Execution Pipeline Task Matching Across Specialized Environments
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Solution Overview
Problem
Existing analytical pipelines face inefficiencies due to the use of a single execution environment that is not tailored to the specific execution properties of each task, leading to complex data processing and resource wastage, especially when dealing with historical, distributed, and sensitive data.
Innovation Solution
A system and method that control the execution of tasks within selected execution environments by matching task properties with environment properties, assigning tasks to environments that best fit their requirements, thereby reducing data movement and optimizing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single execution environment is used to execute all tasks, then device complexity is reduced, but execution efficiency and resource utilization deteriorate
Solution Approach 1:
The patent segments the execution environment into multiple specialized environments (e.g., cloud execution environment, on-premises execution environment, batch execution environment) based on task characteristics. Each environment is optimized for specific workloads, allowing tasks to be routed to the most appropriate environment rather than using a single generic environment for all tasks.
Solution Approach 2:
The patent implements dynamic execution environment selection where the system continuously monitors task properties and execution environment availability, then dynamically assigns tasks to optimal environments. This dynamic routing adapts to changing conditions such as resource availability, task priorities, and environment capacity, maximizing execution efficiency without requiring static complex infrastructure.
2Ease of operation
If a single execution environment is used for all tasks, then ease of operation is improved, but resource utilization and network bandwidth utilization deteriorate
Solution Approach 1:
The patent implements self-service through automated task-environment matching where the system automatically evaluates task properties against available execution environment characteristics and assigns tasks without manual intervention. The pipeline module autonomously determines optimal environment assignments based on task requirements, data locations, and resource availability, eliminating the need for manual configuration while optimizing resource utilization.
Solution Approach 2:
The patent changes the parameters of execution environment selection by evaluating multiple factors including task compute requirements, data location, security requirements, and environment capacity. The system adjusts assignment decisions based on these varying parameters, routing tasks to environments that minimize resource wastage while maintaining operational simplicity through automated parameter-based decision making.
3Device complexity
If tasks are executed in environments not tailored to their specific needs, then device complexity is reduced, but computing resource utilization and network bandwidth utilization deteriorate
Solution Approach 1:
The patent applies local quality by customizing execution environments to match specific task requirements. Instead of using a uniform environment for all tasks, the system creates and assigns environments with appropriate local characteristics (e.g., specific hardware configurations, software stacks, security settings) tailored to each task's compute needs, data types, and performance requirements, thereby optimizing resource utilization.
Solution Approach 2:
The patent introduces a pipeline module as an intermediary between task submission and execution environment assignment. This intermediary component evaluates task properties, matches them against available execution environment characteristics, and facilitates optimal task-environment pairing. The pipeline module acts as a mediator that translates diverse task requirements into appropriate environment selections without requiring complex manual configuration.
Data Source
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AI summary
Systems, methods, and other embodiments associated with controlling an execution pipeline are described. In one embodiment, a method includes generating an execution pipeline for executing a plurality of tasks. The example method may also include evaluating execution definitions of the tasks to identify execution properties of the plurality of tasks. The example method may also include assigning each task to an execution environment selected from a set of execution environments based upon execution properties of the task matching execution properties of the execution environments. The example method may also include controlling the execution pipeline to execute each task within the assigned execution environments.