Reinforcement Learning Task Allocation for Stream Processing
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Solution Overview
Problem
Existing reinforcement learning schemes for resource allocation in stream processing tasks do not account for future information, leading to suboptimal allocation schemes and inefficiencies in processing resource utilization.
Innovation Solution
A method that predicts the operating state of dedicated processing resources when executing tasks based on initial resource states and candidate allocations, allowing for the selection of an optimized allocation scheme that considers future operating conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If reinforcement learning schemes are used for resource allocation without considering future information, then the allocation can be made quickly based on current state, but the allocation scheme becomes suboptimal and resource utilization efficiency deteriorates
Solution Approach 1:
The patent applies preliminary action by predicting future operating states before making allocation decisions. The reinforcement learning model forecasts multiple possible future states of processing resources (such as queue lengths, resource utilization levels) and uses these predictions to inform the current allocation decision, rather than reacting only to the current state. This allows the system to proactively prepare for future conditions and make more optimal allocation choices.
Solution Approach 2:
The patent implements feedback by using the predicted future operating states as feedback signals to improve current allocation decisions. The model continuously monitors actual resource states, compares them with predicted states, and uses this feedback loop to refine predictions and optimization strategies. This feedback mechanism enables the system to learn from past performance and adapt to changing conditions, improving both accuracy and resource utilization over time.
2Device complexity
If traditional reinforcement learning is used that only considers current state, then the implementation is simple, but the allocation scheme becomes suboptimal
Solution Approach 1:
The patent applies dimensionality change by extending the state space from only current state to include predicted future states. Instead of making decisions based on a single time point, the reinforcement learning model incorporates temporal dimensions by predicting and evaluating multiple future states. This transforms the problem from a static decision-making process to a dynamic one that considers the evolution of system states over time, enabling more comprehensive optimization.
Data Source
AI summary
A method in an illustrative embodiment includes: obtaining an initial state of a group of dedicated processing resources; determining, in response to a request for receiving a group of tasks, a set of candidate allocations each indicating allocation of the group of tasks to the group of dedicated processing resources; determining, based on the initial state, an operating state of the group of dedicated processing resources when executing the group of tasks according to each candidate allocation in the set of candidate allocations; and selecting a target allocation from the set of candidate allocations based on the operating state to enable the group of tasks to be executed on the group of dedicated processing resources according to the target allocation.