Virtualization Assessment Engine for Server Deployment Optimization
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Solution Overview
Problem
The manual distribution of virtualization applications and software applications across computer servers in data centers often leads to inefficiencies due to over-cautious loading, resulting in server sprawl and suboptimal utilization of resources like power, cooling, memory, storage, and physical space, as system administrators struggle to accurately determine efficient deployment plans considering complex technical and business factors.
Innovation Solution
A virtualization assessment engine is used to analyze and generate deployment plans by processing data on target applications and hardware, matching application attributes with predefined models, determining compatibility, and optimizing virtual machine and application placement to minimize resource conflicts and maximize efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual distribution of virtualization applications and software applications is used, then system administrators can control deployment, but resource utilization becomes suboptimal and server sprawl occurs
Solution Approach 1:
The system enables self-service through automated assessment engines that independently analyze application requirements, evaluate server compatibility, and generate optimal deployment plans without requiring manual administrator intervention for each deployment decision
Solution Approach 2:
The patent replaces the mechanical manual process of administrator-driven deployment with an automated computational system that uses assessment engines to evaluate applications, servers, and compatibility factors, substituting human decision-making with algorithmic optimization
2Reliability
If over-cautious loading is applied to avoid server overload, then system reliability is maintained, but server sprawl increases and physical space is wasted
Solution Approach 1:
The assessment engine performs preliminary evaluation of application requirements and server capabilities before deployment, pre-identifying compatible servers and predicting resource usage patterns to prevent both overload and underutilization
Solution Approach 2:
The system dynamically adjusts deployment parameters based on real-time server status and application requirements, changing load thresholds and resource allocation parameters to optimize both reliability and space utilization rather than using fixed conservative limits
3Measurement precision
If complex technical and business factors are considered in deployment planning, then deployment accuracy improves, but the complexity of determining deployment plans increases
Solution Approach 1:
The assessment engine segments the complex deployment planning process into distinct evaluation modules that separately analyze technical factors (hardware compatibility, resource requirements) and business factors (cost, SLA requirements), evaluating each independently and integrating results systematically
Data Source
AI summary
There is provided a method of determining a deployment of applications in a computer system comprising a plurality of computing hardware. The method comprise obtaining, for each of a plurality of applications, a set of application characteristics, assigning each of the plurality of applications, using the obtained characteristics, to one of a plurality of predetermined application models, and determining a virtual server deployment plan and an application deployment plan, the determination based in part on characteristics of the computer system, characteristics of the applications, and application model compatibility data.