Adaptive HPC Workflow Allocation for Task Interference Control
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Solution Overview
Problem
Existing solutions for optimizing resource utilization and minimizing costs in cloud-based HPC workflows face challenges due to varying machine architectures, interference among tasks, and lack of real-time profiling and scheduling insights, leading to inefficient resource allocation and increased costs.
Innovation Solution
A hybrid online/offline learning framework that profiles workflow tasks during runtime, using reinforcement learning agents to adapt scheduling decisions based on real-time data and pre-trained models for known workflows, minimizing bottlenecks and idle time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tasks are packed onto available resources to maximize throughput, then resource utilization improves, but task interference increases and degrades overall performance
Solution Approach 1:
The system dynamically adjusts resource allocation and task packing density based on real-time performance monitoring. The scheduler modifies allocation decisions on-the-fly, transitioning between aggressive packing and conservative isolation based on observed interference patterns, thereby maintaining both high throughput and acceptable performance levels
Solution Approach 2:
The system implements continuous performance monitoring and feedback loops that measure task execution quality and resource utilization. This feedback informs the scheduler to adjust future allocation decisions, preventing excessive task interference while maximizing resource usage through learned patterns from offline training
2Loss of energy
If workflow tasks are characterized offline, then computational overhead is reduced, but runtime context insights are lost
Solution Approach 1:
The system performs preliminary workflow characterization offline to establish baseline performance models and expected resource requirements. This pre-processing reduces the computational burden during runtime while the system supplements this with minimal real-time monitoring to capture actual runtime context, combining both approaches to achieve efficient and accurate scheduling
Solution Approach 2:
The system adds a temporal dimension to workflow characterization by combining offline static analysis with online dynamic monitoring. This multi-dimensional approach captures both the structural properties of workflows (offline) and their actual execution behavior (online), providing comprehensive insights without excessive computational overhead
3Productivity
If compute resources are allocated to maximize throughput, then productivity improves, but infrastructure costs increase
Solution Approach 1:
The system changes the allocation parameters dynamically based on workflow characteristics and system state. By adjusting resource allocation intensity, task packing density, and isolation levels according to learned patterns from offline training and online monitoring, the system achieves high throughput for critical workflows while reducing resource consumption for less demanding tasks, thereby lowering overall infrastructure costs
Data Source
AI summary
A method includes receiving a user-submitted workflow comprising a plurality of kernels. The method further includes padding at least one kernel of the user-submitted workflow with at least one profiling tag and executing the user-submitted workflow on a compute node. The method further includes receiving at least one metric from the workflow during execution of the workflow according to the at least one profiling tag and training a reinforcement learning agent according to the at least one metric, wherein the reinforcement learning agent determines a suggested action for a particular type of kernel according to the at least one metric. The method further includes utilizing the suggested actions in making a scheduling decision for performing a task associated with an unexecuted kernel within the plurality of kernels while the user-submitted workflow continues executing, wherein the scheduling decision comprises a computing resource allocation for executing the task.