Cross-OS Command Translation for Unified Distributed Execution
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Solution Overview
Problem
Current tools lack the ability to efficiently execute commands and organize results in a unified manner across distributed computing networks, leading to inefficiencies and manual intensive tasks in routine maintenance and monitoring.
Innovation Solution
A method for executing commands and scripts across a distributed computing network, translating commands for different operating systems, and displaying results in a unified interface, with features like a recommender engine for command selection and dynamic script modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If commands are executed individually on each computing system, then detailed system information can be gathered, but the process becomes manual intensive and time consuming
Solution Approach 1:
The system segments the command execution process by distributing commands to multiple computing systems simultaneously. Each computing system executes its portion of the command set independently, and results are aggregated back to the user interface. This segmentation enables parallel processing across distributed systems, dramatically improving productivity while reducing time loss.
Solution Approach 2:
The system introduces an intermediary layer between the user and individual computing systems. This intermediary (the command execution system) receives commands from the user, distributes them to appropriate computing systems, collects results, and presents unified output. This mediator eliminates the need for users to manually connect to each system individually, resolving the contradiction between detailed information gathering and manual effort.
2Reliability
If manual individualized maintenance is performed, then system monitoring can be conducted, but computing and human resources are inefficiently dedicated
Solution Approach 1:
The computing systems perform self-monitoring and self-reporting through automated command execution. Systems automatically execute monitoring commands, gather their own status information, and return results without requiring manual intervention. This self-service capability maintains reliable system monitoring while dramatically improving resource utilization efficiency by eliminating manual labor.
Solution Approach 2:
The system provides universal command execution capability that works across multiple computing systems with different operating systems. A single command interface can monitor diverse systems (Windows, Linux, macOS) through platform-specific scripts, making the monitoring process multi-functional and resource-efficient while maintaining comprehensive system reliability.
3Adaptability or versatility
If commands are translated for different operating systems, then compatibility is achieved, but system complexity increases
Solution Approach 1:
The system applies local quality by providing platform-specific command translations only where needed. Instead of treating all systems uniformly, the system detects the operating system type and applies appropriate translation rules locally for each platform. This approach achieves cross-platform adaptability while managing complexity by applying transformations only at the necessary local level rather than universally.
Solution Approach 2:
The system uses copying by creating platform-specific versions of commands through script templates. Instead of writing unique commands for each operating system, the system copies a master command template and adapts it to platform-specific syntax. This copying approach simplifies the translation mechanism by reusing templates rather than maintaining separate complex translation logic for each platform.
4Productivity
If results from multiple systems are organized in unified manner, then analytics become efficient, but data processing complexity increases
Solution Approach 1:
The system applies parameter changes by transforming results from different operating systems into a standardized format with consistent parameters. The aggregation process modifies data parameters (such as converting different output formats to JSON, normalizing field names, and standardizing data types) to create uniform result sets. This parameter standardization enables efficient analytics processing while managing complexity through systematic transformation rules rather than ad-hoc processing.
Data Source
AI summary
Systems and methods for executing commands and scripts across operating systems and gathering results in a unified manner are provided. A method includes receiving one or more commands from a predefined list of commands from one or more devices in a distributed computing environment; receiving a set of targets distributed in the distributed computing environment; translating the one or more commands to a set of target commands, wherein each target command is associated with at least one target of the set of targets; transmitting a request to execute a script associated with each of the target commands to each of the targets; executing the script on each of the targets thereby generating a plurality of results corresponding to each of the target commands and each of the targets; and storing the plurality of results in a format for display in a user interface.