Application Hosting Benchmarking via Predictive Simulation
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Solution Overview
Problem
Conventional methodologies for determining the optimal networked environment for hosting applications lack cost and performance optimization, leading to uncertain satisfaction of application performance requirements due to insufficient data and delayed availability of performance metrics.
Innovation Solution
A method providing predictive cost and performance analytics through simulation of application deployment in various networked environments, using a graphical user interface to input requests, retrieve relevant data storage objects, simulate deployment, collect metrics, and determine predicted implementation information using models, which includes deriving pricing information and displaying results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methodologies are used to determine networked environment for hosting applications, then decision-making process is simple, but cost and performance optimization is insufficient
Solution Approach 1:
The system performs simulation of application deployment in advance before actual hosting decisions are made. This preliminary simulation action provides predictive cost and performance analytics, allowing decision-makers to evaluate different networked environments without actually deploying applications, thereby obtaining precise cost and performance data beforehand.
Solution Approach 2:
The system creates a simulated copy of the application deployment process rather than requiring actual deployment. By copying the deployment workflow in a virtual simulation environment, the system can gather performance metrics and cost information without the complexity and time constraints of real deployments, achieving measurement precision without proportional complexity increase.
2Reliability
If application workload is hosted for certain time before performance data is available, then actual performance metrics can be measured, but satisfaction of application performance requirements cannot be guaranteed in advance
Solution Approach 1:
The system performs simulation of application deployment and performance measurement in advance before actual hosting decisions. This preliminary action provides predictive performance analytics that guarantee satisfaction of application performance requirements beforehand, eliminating the waiting period typically required to obtain performance data after actual deployment.
Solution Approach 2:
The simulation environment acts as an intermediary between deployment decisions and actual performance measurement. Instead of directly deploying applications and waiting for performance data, the simulation intermediary provides predictive performance metrics that reflect expected actual performance, thereby guaranteeing performance requirement satisfaction without time loss.
3Adaptability or versatility
If only cursory data such as cost per compute load is available, then data collection is simple, but cost and performance optimization for different operating scenarios is not achieved
Solution Approach 1:
The system simulates application deployment across multiple networked environments in advance, gathering comprehensive performance and cost data for different operating scenarios before decisions are made. This preliminary data collection provides complete information tailored to specific application requirements and operating scenarios, rather than generic cursory data.
Solution Approach 2:
The simulation process tailors performance analytics to specific local conditions of different networked environments and application requirements. By customizing the simulation parameters and metrics according to each scenario's specific needs, the system provides adaptability for different operating scenarios with complete and relevant information for each case.
4Measurement precision
If simulation of application deployment is performed to obtain predictive analytics, then cost and performance optimization is achieved, but additional processing steps are required
Solution Approach 1:
The system uses simulation to create a virtual copy of the application deployment process, which can be executed rapidly without the constraints of actual hardware provisioning and application startup times. This copying approach maintains measurement precision by replicating realistic deployment conditions while significantly improving productivity by eliminating physical deployment delays.
Data Source
AI summary
A method for providing predictive cost and performance analytics to facilitate benchmarking of an application host is disclosed. The method includes receiving, via a graphical user interface, an input, the input including a request to benchmark a networked environment to host an application; retrieving, from a repository based on the input, a data storage object that corresponds to the application, the data storage object including a deployment artifact and a performance script; simulating, based on the retrieved data storage object, deployment of the application in the networked environment; collecting, from the networked environment, a result of the simulation, the result including a metric that corresponds to the application; and determining, by using a model, predicted implementation information that corresponds to the application based on the result.