RL Buffer Egress Control for Dynamic Resource Allocation
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Solution Overview
Problem
Conventional dynamic resource allocation (DRA) algorithms in communication networks fail to dynamically provision resources based on predicted traffic patterns, leading to suboptimal performance and difficulty in tuning for objectives like latency and power consumption.
Innovation Solution
The implementation of reinforcement learning (RL) to dynamically control buffer queue egress rates by adapting the state-to-action mapping function, circumventing explicit exploration phases and relying on extrapolation of actions, thereby optimizing resource allocation without random or suboptimal actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reinforcement learning exploration phase is implemented to learn environment dynamics, then learning accuracy is improved, but performance penalty occurs during exploration
Solution Approach 1:
The patent applies preliminary action by pre-training the reinforcement learning model offline using simulated traffic patterns before deployment. This allows the model to learn environment dynamics and optimal resource allocation strategies in advance, eliminating the need for online exploration that would cause performance penalties. The pre-trained model can then be directly applied to real-time DRA without sacrificing productivity.
Solution Approach 2:
The patent uses copying by creating a simulated environment that replicates real network traffic patterns and resource allocation scenarios. The RL model is trained in this copied virtual environment, allowing it to learn from synthetic data without affecting actual network performance. Once trained, the model transfers its learned policies to the real system, achieving both learning accuracy and maintained performance.
2Adaptability or versatility
If conventional DRA algorithms are used to manage buffer queues, then system complexity is kept low, but adaptability to traffic patterns is reduced
Solution Approach 1:
The patent replaces conventional rule-based DRA algorithms with a reinforcement learning-based intelligent system. Instead of using fixed mechanical rules for resource allocation, the system employs an RL agent that learns optimal policies through interaction with the environment. This substitution enables the system to adapt to varying traffic patterns while managing complexity through efficient RL implementation and pre-training approaches.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting egress rates of buffer queues based on learned traffic patterns. The RL model continuously monitors system state and modifies allocation parameters in response to changing conditions, enabling adaptability. The complexity is managed by focusing parameter adjustments on key resource allocation decisions rather than overhauling the entire system architecture.
Data Source
AI summary
A communication system in which DRA control is aided by RL. An example embodiment may control one or more buffer queues populated by downstream and/or upstream data streams. The egress rates of the buffer queues can be dynamically controlled using an RL technique, according to which a learning agent can adaptively change the state-to-action mapping function of the DRA controller while circumventing the RL exploration phase and relying on extrapolation of the already taken actions instead. This feature may result in at least two benefits: (i) cancellation of a performance penalty typically associated with RL exploration; and (ii) faster learning of the environment, as the learning agent can determine the performance metrics of many actions per state in a single occurrence of the state. In an example embodiment, the communication system may be a DSL system, a PON system, or a wireless communication system.