Learned Scheduling of Autonomous Actions in ChatOps
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Solution Overview
Problem
Current collaboration platforms lack an effective mechanism to ensure efficient use of resources, leading to idle hardware and software resources and potential security breaches due to untimely resource release.
Innovation Solution
The method generates learned schedules for action executions on a collaboration platform using natural language processing of contextual information from ChatOps conversations, clustering action executions based on past execution times, and ranking recommendation candidates to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resources are allocated to users on collaboration platforms, then resource availability and user productivity are improved, but resource idle time increases and security risks arise from untimely resource release
Solution Approach 1:
The system performs preliminary actions by analyzing historical ChatOps conversations and action execution patterns to predict future resource needs. It proactively schedules resource allocation and release times before actual usage patterns occur, thereby preventing resource idle time while ensuring resources are available when needed for user productivity.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual resource usage against predicted patterns. It analyzes ChatOps conversations and action executions in real-time, comparing them with learned schedules, and adjusts future resource allocation predictions based on deviations from expected patterns, thereby optimizing resource utilization and reducing idle time.
2Device complexity
If manual resource management is used, then system complexity is reduced, but resource utilization efficiency deteriorates and security breaches may occur
Solution Approach 1:
The system enables self-service by automatically analyzing ChatOps conversations, learning action execution patterns, and autonomously generating optimized resource allocation schedules without requiring manual intervention. The system serves itself by continuously improving its predictions based on accumulated data, thereby achieving high resource utilization efficiency while maintaining relatively simple system architecture.
Solution Approach 2:
The system introduces an intermediary layer between users and resource management infrastructure. This intermediary automatically processes ChatOps conversations, predicts resource needs, and manages resource allocation, thereby shielding users from complexity while achieving optimized resource utilization and preventing security breaches through automated timely release.
3Productivity
If automated scheduling is implemented, then resource utilization is improved, but system complexity and processing requirements increase
Solution Approach 1:
The system achieves multi-functionality by combining natural language processing of ChatOps conversations, historical pattern analysis, predictive scheduling, and automated resource management into a single unified platform. This universal system handles multiple functions that would otherwise require separate tools, thereby improving resource utilization while keeping overall system complexity manageable through consolidation.
Data Source
AI summary
Learned scheduling of autonomous actions includes generating groups of action executions that execute on a collaboration platform. The groups of action executions are generated by natural language processing of contextual information extracted from one or more Chat Operations conversations. Recommendation candidates corresponding to the action executions are generated by clustering action executions contained in each of the groups. The action executions are clustered based on times of past executions. A leaned schedule is generated by ranking the recommendation candidates based on the contextual information. As generated, the learned schedule indicates one or more recommendations to execute a specific action within a specific time.