Multi-tier Application Scaling via Reinforcement Learning
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Solution Overview
Problem
Existing virtual data centers face challenges in scaling multi-tier applications effectively due to complex resource dependencies and static resource usage thresholds, which are insufficient for handling dynamic workloads.
Innovation Solution
A module and method that automatically scale multi-tier applications by selecting between reinforced learning and heuristic operations based on operational metrics, using a selector to recommend scaling actions, with reinforced learning applying to select from possible actions and heuristic operations using defined heuristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static resource usage thresholds are used for scaling, then the scaling decision process is simple, but the system cannot adapt to dynamic workloads and complex multi-tier dependencies
Solution Approach 1:
The patent implements dynamic threshold adjustment by using reinforcement learning to continuously learn and adapt resource usage thresholds based on changing workload patterns and multi-tier application states. Instead of fixed thresholds, the system dynamically modifies thresholds in real-time to match actual system conditions, resolving the contradiction between simplicity and adaptability.
Solution Approach 2:
The system employs feedback mechanisms where the reinforcement learning agent continuously monitors scaling decisions, their outcomes, and system performance. This feedback loop allows the system to learn from past decisions and improve future scaling actions, enabling adaptation to dynamic workloads while managing complexity through iterative learning rather than complex rule-based systems.
2Productivity
If reinforcement learning is used to select scaling actions, then the system can adapt to complex multi-tier dependencies, but the computational overhead and training time increase
Solution Approach 1:
The patent applies preliminary action by pre-training the reinforcement learning model offline using historical workload data and multi-tier application patterns. This pre-training phase prepares the model in advance, so that during actual operation, the system can make scaling decisions quickly without extensive real-time computation, thus reducing operational training time while maintaining high scaling efficiency.
Solution Approach 2:
The system uses partial action by implementing a hybrid approach where reinforcement learning handles complex multi-tier scaling decisions while simpler heuristic rules manage routine scaling scenarios. This partial application of reinforcement learning reduces overall computational overhead and training requirements while still achieving high productivity for complex scaling situations.
3Ease of operation
If virtual machine resource usage thresholds are set by user, then the scaling logic is straightforward, but the system cannot handle complicated multi-tier dependencies
Solution Approach 1:
The patent implements self-service by enabling the system to automatically configure and adjust scaling thresholds for multi-tier applications without requiring detailed user input. The reinforcement learning agent autonomously learns the complex dependencies between different tiers and resources, automatically determining appropriate scaling parameters. This maintains ease of operation for users while achieving high adaptability for complex multi-tier scenarios.
Data Source
AI summary
A module and method for automatically scaling a multi-tier application, wherein each tier of the multi-tier application is supported by at least one virtual machine, selects one of reinforced learning and heuristic operation based on a policy to recommend a scaling action from a current state of the multi-tier application. If reinforced learning is selected, the reinforced learning is applied to select the scaling action from a plurality of possible actions for the multi-tier application in the current state. If heuristic operation is selected, the heuristic operation is applied to select the scaling action using a plurality of defined heuristics.