Adaptive Edge Resource Allocation With Actor-Critic Feedback
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Solution Overview
Problem
Edge computing environments face challenges such as limited computing resources, scalability issues, and unexpected workload spikes leading to resource overfitting or underfitting, which can result in shortages for critical tasks.
Innovation Solution
Implementing a machine learning framework with a first model to predict resource states and a second model, comprising an actor-critic architecture, to dynamically adjust resource allocations based on usage patterns and feedback, using deep reinforcement learning to optimize resource distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resources are allocated statically in edge computing environments, then device complexity is reduced and ease of operation is improved, but resource utilization efficiency deteriorates and productivity decreases due to overfitting or underfitting during workload spikes
Solution Approach 1:
The system employs machine learning models that automatically detect and improve resource allocations without human intervention. The models process usage data, determine service states, and assign new resource allocations autonomously, allowing the system to self-optimize resource distribution in response to workload changes.
Solution Approach 2:
The second machine learning model includes a feedback component that continuously evaluates predicted resource allocations and updates the prediction component based on evaluation results. This closed-loop feedback mechanism enables the system to learn from past allocations and improve future resource distribution decisions, resolving the contradiction between automation complexity and resource utilization efficiency.
2Productivity
If machine learning models are used to dynamically adjust resource allocations, then resource utilization and productivity are improved, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent introduces usage data as an intermediary that bridges the gap between service execution and resource allocation decisions. The machine learning models process this intermediary data to determine service states and generate resource allocation recommendations, making the complex evaluation process more manageable and measurable through standardized data inputs and outputs.
3Reliability
If resource allocations are adjusted to handle unexpected workload spikes, then reliability is improved, but loss of time increases due to overfitting or underfitting resources for critical tasks
Solution Approach 1:
The system performs preliminary actions by continuously processing usage data and determining service states before workload spikes occur. The machine learning models are trained to predict resource allocation needs in advance, allowing the system to proactively adjust resources for critical tasks before performance degradation occurs, rather than reacting after problems arise.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for adaptive resource allocation are provided herein. An example method includes obtaining usage data usage data relating to execution of a set of services and processing the usage data with a first machine learning model to determine one or more states corresponding to respective services in the set, where the first machine learning model evaluates one or more resource allocations for one or more services in the set based on the usage data. The method includes assigning a new resource allocation to at least one service in the set using a second machine learning model. The second machine learning model includes a prediction component that predicts the new resource allocation based on the determined one or more states corresponding to the at least one service, and a feedback component that updates the prediction component based on an evaluation of the new resource allocation.