Cloud Application Deployment Scheduler with Optimization Matching
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Solution Overview
Problem
Deploying computing applications on clouds often faces challenges in optimizing resource allocation and cost management, as existing methods lack efficient mechanisms to match application requirements with available resources, leading to suboptimal performance and higher costs.
Innovation Solution
A smart scheduler system that determines an optimized configuration for deploying computing applications on clouds by matching application requirements with a resource model, using a cloud resource inventory, and adjusting virtual instances based on optimization goals such as cost, availability, and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional deployment methods are used, then implementation is simple, but resource allocation efficiency is low and costs are high
Solution Approach 1:
The patent introduces a smart scheduler as an intermediary component between the application deployment system and cloud resources. The smart scheduler receives deployment requests, automatically determines optimized configurations by matching application requirements with cloud resource models, and manages resource allocation. This intermediary layer handles the complexity of resource optimization internally while presenting a simplified interface to users, thereby improving resource allocation efficiency without requiring users to directly manage complex optimization logic.
2Reliability
If manual configuration is used, then setup is straightforward, but performance optimization is insufficient
Solution Approach 1:
The smart scheduler implements self-service by automatically determining optimized deployment configurations without requiring manual user input. When a deployment request is received, the system autonomously matches application requirements with available cloud resources, selects appropriate virtualization service providers and machine instances, and adjusts resource allocation based on optimization goals such as cost, availability, and reliability. This self-service mechanism ensures high deployment performance while eliminating the need for users to perform complex manual configuration tasks.
3Loss of energy
If cloud resources are not optimized, then deployment is faster, but costs increase significantly
Solution Approach 1:
The smart scheduler optimizes cost efficiency by dynamically changing parameters related to resource allocation and configuration. It evaluates multiple cloud resource options with different characteristics (virtualization service providers, machine instances, pricing models) and selects configurations that minimize costs while meeting performance requirements. The system adjusts resource allocation parameters based on optimization goals, application requirements, and available cloud inventory, thereby reducing unnecessary resource consumption and associated costs without requiring users to manually manage complex resource parameters.
4Productivity
If automated smart scheduler is implemented, then resource allocation is optimized, but system complexity increases
Solution Approach 1:
The smart scheduler system is segmented into distinct functional modules to manage complexity. The system separates concerns by implementing independent components for receiving deployment requests, determining application requirements, evaluating cloud resource inventory, matching requirements with resources, selecting optimization strategies, and provisioning resources. This modular segmentation allows each component to handle specific tasks independently, making the overall complex system more manageable, maintainable, and scalable while preserving deployment efficiency benefits.
Data Source
AI summary
A system is provided to deploy a computing application on a cloud using optimization criteria. The system comprises a launch request detector to detect a request to launch a computing application within a virtualization service, an application requirement detector to determine application requirement for the computing application, a cloud resource inventory detector to determine cloud resource inventory, an optimization requirement detector, a matching module, and a configuration module. The optimization requirement detector may be configured to determine an optimization requirement. The matching module may be configured to perform matching of the application requirement with the resource inventory based on the optimization requirement. The configuration module may be configured to generate an optimized application deployment configuration based on the result of the matching and cause launching of the computing application within the virtualization service using the optimized application deployment configuration.