Dynamic Resource Allocation Model for IT Workloads
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Organizations face challenges in optimizing IT service environments due to the proliferation of data, leading to unnecessary overprovisioning of resources, which results in underutilization of resources over extended periods.
Innovation Solution
A dynamic model is generated to predict performance based on various metrics, allowing for adaptive allocation of computer resources to workloads, incorporating machine learning and gamification to improve resource utilization and employee engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If more computer resources are provisioned to handle data proliferation, then system capacity and reliability are improved, but resource utilization efficiency deteriorates due to overprovisioning and extended periods of unused resources
Solution Approach 1:
The patent implements dynamic resource allocation where the system continuously monitors workload metrics and automatically adjusts resource provisioning in real-time. The orchestration platform dynamically scales resources up or down based on actual demand, transforming the static overprovisioned infrastructure into a flexible, adaptive system that maintains reliability while optimizing utilization efficiency
Solution Approach 2:
The system employs self-service mechanisms through automated orchestration that monitors its own resource utilization and workloads. The platform autonomously makes provisioning decisions based on monitored metrics without requiring manual intervention, enabling the system to self-optimize resource allocation and eliminate the need for excessive static provisioning
2Reliability
If static resource provisioning is used to ensure adequate system capacity, then reliability is improved, but adaptability deteriorates due to inability to respond to changing workload demands
Solution Approach 1:
The orchestration platform transforms static resource provisioning into a dynamic system that continuously adapts to changing workload demands. By implementing real-time monitoring and automated scaling capabilities, the system maintains adequate capacity for reliability while simultaneously achieving adaptability to respond to varying workloads efficiently
Solution Approach 2:
The system implements feedback mechanisms where workload metrics are continuously monitored and fed back to the orchestration platform. This feedback loop enables the system to adjust resource provisioning in response to actual workload conditions, simultaneously ensuring adequate capacity for reliability while achieving adaptability to changing demands
3Device complexity
If manual resource allocation processes are used, then system complexity is reduced, but productivity deteriorates due to time-consuming allocation and lack of optimization
Solution Approach 1:
The orchestration platform implements self-service automation that autonomously performs resource allocation based on monitored workload metrics. This automated system eliminates time-consuming manual allocation processes while incorporating optimization algorithms that improve resource utilization efficiency, achieving high productivity without proportionally increasing system complexity
Data Source
AI summary
According to an example of the present disclosure, an information technology (IT) environment, such as a data center, a cloud services platform or other type of computing environment, may include computer resources that are dynamically allocated to workloads. A model is generated to estimate performance and allocate computer resources to the workloads.