Virtualized Environment Cluster Utilization for Compute Jobs
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Solution Overview
Problem
The initialization of virtualized environments in cloud computing is time-consuming and reduces overall resource utilization, as it requires significant processing cycles and resources, especially for compute-intensive tasks like machine learning and deep learning, where each application may need different configurations.
Innovation Solution
The method involves performing entity extraction on job description files to create clusters based on compute resource requirements, allowing for the reuse of previously initialized virtualized environments for similar queued jobs, thereby reducing the overhead of repeated initialization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If virtualized environments are initialized for each compute job, then compute jobs can be executed with dedicated configurations, but the initialization process consumes significant time and processing resources
Solution Approach 1:
The system performs preliminary actions by pre-initializing virtualized environments based on extracted job entities and requirements before actual compute jobs are submitted. This allows environments to be ready in advance, eliminating initialization delays when jobs need to execute.
Solution Approach 2:
The system performs preliminary actions by pre-initializing virtualized environments based on extracted job entities and requirements before actual compute jobs are submitted. This allows environments to be ready in advance, eliminating initialization delays when jobs need to execute.
2Adaptability or versatility
If virtualized environments are initialized for each compute job, then each job can have its specific configuration, but processing cycles and resources are significantly consumed during initialization
Solution Approach 1:
The system creates universal virtualized environments that can serve multiple compute jobs with similar requirements. By extracting common entities from job descriptions and creating standardized environments, the system reduces redundant initialization while maintaining the ability to accommodate different job configurations through parameter variations.
Solution Approach 2:
The system utilizes parameter changes by adjusting environment configurations based on extracted job entities such as compute resources, storage requirements, and network settings. This allows a single environment template to be adapted for different jobs by modifying parameters rather than creating entirely new environments each time.
3Productivity
If virtualized environments are reused for queued compute jobs, then resource efficiency improves, but the system complexity increases due to cluster management
Solution Approach 1:
The system applies segmentation by dividing compute jobs into clusters based on their entity requirements and characteristics. This organization allows efficient matching of jobs to appropriate virtualized environments while maintaining manageable cluster sizes and reducing the complexity of tracking individual job-environment mappings.
Solution Approach 2:
The system implements feedback mechanisms by monitoring environment usage, job completion status, and resource availability. This feedback enables automatic decisions about when to reuse environments, when to create new ones, and how to manage cluster allocations, reducing manual management complexity while improving resource efficiency.
Data Source
AI summary
Disclosed embodiments provide techniques for compute job allocation in a virtualized computing environment. A first list of compute jobs that are currently executing in a virtualized environment is obtained. For each job in the first list, a job description file is obtained. An entity extraction process is performed on the job description file to extract a plurality of job entities. Multiple clusters are created that correspond to the compute jobs in the first list. A second list of compute jobs that are currently queued for execution is obtained. Compute jobs in the second list are assigned to a cluster from the plurality of clusters, and the virtualized environment is reused for execution of a compute job from the second list based on the assigned cluster.