Job Flow Status Compression in Distributed Task Containers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing complexity and scale of distributed task routines and data analysis tasks require more efficient mechanisms for organization, reproducibility, and resource allocation in distributed computing environments, particularly in managing job flows and resource allocation dynamically.
Innovation Solution
An apparatus and method that utilize message queuing to coordinate the execution of task routines across multiple container environments, dynamically managing resources and ensuring job flow completion despite events like container uninstantiation or failure, by using resource allocation routines and message queues to preserve and restore task states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed task routines and data analysis tasks are executed across multiple container environments, then productivity and resource utilization are improved, but reliability and system stability deteriorate due to container failures and uninstantiation events
Solution Approach 1:
The system performs preliminary actions by saving task state information to a data store before container uninstantiation occurs. This allows the task state to be preserved and restored later, preventing data loss and ensuring job flow completion even when containers fail. The state saving mechanism proactively prepares for potential failures by storing critical task information externally.
Solution Approach 2:
A message queue system acts as an intermediary between task routines and container management. The message queue decouples the task execution from container lifecycle events, allowing tasks to be queued, restored, and resumed independently of container instantiation status. This intermediary layer absorbs the shock of container failures and maintains task continuity.
2Reliability
If message queuing and state preservation mechanisms are implemented, then reliability and task completion are improved, but device complexity and system overhead increase
Solution Approach 1:
The message queue system serves multiple functions: it acts as a task coordinator, state storage medium, failure recovery mechanism, and container decoupling layer. By making the message queue multi-functional, the system reduces the need for separate dedicated components for each function, thereby managing complexity while maintaining reliability.
Solution Approach 2:
The system discards transient container instances while recovering and preserving task state information in the message queue and data store. When containers are uninstantiated, the task state is recovered from external storage rather than relying on volatile container memory, enabling reliable task resumption without requiring complex in-container state management.
3Productivity
If containers are dynamically instantiated and uninstantiated based on resource availability, then resource utilization is improved, but task continuity and execution stability deteriorate
Solution Approach 1:
The message queue serves as an intermediary that decouples task execution from container lifecycle management. Tasks are submitted to the message queue and can be restored and executed on different container instances without losing state. This allows containers to be dynamically instantiated and uninstantiated based on resource availability while maintaining task continuity through the intermediary message queue layer.
Solution Approach 2:
The system creates copies of task state information in external data stores and message queues rather than relying on single-container memory. When containers are uninstantiated, task state is copied/restored from these external representations, enabling seamless transition between container instances and maintaining execution stability despite container churn.
Data Source
AI summary
An apparatus includes a processor to: within a performance container, execute a performance routine to derive an order of performance of tasks of a job flow based on dependencies, begin performing the tasks, and store, within a job queue, a job performance status indication including task performance statuses; identify a set of sub flows within the job flow based on branches in the job flow; correlate each of the task performance statuses to a corresponding sub flow performance status; reduce the job performance status indication size by, for each sub flow in which all tasks have been completed, replace the corresponding task performance statuses with the corresponding sub flow performance status of completed, and for each sub flow with no task performed, replace the corresponding task performance statuses with the corresponding sub flow performance status of not executed; and transmit the job performance status indication to the requesting device.