Multi-Cloud Compute Job Allocation Across Cost and Performance Targets
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Solution Overview
Problem
Existing solutions for optimizing compute jobs in multi-cloud environments focus on a single dimension and do not effectively address ad-hoc resource allocation across multiple cloud environments.
Innovation Solution
A system and method for identifying compute jobs, determining optimization targets, processing resource requirements and availability across multiple cloud networks, and orchestrating resource allocations to satisfy those targets, allowing for ad-hoc decisions on how to optimize each resource of a compute job by the appropriate optimization dimension across multiple cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing solutions are used to assess costs for different types of jobs on different cloud environments, then cost assessment capability is provided, but the solutions cannot effectively address ad-hoc resource allocation across multiple cloud environments for different optimization dimensions
Solution Approach 1:
The system segments the resource allocation problem by creating separate optimization modules for different dimensions (cost, performance, resource utilization). Each module independently evaluates cloud resources based on its specific criteria, allowing ad-hoc selection of optimization dimensions without requiring a complete system redesign for each scenario.
Solution Approach 2:
The system adds the dimension of multiple optimization criteria by evaluating cloud resources across cost, performance, and resource utilization simultaneously. This multi-dimensional evaluation framework allows users to prioritize different dimensions ad-hoc while maintaining a unified resource allocation system.
2Productivity
If existing solutions measure a single dimension on a single cloud environment, then simplicity is maintained, but optimal resource allocation across multiple cloud environments cannot be achieved
Solution Approach 1:
The system creates a universal optimization framework that can evaluate and allocate resources across multiple cloud environments for multiple optimization dimensions simultaneously. This multi-functional system handles cost optimization, performance optimization, and resource utilization optimization through a single unified platform, improving productivity without proportionally increasing complexity.
Solution Approach 2:
The system introduces an intermediary optimization layer that sits between the compute job requirements and the multiple cloud environments. This intermediary processes resource requirements, evaluates available cloud resources across different environments based on selected optimization dimensions, and orchestrates the allocation, thereby managing complexity while achieving optimal multi-cloud resource allocation.
3Ease of operation
If manual resource allocation is performed for each cloud environment, then flexibility is maintained, but time consumption and operational complexity increase
Solution Approach 1:
The system enables self-service automated resource allocation by allowing users to define their optimization preferences once, after which the system automatically evaluates cloud resources and makes allocation decisions across multiple environments. This eliminates the need for manual intervention in each allocation scenario while maintaining flexibility through user-defined optimization criteria.
Solution Approach 2:
The system performs preliminary evaluation of cloud resources across multiple environments in advance, caching resource availability and characteristic information. When resource allocation is needed, the system quickly retrieves pre-evaluated information and makes decisions based on the selected optimization dimension, significantly reducing allocation time while maintaining ease of operation.
Data Source
AI summary
As described herein, a system, method, and computer program are provided for optimization of allocation of compute job resources in a multi-cloud environment. A compute job to be run is identified. An optimization target for the compute job is determined. Resource requirements of the compute job and resource availability for a plurality of cloud networks are processed to determine a resource allocation across the plurality of cloud networks that satisfies the optimization target for the compute job. The resource allocation is orchestrated for the compute job.