Agentless Active and Available Inventory Discovery for Cloud Scaling
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Solution Overview
Problem
Managing on-premise computing hardware and purchased cloud computing resources is challenging due to high variability in usage, making it difficult to efficiently allocate and scale computing resources.
Innovation Solution
An apparatus and method for determining active and available inventory (AAI) of computing resources by processing log data from remote servers to identify unused resources, which includes a vector log agent to enrich data and a log processor to derive AAI, enabling efficient allocation and redeployment of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If computing resources are dynamically scaled up and down to adapt to usage changes, then adaptability is improved, but resource utilization efficiency deteriorates due to difficulty in managing on-premise hardware and cloud resources
Solution Approach 1:
The system continuously monitors log data from remote servers to track actual computing resource usage patterns. This feedback mechanism enables the system to identify unused or underutilized resources and dynamically reallocate them, creating a closed-loop control system that adapts to usage changes while maintaining high utilization efficiency. The feedback drives automated decisions about resource provisioning and deprovisioning.
Solution Approach 2:
The system automatically discovers, monitors, and reallocates computing resources without requiring manual intervention. By processing log data and autonomously identifying unused resources, the system serves itself in managing the complexity of multi-cloud and on-premise infrastructure, enabling dynamic scaling while maintaining optimal resource utilization through self-directed resource orchestration.
2Device complexity
If manual management methods are used for computing resources, then device complexity is reduced, but productivity deteriorates due to difficulty in tracking and allocating resources efficiently
Solution Approach 1:
The system introduces an intermediary layer that sits between the diverse computing resources (on-premise servers and cloud instances) and the management processes. This intermediary automatically processes log data, discovers resource usage patterns, and makes allocation decisions, thereby reducing the complexity of directly managing heterogeneous resources while significantly improving allocation efficiency through automated intelligence.
Solution Approach 2:
The system creates a virtual representation or copy of the physical computing infrastructure by processing and analyzing log data. This digital twin or model of resource usage enables efficient tracking, monitoring, and reallocation of resources without requiring direct manual intervention with the physical systems, thereby reducing management complexity while maintaining high productivity.
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). The one or more components may be modified based on the AAI such as by adding a component, deleting a component, or moving a component to a new host.