Active Inventory Orchestration for Dynamic Application Provisioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computing environments struggle with the dynamic scaling of applications due to the lack of efficient methods for automatically deploying and managing computing resources, particularly in complex installations with numerous servers and cloud platforms, where installing agents for data collection is time-consuming and resource-intensive.
Innovation Solution
An orchestrator system that utilizes a vector log agent to process log files from various components, deriving active and available inventory (AAI) to dynamically allocate and manage computing resources, including storage, memory, and processing power, without requiring agents on individual servers, and implements workflows to redeploy and consolidate resources based on usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If agents are installed on individual servers for data collection, then resource inventory information can be gathered, but the deployment process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent extracts the data collection function from individual server agents and consolidates it into a central orchestrator that processes log files directly. This eliminates the need to install and manage agents on every server, reducing deployment time while maintaining the ability to gather comprehensive resource inventory information through log analysis.
Solution Approach 2:
The orchestrator acts as an intermediary between the log files and the provisioning system. Instead of installing agents on servers, the orchestrator processes log files to extract resource inventory information, serving as a mediator that eliminates the need for direct agent installation while still achieving the same data collection goal.
2Extent of automation
If manual deployment methods are used, then control over application deployment is maintained, but automation and efficiency are reduced
Solution Approach 1:
The system enables self-service automation where the orchestrator automatically processes log files, identifies available resources, and deploys applications without manual intervention. The workflow engine automates the provisioning process by reading specifications, identifying suitable hosts, and executing deployment, reducing manual effort while managing complexity through structured automation.
Solution Approach 2:
The system performs preliminary actions by pre-processing log files to create active and available inventory (AAI) data before deployment is needed. This advance preparation of resource information enables automated decision-making during deployment without increasing operational complexity, as the heavy lifting is done beforehand.
3Adaptability or versatility
If computing resources are dynamically scaled, then adaptability to usage changes is improved, but resource allocation efficiency decreases without proper management
Solution Approach 1:
The system implements feedback by continuously monitoring log files to track actual resource usage and comparing it with allocated resources. The orchestrator uses this feedback to identify discrepancies between allocated and used resources, enabling dynamic reallocation that maintains both adaptability to usage changes and allocation efficiency through data-driven decisions.
Solution Approach 2:
The system enables dynamic resource scaling by processing log files to identify current usage patterns and automatically adjusting resource allocation accordingly. The workflow engine dynamically selects appropriate hosts and resources based on real-time conditions, maintaining both adaptability and efficiency through continuous optimization rather than static allocation.
Data Source
AI summary
A computer system pulls observability data (metrics, logs, events, alerts, inventory) for a plurality of components from remote servers, which may be part of a cloud computing platform. The components may be application instances, containers, storage volumes, pods, or other components. The computer system derives a utilization metric for each components and each of one or more types of computing resources: compute, memory, and storage. The utilization metrics are compared to available inventory of computing resources to obtain an active and available inventory (AAI). Components may be redeployed and allocated computing resources reduced based on the AAI. Components may be grouped in clusters and components may be consolidated to a reduced number of clusters based on the AAI. Applications may be provisioned and deployed on clusters in groups of different types (dot, triangle, line, graph) having different runtime requirements based on location, latency, hardware resources, and/or round robin assignment.