Deep Learning Scheduler for Distributed Application Distribution
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Solution Overview
Problem
Optimizing the scheduling of distributed applications is challenging due to real-world variability and uncertainty from sources like network and storage latencies, bandwidth availability, and competing priorities, which affects resource utilization and efficiency.
Innovation Solution
A deep learning architecture, specifically using recurrent neural networks like LSTM, is employed to monitor and predict network characteristics, enabling a scheduler to make informed decisions about task decomposition, unit selection, and timing based on predictive data, allowing for efficient resource allocation and speculative execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional scheduling methods are used for distributed applications, then implementation is simple, but scheduling efficiency deteriorates due to real-world variability and uncertainty
Solution Approach 1:
A deep learning model is introduced as an intermediary between network conditions and scheduling decisions. The model takes network characteristics as input and outputs predicted scheduling outcomes, enabling the scheduler to make informed decisions without directly complex logic for handling all variability and uncertainty scenarios
Solution Approach 2:
The system performs preliminary analysis by training the deep learning model on historical network data to learn patterns of variability and uncertainty. This pre-training enables the model to predict future network conditions and their impact on scheduling, allowing the scheduler to prepare appropriate task assignments in advance rather than reacting to each change
2Productivity
If deep learning architecture is used to predict network characteristics, then scheduling efficiency improves, but computational resources increase
Solution Approach 1:
The deep learning model is trained in advance on historical network data to learn patterns and make predictions. This preliminary training phase separates the computationally intensive learning process from the actual scheduling operations, allowing the model to be deployed and reused for multiple scheduling decisions without repeating the full training process
Solution Approach 2:
Once trained, the deep learning model creates a predictive function that can be applied repeatedly to new network conditions. The model captures the complex relationships in a reusable format, allowing efficient prediction of scheduling outcomes without re-computing the full analysis for each scheduling decision
3Loss of time
If tasks are assigned based on current network state only, then response time is fast, but scheduling optimality deteriorates due to uncertainty
Solution Approach 1:
The deep learning model is pre-trained on historical network data to learn patterns of variability and uncertainty. This allows the model to predict future network conditions and their impact on task completion, enabling the scheduler to make optimal decisions in advance rather than reacting to current conditions alone
Solution Approach 2:
The system uses historical network data and task completion outcomes to train and refine the deep learning model. This feedback loop allows the model to continuously improve its predictions of network behavior and scheduling outcomes, increasing reliability over time while maintaining fast response times
Data Source
AI summary
In one embodiment, a method includes training a deep neural network using a first set of network characteristics corresponding to a first time and a second set of network characteristics corresponding to a second time, generating, using the deep neural network, a predictive set of network characteristics corresponding to a future time, and assigning a task of a distributed application to a processing unit based on the predictive set of network characteristics.