Multilevel Computational Task Balancing via Hierarchical Distribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing distributed computing systems face challenges in efficiently managing and balancing computationally intensive tasks across multiple compute nodes, leading to suboptimal performance and increased user intervention.
Innovation Solution
The implementation of a multilevel distribution system that uses interface circuitry, machine-readable instructions, and programmable circuitry to allocate computational tasks into sets, distribute them across compute nodes, monitor completion, and dynamically adjust task distribution based on node availability and task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional load balancing algorithms are used to distribute computational tasks between compute nodes, then task distribution is achieved, but system productivity remains suboptimal and user intervention is required
Solution Approach 1:
The patent segments computational tasks into multiple levels (first level tasks and second level tasks) and distributes them across compute nodes in a hierarchical manner. This segmentation enables finer-grained load balancing and automatic task distribution, improving productivity while reducing manual intervention requirements
Solution Approach 2:
The system implements self-service through automatic task distribution and monitoring mechanisms. The load balancer automatically assigns tasks to compute nodes based on availability and capacity, and automatically monitors completion without requiring continuous user intervention, thereby improving automation extent and productivity
2Productivity
If computational tasks are distributed across multiple compute nodes, then parallel processing capability is improved, but system complexity increases
Solution Approach 1:
The patent divides tasks into multiple levels and groups them into sets, creating a hierarchical task structure. This segmentation simplifies the distribution process by breaking down complex task management into manageable units, enabling parallel processing while reducing system complexity
Solution Approach 2:
The load balancer is designed as a multi-functional component that handles task allocation, monitoring, and coordination across multiple compute nodes. This universal design consolidates complexity into a single manageable system rather than distributing complexity throughout the entire system
3Ease of operation
If tasks are allocated and distributed manually, then control over task assignment is maintained, but time consumption increases
Solution Approach 1:
The system performs self-service by automatically allocating and distributing tasks to compute nodes based on predefined criteria and real-time availability. This eliminates manual task assignment while maintaining operational control through automated decision-making, significantly reducing time consumption
Solution Approach 2:
The patent implements preliminary action by pre-configuring task distribution criteria and compute node availability parameters before task execution. This allows the system to automatically make allocation decisions without manual intervention, reducing both time loss and maintaining operational ease
Data Source
AI summary
Methods and apparatus are disclosed for multilevel balancing of computational tasks, the multilevel balancing including interface circuitry to receive or access a batch of tasks; machine readable instructions; and programmable circuitry to at least one of instantiate or execute the machine readable instructions to allocate the batch of computational tasks into sets, distribute the sets to compute nodes, monitor the compute nodes for completion of the computational tasks, distribute ones of the computational tasks to computational resources of the respective compute nodes based on the monitoring of the completion of the computational tasks, monitor the compute nodes for completion of respective ones of the sets, distribute queued sets to the compute nodes based on the monitoring of the completion of the sets, and distribute queued tasks to the computational resources based on the monitoring of the completion of the tasks.