Real-time Compute Infrastructure Optimization via Job Metadata Analysis
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Solution Overview
Problem
Current virtualized compute infrastructure in cloud environments struggles to optimize resource allocation in real-time based on specific job requirements, often leading to inefficiencies, errors, and increased costs due to fixed hardware configurations and predictive auto-scaling methods that fail to account for diverse job needs.
Innovation Solution
A system with a requirements engine that processes job metadata to dynamically optimize virtual machine configurations in real-time, provisioning or terminating resources based on job-specific requirements, using logical expressions to match jobs with optimal virtual machine configurations and prioritizing cost-effectiveness and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If predictive auto-scaling based on historical usage is used, then resource provisioning is automated, but it ignores specific job requirements leading to suboptimal VM configurations
Solution Approach 1:
The system dynamically determines VM configurations by analyzing job metadata and requirements in real-time, rather than using static predictive models. The requirements engine continuously evaluates job characteristics and provisions appropriate VM types based on current job demands, making the auto-scaling system adaptive to specific job requirements.
Solution Approach 2:
The system changes the parameters used for auto-scaling decisions from historical usage patterns to job-specific metadata and requirements. By parsing job metadata and evaluating requirements against available VM configurations, the system selects optimal VM parameters (CPU, memory, storage) based on actual job needs rather than historical averages.
2Adaptability or versatility
If a single VM configuration capable of running any job is used, then platform compatibility is ensured, but excess resources are wasted on jobs with lower requirements
Solution Approach 1:
Instead of using a uniform VM configuration for all jobs, the system applies local quality by matching specific VM configurations to specific job requirements. Each job receives a VM configuration tailored to its particular needs based on metadata analysis, ensuring that resources are allocated according to the actual requirements of each individual job rather than using a one-size-fits-all approach.
Solution Approach 2:
The system segments the pool of available VM configurations into different types based on resource characteristics. By categorizing VMs into different configuration types and matching jobs to appropriate segments, the system avoids provisioning excessive resources to jobs that don't require them, while still maintaining the capability to handle diverse job types.
3Ease of manufacture
If VM configurations are determined ahead of time, then provisioning is simplified, but jobs cannot be optimized for their specific requirements
Solution Approach 1:
The system enables self-service by automatically analyzing job metadata and requirements to determine appropriate VM configurations without manual intervention. The requirements engine autonomously evaluates job characteristics, compares them against available VM configurations, and provisions the optimal configuration, maintaining provisioning simplicity while achieving job-specific optimization.
Solution Approach 2:
The system performs preliminary analysis of job metadata and requirements before VM provisioning occurs. By evaluating job characteristics in advance and determining the optimal configuration beforehand, the system ensures that jobs are assigned to appropriately configured VMs from the start, improving execution efficiency without complicating the provisioning process.
4Quantity of substance
If fixed hardware configurations are used, then infrastructure cost is reduced, but jobs may experience errors due to incompatibility or underutilization
Solution Approach 1:
The system changes the approach from fixed hardware configurations to dynamic parameter selection based on job requirements. By analyzing job metadata and adjusting VM parameters (CPU, memory, storage, network) to match specific job needs, the system ensures compatibility and optimal performance for each job while maintaining efficient resource utilization across the infrastructure.
Solution Approach 2:
The system introduces dynamics to the hardware configuration selection process by continuously evaluating job requirements and provisioning appropriate VM configurations. This dynamic approach ensures that each job runs on hardware that is compatible and optimally suited to its requirements, reducing execution errors while maintaining efficient resource usage through real-time adaptation.
Data Source
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AI summary
A system and method is provided to dynamically optimize the topography of a compute cluster in real time based on the runtime configuration, specified as metadata, of jobs in a queuing or scheduling environment. The system provisions or terminates multiple types of virtualized resources based on the profile of all applications of the jobs in a current queue based on their aggregate runtime configuration specified as requirements and rank expressions within associated metadata. The system will continually request and terminate compute resources as jobs enter and exit the queue, keeping the cluster as minimal as possible while still being optimized to complete all jobs in the queue, optimized for cost, runtime or other metric. End users can specify the runtime requirements of jobs, thereby preventing the user from having to know about the physical profile of the compute cluster to specifically architect their jobs.