Force-Directed Graph Resource Allocation
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Solution Overview
Problem
Existing computer simulations that distribute computations across processors in a cluster face inefficiencies due to inter-node communication latencies, particularly when tasks need to communicate with each other, leading to unsatisfactory processing efficiency, cost, and reliability.
Innovation Solution
A computationally-efficient method is introduced that uses a force-directed graph (FDG) to associate entities in a network graph, where seed nodes are configured with attractions or repulsions based on attribute sets, achieving equilibrium to minimize inter-node communication by translating the FDG solution space to the network graph, allowing for efficient allocation of resources and reducing the need for random seeding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If tasks are partitioned across multiple computing nodes to reduce computational demands on individual processors, then the computational load per processor is reduced, but inter-node communication latencies increase and negatively impact system performance
Solution Approach 1:
The patent applies preliminary action by performing graph partitioning and task allocation before the simulation execution begins. The system pre-processes the computational graph to identify communication patterns and optimally assigns tasks to computing nodes in advance, thereby minimizing inter-node communication latencies during actual simulation runtime while maintaining reduced computational load per processor.
Solution Approach 2:
The patent segments the computational tasks and data into distinct partitions that can be independently processed while minimizing cross-partition communication. By dividing the simulation workload into spatially-coherent segments and assigning them to specific computing nodes, the system reduces the frequency and volume of inter-node communications required during execution.
2Loss of time
If graph partitioning techniques are used to minimize inter-node communications, then communication efficiency improves, but computational efficiency of the overall system remains unsatisfactory
Solution Approach 1:
The patent applies dynamics by implementing adaptive load balancing and dynamic task migration capabilities. The system continuously monitors computational progress and communication patterns, then dynamically reassigns tasks between computing nodes to optimize both communication efficiency and overall computational throughput, preventing any single node from becoming a bottleneck.
Solution Approach 2:
The patent implements feedback mechanisms that monitor system performance metrics including communication latencies, processor utilization, and task completion rates. This feedback is used to continuously refine the task allocation strategy, adjusting partitioning and assignment decisions to simultaneously minimize inter-node communications and maximize overall system computational efficiency.
3Ease of manufacture
If random seeding is used in graph partitioning, then implementation is simple, but computational efficiency and processing speed are reduced
Solution Approach 1:
The patent applies parameter changes by transitioning from random seeding to deterministic seeding strategies based on graph structural properties such as node degrees, centrality measures, and community detection algorithms. This change in seeding parameters maintains implementation feasibility while dramatically improving partitioning quality and reducing the computational iterations required to achieve optimal task allocation.
Solution Approach 2:
The patent performs preliminary analysis of the graph structure before partitioning, pre-computing structural metrics and identifying optimal seed nodes based on graph topology. This preliminary action eliminates the need for random trial-and-error seeding, directly improving processing speed while keeping the implementation approachable through structured preprocessing steps.
Data Source
AI summary
A method to associate a set of first entities to a set of second entities, e.g., computing jobs to processors, agent teams to workspace resources within a physical location, or the like. The NG is seeded using a force directed graph (FDG), whose “seed” particles represents the agents and their relative interconnectedness. The FDG is first brought into an equilibrium state to define a solution space. A relative coordinate system of the FDG solution space is then translated to a number of vertices represented in the NG, and then an initial seeding of the seed particles in the NG (based on their relative positions in the FDG solution space) is carried out. A search is then performed. During the search, each seed vertex releases its embedded agents to adjacent vertices to enable the agents to search for and achieve a required count. During this process, the seed particles grow to the desired size (with their constituent first entities then located at the NG vertices) to complete the agent-to-resource allocation process.