Cloud Deployment Automation for Cost-Aware Resource Optimization
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Solution Overview
Problem
Cloud computing environments face high maintenance and operational costs due to overprovisioning of resources, leading to inefficiencies and increased expenses, while balancing scalability and cost-efficiency remains a challenge.
Innovation Solution
A system and method for optimizing resource utilization in cloud environments by inspecting and generating a representation of the environment, determining resource costs, and initiating optimization actions to reduce costs, including deprovisioning underutilized resources and updating inefficient software components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are overprovisioned to ensure reliability and performance, then service reliability is improved, but operational costs increase
Solution Approach 1:
The system dynamically adjusts resource allocation based on real-time usage patterns and predictive analytics. Instead of static overprovisioning, the system continuously optimizes resource distribution to match actual demand, ensuring reliability while minimizing wasted capacity costs.
Solution Approach 2:
The system implements feedback loops that monitor resource usage, service performance, and cost metrics. This feedback enables the system to learn from actual usage patterns and adjust resource allocation automatically, resolving the contradiction between maintaining reliability and reducing operational costs.
2Adaptability or versatility
If more computing power and storage are allocated to mitigate resource shortages, then service scalability is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The system uses predictive analytics to forecast future resource needs before shortages occur. By anticipating demand patterns, the system can provision resources in advance in a targeted manner, ensuring scalability is ready when needed without allocating excessive resources to areas with low demand.
Solution Approach 2:
The system applies differentiated resource allocation strategies to different services and departments based on their specific usage patterns and growth requirements. This localized approach ensures each area receives appropriate resource levels tailored to their actual needs rather than uniform overprovisioning.
3Reliability
If resources are allocated to ensure adequate capacity, then service availability is improved, but cost-efficiency deteriorates
Solution Approach 1:
The system changes resource allocation parameters dynamically based on usage patterns, service priorities, and cost metrics. By continuously adjusting parameters such as resource quotas, allocation ratios, and provisioning thresholds, the system optimizes the balance between service availability and cost-efficiency.
Data Source
AI summary
A system and method for optimizing the deployment of a cloud computing environment is presented. The method includes: inspecting the cloud computing environment; generating a representation of the cloud computing environment based on a result of inspecting the cloud computing environment; determining a resource cost associated with each entity of a plurality of entities represented in the generated representation of the cloud computing environment; generating an optimization action for an entity of the plurality of entities, wherein the optimization action reduces the determined resource cost; and initiating the optimization action in the cloud computing environment.