Deep Learning Resource Scheduling via Prediction Model
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Solution Overview
Problem
Existing deep learning task resource scheduling methods require users to manually specify resources, leading to inefficient allocation due to either insufficient or excessive resource allocation, which can result in long waiting times or resource waste, especially given the varying demands of deep learning tasks based on completion time and processing complexity.
Innovation Solution
A method and apparatus for resource scheduling that determines the required resources for deep learning tasks based on user-specified processing requirements, including completion time, using a resource prediction model to allocate dedicated and general processing resources and storage, ensuring efficient resource utilization and cost optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually specify resources for deep learning tasks, then users have control over resource allocation, but resource allocation efficiency deteriorates due to insufficient or excessive allocation
Solution Approach 1:
The patent introduces an automated resource scheduling system that acts as an intermediary between users and computing resources. This system uses machine learning models to predict optimal resource requirements based on task characteristics, historical data, and current system state, thereby resolving the contradiction by providing both user convenience and efficient allocation through intelligent mediation
Solution Approach 2:
The system enables self-service resource allocation by allowing the scheduling system to automatically determine resource requirements without manual user intervention. The system learns from historical task performance and current conditions to autonomously allocate resources, improving efficiency while maintaining user control through configurable policies and constraints
2Speed
If more resources are allocated to deep learning tasks, then processing speed improves, but resource waste increases due to excessive allocation
Solution Approach 1:
The patent applies partial action by allocating resources dynamically based on actual task needs rather than providing full resources upfront. The system monitors task progress and adjusts resource allocation in real-time, providing additional resources only when and where needed, thus avoiding the energy waste of continuously allocating excessive resources
Solution Approach 2:
The system implements dynamic resource allocation where resource assignments are not fixed but adjust continuously based on task requirements, system load, and performance metrics. This dynamic approach allows the system to optimize the balance between processing speed and resource utilization, allocating more resources when speed is critical and fewer when tasks can proceed at lower intensity
3Loss of energy
If fewer resources are allocated to deep learning tasks, then resource waste decreases, but waiting time increases due to insufficient allocation
Solution Approach 1:
The system performs preliminary actions by pre-allocation and pre-positioning of resources based on predicted task requirements. Using historical data and task characteristics, the system anticipates resource needs and prepares allocations in advance, reducing waiting time while avoiding the waste of allocating resources that turn out to be unnecessary
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors task progress, resource utilization, and performance metrics. This feedback loop allows the system to adjust resource allocation in real-time, adding resources when tasks are falling behind schedule and reducing allocation when tasks are completing ahead of time, thus balancing waiting time and resource waste
4Productivity
If automated resource scheduling is implemented, then resource allocation efficiency improves, but system complexity increases
Solution Approach 1:
The patent applies universality by designing a multi-functional scheduling system that handles diverse deep learning workloads, resource types, and allocation scenarios through a unified framework. This universal system manages multiple functions including prediction, optimization, monitoring, and adjustment within a single coherent architecture, improving efficiency without proportionally increasing complexity through modular design
Data Source
AI summary
Embodiments of the present disclosure provide a method, apparatus and computer program product for resource scheduling. The method comprises obtaining a processing requirement for a deep learning task, the processing requirement being specified by a user and at least including a requirement related to a completion time of the deep learning task. The method further comprises determining, based on the processing requirement, a resource required by the deep learning task such that processing of the deep learning task based on the resource satisfies the processing requirement. Through the embodiments of the present disclosure, the resources can be scheduled reasonably and flexibly to satisfy the user's processing requirement for a particular deep learning task without requiring the user to manually specify the requirement on the resources.