Workflow Job Scheduling via Resource-Based Prioritization
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Solution Overview
Problem
The increasing complexity and volume of computational workflows in cloud computing environments, particularly in bioinformatics and genomics, pose challenges in optimizing the execution and resource allocation for jobs with varying computational resource requirements, leading to inefficiencies in processing and cost management.
Innovation Solution
A method that identifies jobs ready for execution, sorts them based on resource requirements, and provisions computational instances starting with the highest priority job, using a first-fit-decreasing strategy to efficiently pack subsequent jobs onto these instances, thereby optimizing processing speed and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If computational workflows are deployed in cloud-based computing environments to accommodate increasing volume and complexity, then scalability and resource availability are improved, but efficiency of analysis and resource utilization deteriorate due to varying computational resource requirements of different jobs
Solution Approach 1:
The system performs preliminary actions by sorting jobs into a prioritized queue based on their computational resource requirements before execution. Jobs requiring more computational resources are placed higher in the queue, ensuring they are allocated and executed first. This preliminary organization optimizes resource utilization and improves analysis efficiency in cloud-based environments.
Solution Approach 2:
The system changes the parameter of job execution order by sorting jobs based on their computational resource requirements. Instead of executing jobs in arbitrary or sequential order, the system dynamically adjusts the execution sequence according to resource needs, with high-resource jobs executed first. This parameter change optimizes cloud resource utilization and improves overall analysis efficiency.
2Adaptability or versatility
If computational workflows include various work items to be run concurrently with different computational resource requirements, then versatility of processing is improved, but ability to optimally process workflows deteriorates due to resource allocation challenges
Solution Approach 1:
The system segments the computational workflow into individual jobs, each with specific computational resource requirements. By dividing the workflow into discrete, sortable units, the system can apply different prioritization strategies to different jobs based on their resource needs. This segmentation enables versatile processing while maintaining optimal resource allocation through the prioritized queue system.
Solution Approach 2:
The system changes the execution parameter by sorting jobs according to their computational resource requirements. Jobs with higher resource requirements are assigned higher priority and executed first, while jobs with lower requirements are scheduled subsequently. This dynamic parameter adjustment allows the system to handle diverse work items concurrently while optimizing overall workflow processing efficiency.
Data Source
AI summary
In one aspect, a method for scheduling jobs in a computational workflow includes identifying, from a computational workflow by a workflow execution engine executing on a processor, a plurality of jobs ready for execution. The method includes sorting, based on computational resource requirements associated with each identified job, the identified jobs into a prioritized queue. The method includes provisioning one or more computational instances based on the computational resource requirements of the identified jobs in the prioritized queue, wherein at least one computational instance is provisioned based on a highest priority job in the queue. The method includes submitting the prioritized jobs for execution to the one or more computational instances.