Virtual Machine Allocation via Equivalence Set Meta-Combinations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Efficient allocation of virtual machines (VMs) to network nodes is challenging, leading to suboptimal use of system resources and increased physical server requirements in cloud hosting and network management services.
Innovation Solution
The method involves grouping VMs into equivalence sets based on resource requirements, generating meta-combinations representing all possible combinations of VMs within these sets, and assigning these combinations to network nodes to ensure cumulative resource requirements align with the maximum resource capacity of each node, using algorithms like greedy heuristics for optimal allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If VMs are allocated to network nodes using traditional methods, then allocation can be completed, but resource utilization is suboptimal and requires more physical servers
Solution Approach 1:
The patent merges VM allocation decisions by grouping VMs into equivalence sets based on identical resource requirements, then combining these sets into meta-combinations that represent all possible allocations. This merging approach optimizes resource utilization by systematically packing VMs onto the minimum number of physical servers, thereby reducing the total quantity of physical servers needed while improving overall resource utilization efficiency.
2Adaptability or versatility
If brute force or multithreaded methods are used to generate all VM combinations, then complete allocation options are available, but computational overhead and time consumption increase significantly
Solution Approach 1:
The patent segments the VM allocation problem by dividing VMs into equivalence sets based on identical resource requirements. Instead of generating all possible combinations of individual VMs, the system segments them into groups and generates meta-combinations at the group level. This segmentation maintains complete allocation options while dramatically reducing computational time and overhead compared to brute force or multithreaded methods that would enumerate every individual VM combination.
3Reliability
If VMs with different resource requirements are allocated independently, then specific resource needs are met, but overall system efficiency decreases
Solution Approach 1:
The patent creates meta-combinations that serve multiple functions simultaneously: they represent all possible VM combinations within equivalence sets, ensure resource requirement satisfaction through systematic assignment, and optimize system efficiency by packing VMs onto the minimum number of physical servers. This universal approach allows a single allocation framework to handle diverse resource requirements while improving overall system efficiency.
Data Source
AI summary
A method for determining allocation of virtual machines includes: at least one of generating and accessing a list of virtual machines (VMs) configured to run on a network, each VM having at least one network resource requirement, each VM in the list associated with a size of the at least one network resource requirement; selecting at least one equivalence set of VMs having a resource requirement size that is at least substantially identical; generating a plurality of meta-combinations from the at least one equivalence set of VMs, each meta-combination representing all possible combinations of a selected number of individual VMs from the equivalence set; determining a maximum resource capacity of a node in the network; and providing a meta-combination assignment to the node, the meta-combination assignment including a group of meta-combinations having a cumulative resource requirement size value that is less than or equal to the maximum resource capacity.