Cloud Application Vertical Scaling via Predictive Resource Allocation
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Solution Overview
Problem
Current cloud deployment systems lack mechanisms for dynamic deployment and vertical scaling of applications, leading to underutilized resources, increased costs, and performance degradation during peak loads, as they rely on static configurations and fail to optimize resource usage effectively.
Innovation Solution
A method and system that dynamically deploy applications by receiving system parameters, identifying storage access details, and estimating an ideal configuration using rule-based, neural network, or statistical models to perform vertical scaling, ensuring optimal resource allocation and utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static configuration is used for cloud deployment, then device complexity is reduced and ease of operation is improved, but resource utilization deteriorates and cost optimization is lost
Solution Approach 1:
The patent implements dynamic configuration that automatically adjusts resource allocation based on real-time workload monitoring and predictive analytics. The system transitions from static to dynamic provisioning, enabling resource scales to adapt continuously to actual application needs, thereby improving resource utilization while maintaining operational simplicity through automation.
Solution Approach 2:
The system incorporates continuous feedback loops that monitor resource usage patterns, application performance metrics, and workload characteristics. This feedback drives automatic configuration adjustments, allowing the system to learn from historical data and optimize resource allocation dynamically without manual intervention, resolving the contradiction between operational simplicity and resource efficiency.
2Adaptability or versatility
If horizontal scaling is implemented by adding/deleting units, then adaptability is improved, but system reliability deteriorates due to application downtime
Solution Approach 1:
The patent employs predictive analytics and machine learning models to forecast future resource requirements before actual demand occurs. This allows pre-provisioning of resources during low-utilization periods, enabling scaling operations to be performed in advance without causing application downtime when demand increases, thus maintaining both adaptability and reliability.
Solution Approach 2:
The system maintains buffer resources and implements gradual scaling strategies that cushion against sudden demand spikes. By provisioning additional capacity in advance and using progressive scaling approaches, the system prevents service disruptions during scaling operations, preserving application availability while maintaining scaling flexibility.
3Loss of energy
If unit size is under-provisioned to reduce cost, then loss of energy is reduced, but productivity deteriorates during peak load scenarios
Solution Approach 1:
The patent dynamically changes resource allocation parameters based on real-time workload conditions and predictive forecasts. The system adjusts CPU, memory, and storage parameters continuously, allowing under-provisioning during low-demand periods to reduce costs while automatically scaling up parameters during peak loads to maintain application performance, thus resolving the contradiction between cost optimization and productivity.
4Loss of energy
If vertical scaling is implemented dynamically, then resource utilization is improved and cost optimization is achieved, but device complexity increases
Solution Approach 1:
The patent implements self-service automation where the system automatically performs configuration analysis, resource allocation optimization, and dynamic scaling operations without human intervention. Machine learning models and automated decision-making algorithms handle the complexity of vertical scaling, allowing the system to achieve cost efficiency through dynamic resource management while keeping operational complexity hidden from users through full automation.
Data Source
AI summary
The present invention discloses method and application deployment system for dynamic deployment and vertical scaling of applications in a cloud environment. The application deployment system receives information of one or more system parameters of a target system and performs one or more processing operations on information to identify storage access details for a predefined time period of the target system. Further, an ideal configuration for the target system is estimated based on current requirement of one or more system parameters and the identified storage access details by using at least one of a rule-based model, a neural network model, and statistical model. Thereafter, the application deployment system performs dynamic deployment of the one or more applications based on the ideal configuration.