Cloud Job Scheduling via Warmup Container Conversion
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Solution Overview
Problem
Existing data integration solutions in cloud computing environments face challenges in efficiently scheduling jobs and scaling resources, leading to increased costs and delays due to inefficient resource utilization and prioritization.
Innovation Solution
The method involves analyzing job-level, stage-level, and task-level resource requirements to proactively scale cloud resources by creating 'warmup' containers with lower priority, which can be converted to worker containers as needed, ensuring efficient execution and reducing waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If cloud resources are scaled up proactively to meet desired execution time, then job execution time is reduced, but resource waste and costs increase due to over-provisioning
Solution Approach 1:
The system performs preliminary actions by creating warmup containers before the actual job execution. These warmup containers are prepared in advance with lower priority and are converted to worker containers when needed, allowing the system to meet desired execution time without over-provisioning resources. The executor requests warmup containers proactively based on historical data and environmental configuration, enabling timely resource availability while avoiding excessive resource allocation.
2Productivity
If more worker containers are created to execute jobs faster, then productivity increases, but device complexity increases due to dynamic resource management
Solution Approach 1:
The system introduces dynamic resource management through the warmup container mechanism. Containers dynamically transition from warmup state to worker state based on job execution needs. The priority of containers is dynamically adjusted, with warmup containers having lower priority than active worker containers. This dynamic approach enables the system to scale resources up when needed while maintaining manageable complexity through automated executor control.
3Productivity
If resources are allocated based on historical data and metadata analysis, then resource allocation efficiency improves, but measurement precision requirements increase
Solution Approach 1:
The system implements feedback mechanisms by requesting historical data from databases based on metadata associated with job stages and environmental configuration. The executor uses this historical information to determine resource requirements and execution times, creating a feedback loop that improves resource allocation efficiency. The feedback from historical executions allows the system to learn from past performance and make more accurate resource requests.
Data Source
AI summary
A method, apparatus, and non-transitory computer-readable medium for scheduling a job on a cluster, including receiving a job comprising one or more stages, each stage comprising one or more tasks, requesting historical data based on metadata associated with each of the one or more stages of the job and environmental configuration data of the cluster, determining a resource requirement of each stage of a plurality of stage groups wherein a stage group comprises one or more stages, scheduling the first stage group on the cluster, requesting a one or more new worker containers on the cluster for execution of a first subsequent stage group to be executed after the first stage group, and scheduling the first subsequent stage group on the cluster based at least in part on the completion of execution of the first stage group.