Dynamic Workload Orchestration via Data Complexity Scoring
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Solution Overview
Problem
Existing workload processing technologies lack efficient dynamic orchestration methods for containerized applications, particularly in handling varying data complexity, leading to suboptimal resource allocation and processing efficiency.
Innovation Solution
A computer-implemented method for dynamic workload orchestration based on data complexity, which computes complexity scores for workload portions and uses an orchestration engine to assign them to corresponding compute resources, optimizing resource allocation and processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workloads are processed using traditional static orchestration methods, then system simplicity is maintained, but resource allocation efficiency deteriorates due to inability to adapt to varying data complexity
Solution Approach 1:
The patent implements dynamic workload orchestration by continuously monitoring data complexity metrics and adjusting resource allocation in real-time. The system transitions from static to dynamic scheduling, where compute resources are dynamically assigned based on incoming workload characteristics, enabling efficient adaptation to varying data complexity while maintaining high resource utilization.
Solution Approach 2:
The system changes operational parameters by adjusting resource allocation decisions based on measured data complexity parameters. Complexity metrics such as data volume, variety, and processing requirements are used as input parameters to modify orchestration behavior, allowing the system to optimize resource distribution according to actual workload conditions rather than fixed policies.
2Productivity
If uniform resource allocation is used for all workloads, then system operation simplicity is maintained, but processing efficiency deteriorates due to mismatch between resource capacity and workload complexity
Solution Approach 1:
The patent applies local quality by tailoring resource allocation decisions to specific workload characteristics rather than applying uniform policies. Each workload receives customized resource assignment based on its data complexity profile, ensuring that processing resources match the specific requirements of each task while maintaining overall system efficiency through differentiated service approaches.
3Loss of energy
If workloads are not segmented by complexity, then orchestration simplicity is maintained, but resource utilization deteriorates due to inability to match appropriate resources to data complexity requirements
Solution Approach 1:
The system segments workloads into distinct categories based on data complexity metrics, creating separate processing streams for different workload types. This segmentation enables specialized resource allocation where compute resources are matched to specific complexity levels, improving resource utilization by preventing both over-provisioning and under-provisioning while maintaining manageable orchestration through structured classification.
Data Source
AI summary
According to aspects of the present disclosure, systems, methods and computer program products can be provided for dynamic workload orchestration based on data complexity. Methods, computer program products and/or systems are provided for dynamic workload orchestration that perform the following operations: (i) receiving a workload for orchestration; (ii) computing complexity scores for respective portions of the workload, where the complexity scores are computed based at least on parameters describing data associated with the portions of the workload; and (iii) using an orchestration engine to assign the portions of the workload to corresponding compute resources, based on their respective complexity scores.