Virtual Machine Load Balancing Exhaustive Search Matrix
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Solution Overview
Problem
Existing methods for distributed load balancing in virtual machines are limited by their inability to exhaustively search for optimal solutions, often relying on greedy algorithms or decision trees that explore only a fixed depth, which may not provide the most efficient distribution of virtual machines across hosts.
Innovation Solution
A management server generates a matrix representing virtual machine to host mappings, calculates imbalance metrics, identifies candidate migrations, and determines the best migration based on these metrics, using an efficient data structure and algorithms like depth-bound decision trees and recursive breadth-first search to exhaustively explore possible configurations while respecting cost-benefit trade-offs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If greedy algorithms or fixed-depth decision trees are used for load balancing, then the computational complexity is reduced and the algorithm executes faster, but the solution quality deteriorates because exhaustive search is not performed
Solution Approach 1:
The patent pre-calculates and stores imbalance metrics for different migration scenarios in a data structure before making load balancing decisions. This preliminary computation allows the system to quickly retrieve and compare pre-evaluated migration options, achieving exhaustive search quality without the full computational overhead of calculating metrics on-demand during execution
Solution Approach 2:
The system dynamically adjusts the depth of search and migration evaluation based on current system state and constraints. By making the search depth configurable and adaptive rather than fixed, the system can balance between computational resources expended and solution quality achieved, allowing exhaustive search when resources permit while maintaining practical performance
2Manufacturing precision
If exhaustive search is performed to find optimal virtual machine distributions, then the solution quality improves, but the computational time and complexity increase
Solution Approach 1:
The patent segments the exhaustive search process into manageable components by organizing the solution space into a tree structure where each level represents a migration decision. This segmentation allows the system to systematically explore all possibilities while pruning branches that cannot lead to optimal solutions, reducing the effective search space and computation time required for exhaustive search
Solution Approach 2:
The system changes parameters such as search depth, migration cost thresholds, and imbalance metric weights to control the extent of exhaustive search performed. By adjusting these parameters, the system can adapt the level of exhaustiveness to match available computational resources and time constraints while still finding high-quality solutions
Data Source
AI summary
Systems and methods for finding solutions exhaustively in distributed load balancing are provided. A plurality of virtual machines (VMs) is in communication with a virtual machine management server (VMMS). The VMMS is configured to generate a matrix that represents a mapping of a plurality of virtual machines (VMs) to a plurality of hosts and to calculate a first imbalance metric of the matrix. The VMMS is also configured to identify a plurality of candidate migrations the VMs. The VMMS searches through the solution space efficiently and can perform an exhaustive search to find the optimal solution. For each candidate migration, the VMMS is configured to alter the matrix to represent the candidate migration and to calculate a candidate imbalance metric based on the altered matrix. The VMMS is also configured to determine which candidate migration to perform based at least in part on the candidate imbalance metric for each candidate migration and the first imbalance metric.