Automated DevOps Pipeline Model for Cloud Cost Optimization
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Solution Overview
Problem
Traditional approaches to application development and IT operations often result in long delays between releases due to separate development and operations teams, leading to operational roadblocks and inefficiencies in the deployment process.
Innovation Solution
The implementation of an automated DevOps deployment pipeline that generates a deployment pipeline model based on policies for each stage and task, optimized for cost using virtual machines on cloud computing infrastructure, facilitating collaboration and reducing delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional separate development and operations teams are used, then specialization is improved, but release delay increases
Solution Approach 1:
The patent merges previously separate development and operations teams into a unified DevOps organization. This consolidation eliminates silos and enables continuous collaboration between developers and operations personnel, thereby reducing release delays while maintaining necessary functional specialization through role-based responsibilities within the integrated team structure.
Solution Approach 2:
The patent implements automated infrastructure provisioning and pre-configured deployment environments that are prepared in advance. By establishing standardized templates, policies, and automated workflows before deployment needs arise, the system eliminates waiting time for environment setup and reduces overall release delays while maintaining operational rigor.
2Ease of operation
If manual deployment processes are used, then control is improved, but productivity decreases
Solution Approach 1:
The patent implements self-service automated deployment systems where the infrastructure and deployment processes provision and execute themselves based on predefined policies and triggers. This automation maintains control through enforced compliance with operational standards while dramatically increasing deployment speed by eliminating manual intervention bottlenecks.
Solution Approach 2:
The patent incorporates continuous monitoring and feedback mechanisms that track deployment status, resource utilization, and policy compliance in real-time. This feedback loop enables automated adjustments and maintains operational control while accelerating deployment processes through dynamic response to system state changes.
3Adaptability or versatility
If cloud computing infrastructure is used, then scalability is improved, but cost increases
Solution Approach 1:
The patent implements dynamic resource allocation that automatically adjusts cloud infrastructure provisioning based on real-time demand, workload characteristics, and policy constraints. This dynamic approach enables scalability when needed while optimizing cost by deallocating or downscaling resources during low-utilization periods, preventing wasteful spending on permanently allocated capacity.
Solution Approach 2:
The patent utilizes parameter changes in resource provisioning by adjusting virtual machine configurations, storage allocation, and network resources based on actual deployment needs and policy parameters. This allows the system to scale efficiently while controlling costs by matching resource parameters precisely to operational requirements rather than over-provisioning.
Data Source
AI summary
Methods and systems that automate a DevOps deployment pipeline and optimize DevOps cost are described. Methods generate a deployment pipeline model based on policies associated with each deployment stage and task. Methods optimize cost of the deployment pipeline model based on model combinations of VMs. The deployment pipeline model may be executed on a cloud computing infrastructure in order to develop an application program.