Token Ring Node Rebalancing via Electrostatic Simulation
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Solution Overview
Problem
Current load-balancing methods for computer clusters are inefficient and costly, especially for large systems, as they require all nodes but one to be moved, and do not account for heterogeneous nodes with varying computing powers.
Innovation Solution
A server performs a redistribution simulation where nodes are treated as electrically charged particles, applying Coulomb's Law to minimize the number of token movements and assign higher workloads to more powerful nodes, using charge and static friction coefficients, terminal velocity, and simulation length to achieve a balanced cluster with minimal transaction cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If external tools are used to generate equidistant token values and move nodes, then load balance among nodes is improved, but the number of node moves increases and transaction cost increases
Solution Approach 1:
The patent replaces the mechanical approach of moving physical nodes to achieve equidistant distribution with an electromagnetic field-based simulation. Nodes are modeled as charged particles that naturally distribute themselves through electrostatic repulsion forces, eliminating the need for manual node movement while achieving optimal load balance distribution.
Solution Approach 2:
The patent changes the fundamental parameters of the system by introducing charge coefficients and friction coefficients to model node behavior. Instead of forcing equidistant distribution through external tools, the system uses parameter-based simulation where nodes with different computing powers are assigned different charge values, allowing them to self-organize into an optimal distribution that accounts for heterogeneous node capabilities.
2Device complexity
If nodes are treated as homogenous and moved to equidistant positions, then load distribution is simplified, but heterogeneous nodes with varying computing powers cannot be optimized
Solution Approach 1:
The patent applies local quality by assigning different charge coefficients to nodes based on their individual computing powers. Instead of treating all nodes uniformly, each node receives a customized charge value that reflects its local capability, allowing more powerful nodes to handle larger workloads while less powerful nodes handle smaller workloads, thus optimizing overall cluster performance.
Solution Approach 2:
The patent performs preliminary action by running an electromagnetic simulation before actual load distribution. The simulation pre-calculates the optimal positions and charge assignments for all nodes based on their computing powers, creating a predetermined optimal configuration that can be directly applied without trial-and-error adjustments.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces the number of node movements and transaction costs while efficiently rebalancing the cluster, allowing more powerful nodes to handle greater workloads, thus optimizing the token ring's performance.
Implementation Method 1
A server performs a redistribution simulation in which nodes on the cluster are treated as electrically charged particles
Implementation Method 2
assigning higher workloads to more powerful nodes and minimizing the number of tokens that need to be moved
Data Source
AI summary
Nodes on a token ring are rebalanced from an initial condition to a condition in which the load is optimally distributed based on a specified level of balance. Nodes are treated as electrically charged particles for purposes of the simulation and are assigned simulation values based on proportions between the size of the cluster, the computing power of the nodes, and the specified level of balance. A simulation module performs the rebalancing simulation by assigning the specified values to the particles and outputting, for each corresponding node, a token indicating the particle's final position and the position of the node on the token ring. The tokens are input to a redistribution module, which rebalances the cluster based on the generated tokens.