Self-adjusting Framework for Cloud Deployment Capacity
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Solution Overview
Problem
Cloud computing environments face challenges in dynamically adjusting to changing loads and costs due to the on-demand nature of virtual machine geometry, geographical location, and quality of service requirements, leading to inefficiencies in capacity planning and performance optimization.
Innovation Solution
A method is implemented in a live cloud computing environment to automatically adjust operating parameters of deployment components based on anticipated load changes and observed effects, using behavior models to optimize resource allocation and minimize costs while meeting service level agreements (SLAs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If operating parameters are adjusted manually to optimize resource allocation, then resource utilization improves, but response time to load changes increases and operational complexity increases
Solution Approach 1:
The system enables self-service through automated parameter adjustment mechanisms that monitor load conditions and autonomously modify operating parameters without human intervention. The framework includes self-diagnosis capabilities that detect load changes and trigger appropriate parameter adjustments, allowing the system to optimize its own resource allocation in real-time.
Solution Approach 2:
The system implements feedback loops where operating parameters are continuously monitored, evaluated against performance targets, and automatically adjusted based on observed outcomes. This closed-loop control enables rapid response to load changes by using real-time performance data to drive parameter optimization, eliminating manual adjustment delays.
2Reliability
If more resources are allocated to handle peak loads, then service level agreement compliance improves, but cost increases
Solution Approach 1:
The system applies dynamics by making resource allocation flexible and adaptive rather than static. Operating parameters such as compute capacity, memory allocation, and network bandwidth are dynamically adjusted based on real-time load conditions and predictive analytics. This allows the system to scale resources up during peak loads and scale down during low-utilization periods, maintaining SLA compliance while optimizing resource quantity.
Solution Approach 2:
The framework utilizes parameter changes to optimize resource allocation by modifying operational parameters such as virtual machine geometry, geographical location, and quality of service settings. These parameter adjustments enable the system to achieve better SLA compliance with optimized resource quantities by finding the optimal configuration for different load scenarios.
3Measurement precision
If behavior models are created through extensive experimentation, then optimization accuracy improves, but time and computational resources required increase
Solution Approach 1:
The system applies preliminary action by pre-computing behavior models during off-peak periods or using historical data to establish baseline models before actual operation. This preliminary modeling reduces the need for extensive real-time experimentation while maintaining optimization accuracy, as the pre-established models can be quickly adapted to current conditions without requiring lengthy development cycles.
Data Source
AI summary
Automatically improving a deployment. A method includes, in a live distributed computing environment, adjusting operating parameters of deployment components. Effects of the adjusted operating parameters are observed. At least a portion of a behavior model function is defined based on the adjusted operating parameters and observed effects. Based on current distributed computing environmental conditions, operating parameters defined in the behavior model function are adjusted to improve the deployment.