Task-Level Permissioning for Datacenter Management
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Solution Overview
Problem
Managing datacenter operations across multiple technologies and servers is complex due to the need for specialized knowledge and broad permissions, leading to resource allocation challenges and increased costs, especially when tasks span multiple technology silos and servers.
Innovation Solution
The integration of heterogeneous task providers and adapters through a standardized interface using dependency inversion, allowing for unified task execution and permissioning at the task level, reducing complexity and resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If resources are given broad permissions to perform tasks across multiple technologies, then task execution capability is improved, but the principle of least privilege becomes difficult to employ and security risk increases
Solution Approach 1:
The patent segments permissions into task-level scopes rather than resource-level broad permissions. Each task is assigned specific permissions scoped to the exact resources and technologies it needs to access, eliminating the need for broad permissions while maintaining task execution capability across multiple technologies.
Solution Approach 2:
The patent inverts the traditional permission model by moving from resource-level permission assignment to task-level permission assignment. Instead of giving resources broad permissions to access multiple technologies, the system assigns specific permissions to tasks based on their actual needs, reversing the conventional approach and enabling least privilege compliance.
2Adaptability or versatility
If multiple participants are employed to complete tasks spanning technology silos, then task completion capability is improved, but organizational complexity and coordination difficulty increase
Solution Approach 1:
The patent introduces a universal task execution framework that can handle tasks across multiple technologies through a common interface. This multi-functional platform consolidates what would otherwise require multiple specialized tools and participants into a single unified system, reducing organizational complexity while maintaining cross-technology capability.
Solution Approach 2:
The patent introduces a task execution framework as an intermediary layer between different technology silos and organizational participants. This mediator consolidates permissions, resources, and execution logic into a unified task model, eliminating the need for direct coordination between multiple participants and reducing organizational complexity.
3Adaptability or versatility
If resources with knowledge to perform tasks across multiple technologies are employed, then task execution capability is improved, but resource costs and acquisition difficulty increase
Solution Approach 1:
The patent segments technology knowledge requirements into discrete task-level permissions rather than requiring resources to have broad multi-technology expertise. By assigning permissions at the task level, the system can utilize resources with specialized knowledge for specific tasks rather than requiring expensive multi-technology experts for all tasks.
Solution Approach 2:
The patent enables the task execution framework to automatically manage permission assignment and resource allocation based on task requirements. This self-service mechanism eliminates the need for manual coordination and expert intervention in resource allocation, reducing acquisition costs and simplifying the process of deploying multi-technology tasks.
Data Source
AI summary
A Datacenter Management Service (DMS) is provided as a platform designed to automate datacenter management tasks that are performed across multiple technology silos and datacenter servers or collections of servers. The infrastructure to perform the automation is provided by integrating heterogeneous task providers and implementations into a set of standardized adapters through dependency inversion. A platform automating datacenter management tasks may include three main components: integration of adapters into an interface allowing a common interface for datacenter task execution, an execution platform that works against the adapters, and implementation of the adapters for a given type of datacenter management task.


