Heterogeneous Cluster Task Scheduling via Inferred Resource Profiles
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Solution Overview
Problem
Map-reduce frameworks like HADOOP struggle to efficiently schedule tasks in heterogeneous clusters, failing to match jobs with the best compute nodes due to the assumption of homogeneous clusters, which compromises throughput and delays, and do not effectively utilize available resources to meet service level requirements.
Innovation Solution
A system and method that uses an active machine learning approach to infer job resource requirements by analyzing execution times on nodes with differing capabilities, employing Bayesian experimental design to optimize task scheduling and resource allocation, ensuring tasks are executed on nodes best suited for their needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If map-reduce frameworks assume homogeneous clusters and assign tasks regardless of node capabilities, then the scheduling system is simple to implement, but throughput and resource utilization deteriorate in heterogeneous clusters
Solution Approach 1:
The system changes the scheduling parameters from uniform task assignment to capability-aware assignment. By inferring job resource profiles and matching them with node capability profiles, the scheduler optimizes task placement based on inferred parameters rather than assuming homogeneous conditions, thereby improving throughput in heterogeneous clusters
Solution Approach 2:
The system uses feedback from timing information of executed tasks to infer job resource profiles. This feedback loop allows the scheduler to learn about job characteristics from actual execution data and use this information for better scheduling decisions, resolving the contradiction between simple implementation and high productivity
2Loss of time
If tasks are scheduled without inferring job resource profiles, then the scheduling process is fast and simple, but resource utilization and service level requirements deteriorate
Solution Approach 1:
The system performs preliminary action by inferring job resource profiles before final task scheduling. By analyzing timing information from a subset of executed tasks to characterize the job's resource requirements, the system prepares scheduling decisions in advance, improving resource utilization without significantly increasing total scheduling time
Solution Approach 2:
The system applies partial action by inferring resource profiles from only the necessary timing information of executed tasks rather than requiring complete job characterization. This partial inference approach provides sufficient accuracy for optimal scheduling while minimizing the time and computational resources consumed during the scheduling process
3Ease of operation
If heterogeneous clusters are not accounted for in task scheduling, then the framework remains simple and easy to operate, but global metrics such as maximum delay and resource allocation deteriorate
Solution Approach 1:
The system implements self-service by automatically inferring job resource profiles from timing information without requiring manual configuration or user input about job characteristics. The framework autonomously characterizes jobs and makes optimal scheduling decisions, maintaining ease of operation while improving reliability and service level compliance in heterogeneous environments
Data Source
AI summary
A system and method schedules jobs in a cluster of compute nodes. A job with an unknown resource requirement profile is received. The job includes a plurality of tasks. Execution of some of the plurality of tasks is scheduled on compute nodes of the cluster with differing capability profiles. Timing information regarding execution time of the scheduled tasks is received. A resource requirement profile for the job is inferred based on the received timing information and the differing capability profiles. Execution of remaining tasks of the job is scheduled on the compute nodes of the cluster using the resource requirement profile.