Parallel Task Scheduling for Time-Bound Resource Allocation

Solution Overview

Problem

Current scheduling systems in pod-based container environments, such as Kubernetes, fail to determine the optimal number of parallel processes needed to complete time-critical tasks within a specified time frame with optimal resource utilization, leading to inefficiencies and potential resource overconsumption or undercompletion.

Innovation Solution

A task manager that can operate in two modes: inspection mode to determine the optimal number of parallel processes required to complete a task within a specified time range with minimal resource utilization, and run mode to execute the task with the determined settings, allowing for dynamic adjustment based on environmental changes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If the number of parallel processes is increased to complete time-critical tasks within a specified time frame, then task completion speed is improved, but resource utilization increases leading to potential overconsumption

Engineering Contradiction:

Improvetask completion speedVSAvoidresource utilization

Core Design Contradiction:

SpeedVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary analysis before task execution to determine the optimal number of parallel processes needed. By pre-calculating the relationship between process count, task complexity, and completion time, the system can instantiate the exact number of processes required without over-provisioning resources, thus achieving fast completion while avoiding resource overconsumption.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts the number of parallel processes based on real-time conditions including task characteristics, available resources, and environmental changes. This dynamic allocation allows the system to optimize the balance between completion speed and resource utilization for each specific task rather than using a static configuration.

Inventive Principle:

Principle #15Dynamics

2Use of energy by moving object

If the number of parallel processes is reduced to minimize resource utilization, then resource efficiency is improved, but task completion time increases risking failure to meet time constraints

Engineering Contradiction:

Improveresource efficiencyVSAvoidtask completion time

Core Design Contradiction:

Use of energy by moving objectVSLoss of time

Solution Approach 1:

The system performs preliminary analysis before task execution to determine the optimal number of parallel processes needed. By pre-calculating the relationship between process count, task complexity, and completion time, the system can instantiate the exact number of processes required without over-provisioning resources, thus achieving fast completion while avoiding resource overconsumption.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system incorporates feedback mechanisms that monitor actual task execution performance and resource consumption. This feedback is used to refine future process instantiation decisions, ensuring that the system learns from past executions to optimize the balance between resource efficiency and completion time for similar tasks.

Inventive Principle:

Principle #23Feedback

3Device complexity

If fixed process configuration is used for task execution, then system simplicity is maintained, but adaptability to varying task requirements and environmental changes is reduced

Engineering Contradiction:

Improvesystem complexityVSAvoidadaptability to task requirements

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The system dynamically adjusts the number of parallel processes based on real-time conditions including task characteristics, available resources, and environmental changes. This dynamic allocation allows the system to optimize the balance between completion speed and resource utilization for each specific task rather than using a static configuration.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes key parameters such as the number of parallel processes, resource allocation, and execution configuration based on task requirements and environmental conditions. By adjusting these parameters dynamically, the system maintains simplicity in its core architecture while achieving high adaptability to varying task demands.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12430178B2Time-bound task management in parallel processing environment

Publication Date: 2025.09.30 DELL PROD LP

AI summary

Techniques for task management in a parallel processing environment are disclosed. For example, a method comprises obtaining an indication of a data load for a given task, a time range within which the given task is to be executed, and a resource utilization limit for executing the task. The method computes a number of processes to be instantiated to execute the given task for the data load within the time range and the resource utilization limit. The method then schedules the given task to be executed by the computed number of processes in a parallel processing environment.