Server Resource Recommendation for Multi-Vendor Workload Consolidation
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Solution Overview
Problem
Conventional IT workload management tools lack a unified framework for providing end-to-end visibility, predictability, and actionable recommendations across diverse server architectures, leading to inefficiencies such as underutilized servers and overprovisioning of hardware resources.
Innovation Solution
A system and method that utilizes a multi-vendor processing unit performance database and a deep learning model to recommend and optimize AI workload placement, enabling automatic allocation of processing units across different manufacturers, with real-time performance metrics displayed on a user interface dashboard.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If independent tools are used for monitoring, migration, and performance tracking, then specific functions can be performed, but end-to-end visibility and unified management are lacking
Solution Approach 1:
The patent combines multiple independent tools (monitoring, migration, performance tracking) into a single unified server optimization platform that provides end-to-end visibility across diverse server architectures through a common interface and centralized management system
Solution Approach 2:
The system implements a universal framework capable of managing diverse server architectures (x86, ARM, RISC-V) and multiple virtualization platforms (KVM, VMware, Hyper-V) through a single multi-functional platform that performs monitoring, migration, and optimization tasks
2Measurement precision
If narrow metrics such as CPU utilization are focused on, then specific performance aspects can be monitored, but comprehensive server efficiency including power consumption and consolidation potential are missed
Solution Approach 1:
The system transitions from monitoring single parameters (CPU utilization) to simultaneously optimizing multiple parameters including power consumption, consolidation potential, and performance metrics through multi-objective optimization algorithms that balance competing goals
3Reliability
If servers are overprovisioned to ensure capacity, then service availability is maintained, but resource utilization efficiency and energy expenditure increase
Solution Approach 1:
The system implements dynamic resource allocation that automatically adjusts server provisioning based on real-time workload demands, using predictive analytics to scale resources up or down while maintaining service availability and avoiding overprovisioning
Solution Approach 2:
The platform incorporates continuous feedback loops that monitor actual resource utilization and performance metrics, using this data to dynamically adjust provisioning decisions and provide actionable recommendations for optimizing the balance between availability and efficiency
Data Source
AI summary
A system for recommendation and optimization of information technology (IT) server resources is disclosed. The system includes a server comprising at least one processor configured to access input datasets associated with an IT workload, determine types and counts of server resources based on predefined criteria and a server consolidation configuration, and access a multi-server performance database. The processor utilizes a trained deep learning model to predict infrastructure requirements and generate recommendations for an optimal server configuration by balancing performance, power consumption, and resource utilization. The processor automatically allocates server resources from multiple manufacturers based on the recommendations and generates data for display on a user interface dashboard. The dashboard presents server utilization patterns, recommended configurations, and real-time performance metrics of allocated resources. The system enables intelligent consolidation and efficient server management within a datacenter environment.