Dynamic Workload Tiering via Predictive Analytics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current enterprise workload management is static and prone to human error, leading to inefficient resource allocation and increased costs due to the lack of predictive tier changes and adaptive management strategies.
Innovation Solution
A method for dynamically managing enterprise workloads by analyzing past data to suggest service level agreements and determining optimal management service tiers, with a tier change rule policy generated and executed based on user approval, allowing for adaptive tier changes across variable time blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a static tier assignment is used for workload management, then the management process is simple and stable, but resource allocation becomes inefficient and costs increase when workload usage changes
Solution Approach 1:
The patent implements dynamic tier assignment that automatically adjusts management tiers based on real-time workload parameters and usage patterns. The system transitions from static to dynamic tier management by continuously monitoring workload metrics and automatically reassigning tiers to optimize resource allocation and reduce costs when usage changes.
Solution Approach 2:
The system incorporates feedback mechanisms by monitoring workload parameters, usage patterns, and performance metrics to continuously evaluate and adjust tier assignments. This closed-loop feedback enables the system to respond to changing workload conditions and optimize resource allocation dynamically.
2Ease of operation
If manual tier change processes are used, then human control is maintained, but disruption, delays, and human error increase
Solution Approach 1:
The system enables self-service automated tier management by implementing intelligent algorithms that automatically detect when tier changes are needed, evaluate optimal tier assignments, and execute tier transitions without manual intervention. This eliminates human error and accelerates the tier change process while maintaining organizational policies through configurable parameters.
Solution Approach 2:
The system performs preliminary analysis of workload parameters and predicts optimal tier assignments before changes are needed. By pre-evaluating workload patterns and preparing tier change recommendations in advance, the system reduces disruption and delays when actual tier changes are executed.
3Reliability
If resources are allocated based on peak usage, then service level is maintained, but resources are wasted when usage drops
Solution Approach 1:
The system dynamically changes resource allocation parameters based on actual workload usage patterns. By monitoring usage metrics and adjusting resource allocation parameters in real-time, the system maintains adequate resources during peak usage to ensure service levels while reducing resource allocation during low-usage periods to eliminate waste.
4Productivity
If predictive tier change capabilities are added, then resource optimization improves, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary intelligent layer that sits between workload monitoring and tier management. This intermediary layer uses learning algorithms and predictive analytics to process workload data and generate tier change recommendations, simplifying the overall system architecture while enabling sophisticated predictive optimization capabilities.
Data Source
AI summary
A model drive system models tier changes within enterprise workloads by analyzing past data to automatically generate the capability to detect factors or situations which demand a change of service tier in a preferably hybrid cloud context with potentially multiple providers.