Each low-code application receives customized observability tasks determined by its tenant's specific policies and the application's particular characteristics, ensuring optimal fit for each local context while maintaining overall system coherence.
Data Source
AI summary
In one embodiment, an illustrative method herein may comprise: determining, by a process, a tenant-specific policy for creation of low-code applications; dynamically computing, by the process and based on the tenant-specific policy and one or more parameters associated with a particular low-code application to be created, one or more injectable low-code tasks for the particular low-code application; determining, by the process, a plurality of selected injectable low-code tasks from the one or more injectable low-code tasks; and creating, by the process, the particular low-code application by injecting the plurality of selected injectable low-code tasks into the particular low-code application for execution.


