Dynamic Cloud Resource Allocation via Predictive Capacity Modeling
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Solution Overview
Problem
Current cloud-based application delivery platforms use static allocations of cloud resources, failing to account for fluctuations in computing processing resource and network bandwidth consumption during application updates, leading to suboptimal performance and potential stoppages.
Innovation Solution
A resource capacity model is developed to dynamically allocate cloud resources based on runtime data from previous deployment environments, using linear regression and auto-regression techniques to predict resource usage, and a proxy server controls network traffic to optimize resource allocation across deployment groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static allocation of cloud resources is used, then device complexity is reduced and ease of operation is improved, but resource utilization efficiency deteriorates and deployment performance degrades
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring runtime metrics (CPU utilization, memory usage, network bandwidth) during application deployments and automatically adjusting cloud resource allocation based on observed patterns. This transforms the static allocation system into a dynamic one that adapts to actual resource consumption, resolving the contradiction between operational simplicity and deployment performance.
2Device complexity
If resource allocation does not account for runtime variability, then device complexity is reduced, but resource utilization efficiency and deployment reliability deteriorate
Solution Approach 1:
The system implements feedback mechanisms by monitoring runtime resource consumption metrics during deployments and using this information to adjust resource allocation in subsequent deployments. The system collects data on CPU usage, memory consumption, and network bandwidth utilization, then feeds this information back to the resource allocation algorithm to improve reliability while managing complexity through automated control loops.
Solution Approach 2:
The resource allocation system performs self-adjustment by automatically analyzing its own runtime performance data and making decisions about resource allocation without external intervention. The system monitors its own resource consumption patterns and autonomously optimizes allocation parameters, reducing the need for complex manual configuration while improving deployment reliability.
3Ease of operation
If cloud resources are pre-allocated without considering utilization patterns, then ease of operation is improved, but resource utilization efficiency and cost effectiveness deteriorate
Solution Approach 1:
The system automatically analyzes runtime resource utilization patterns and adjusts allocation accordingly without requiring manual optimization. By monitoring actual resource consumption during deployments and using statistical analysis to predict future needs, the system self-optimizes resource allocation to eliminate waste while maintaining operational simplicity through automated decision-making.
Data Source
AI summary
Described embodiments provide systems and methods of allocating cloud resources for application deployments. A resource allocator may identify a first metric indicating usage of cloud resources by clients in a first release environment for an application update. The resource allocator may generate, using the first metric, a resource capacity model for predicting usage of the cloud resources by clients in a second release environment for the application update. The resource allocator may determine, using the resource capacity model, a metric predicting the usage of the cloud resources by the clients in the second release environment. The resource allocator may generate instructions to set an allocation of the cloud computing resources for performing deployment of the application update to the second release environment based on the second metric.