Reinforcement Learning Resource Allocation
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Solution Overview
Problem
Computer networks face inefficiencies in resource allocation, where nodes are often provided with predetermined resources regardless of current need, leading to idle resources and unawareness of incurred costs for users paying for unused resources.
Innovation Solution
A method using a reinforcement learning model to collect and analyze utilization data, setting optimal resource prices, and allocating computing resources based on these prices to maximize utilization and align with user budgets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predetermined resources are allocated to each node, then resource availability is guaranteed, but resource utilization efficiency deteriorates due to idle resources
Solution Approach 1:
The patent implements dynamic resource allocation where the resource allocation module continuously adjusts resource distribution based on real-time utilization data and pricing information. Instead of static predetermined allocation, the system adapts resource assignments to current network conditions, user needs, and pricing signals, thereby reducing idle resources while maintaining availability where needed.
Solution Approach 2:
The system employs feedback mechanisms where utilization data from network nodes is collected and fed back to the resource allocation module. This feedback loop enables the system to learn from actual resource usage patterns and adjust future allocations accordingly, preventing both over-allocation (idle resources) and under-allocation (insufficient availability).
2Loss of energy
If resources are allocated in exchange for payment, then resource value is recognized, but user awareness of incurred costs deteriorates when users are unaware of costs for idle resources
Solution Approach 1:
The pricing agent provides continuous feedback to users about their resource consumption and associated costs. By delivering utilization recommendations that include cost information, the system keeps users informed about their spending, enabling them to make conscious decisions about resource usage and avoid unknowingly paying for idle resources.
Solution Approach 2:
The pricing agent acts as an intermediary between the resource allocation system and users. It translates complex resource utilization data into understandable pricing information and cost alerts, bridging the gap between the allocation mechanism and user awareness, thereby improving cost transparency without complicating the allocation process.
Data Source
AI summary
Systems and methods for resource allocation are described. The systems and methods include receiving utilization data for computing resources shared by a plurality of users, updating a pricing agent using a reinforcement learning model based on the utilization data, identifying resource pricing information using the pricing agent, and allocating the computing resources to the plurality of users based on the resource pricing information.