Virtual Machine Redistribution via Growing Neural Gas
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Solution Overview
Problem
Current cloud-computing environments face challenges in optimizing virtual resource distribution due to imbalances in network resource utilization, as existing tools fail to dynamically reconfigure topology based on real-time and historical bandwidth usage patterns, leading to performance bottlenecks and inefficient resource allocation.
Innovation Solution
A cloud-optimization module that utilizes a growing neural gas algorithm to represent the virtual network as a graph, weighting parameters based on network utilization data, and iteratively optimizes the topology to redistribute virtual components and resources, ensuring more even bandwidth distribution across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtual components are distributed across the network, then resource utilization improves, but network resource imbalance worsens
Solution Approach 1:
The system dynamically reconfigures network topology by continuously monitoring bandwidth utilization and automatically redistributing virtual components based on real-time conditions. The neural gas algorithm adapts the network structure dynamically, moving virtual components from overloaded segments to underutilized segments as utilization patterns change, thereby maintaining resource balance while maximizing utilization.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where network bandwidth utilization is continuously monitored and fed back to the optimization algorithm. This feedback drives automatic reconfiguration decisions, allowing the system to detect imbalances and trigger redistribution of virtual components to restore optimal resource distribution across network segments.
2Device complexity
If network topology is statically configured, then system complexity is reduced, but adaptability to changing utilization patterns worsens
Solution Approach 1:
The system employs self-service automation where the neural gas algorithm autonomously monitors network utilization patterns and performs automatic reconfiguration of virtual component distribution without requiring manual intervention. The system serves itself by detecting imbalances and executing redistribution operations autonomously, maintaining low operational complexity while achieving high adaptability.
Solution Approach 2:
The system changes network topology parameters dynamically by adjusting the distribution of virtual components based on monitored utilization patterns. The neural gas algorithm modifies network configuration parameters automatically in response to changing conditions, enabling the system to adapt to varying workload patterns while maintaining manageable complexity through automated parameter adjustment.
3Ease of manufacture
If virtual components are concentrated in specific network segments, then deployment simplicity is improved, but bandwidth utilization efficiency worsens
Solution Approach 1:
The system transitions from static concentration to dynamic distribution by continuously monitoring bandwidth utilization and automatically redistributing virtual components. The neural gas algorithm enables the network to adapt its configuration dynamically, moving components from concentrated high-utilization segments to distributed lower-utilization segments, thereby improving bandwidth efficiency while maintaining deployment simplicity through automation.
Data Source
AI summary
A method and associated systems for automatic redistribution of virtual machines. A cloud-optimization module selects parameters, such as bandwidth requirements, that characterize an efficiency of a virtual network. It assigns weightings to these parameters based on relative importance of each parameter to the proper operation of the network, where the weightings may be determined as functions of captured network-performance statistics. The module translates the network's topology into a graph in which each node represents a network entity, such as a virtual machine or an application, and each edge represents a connection between two such entities. The module then uses a growing neural gas algorithm to revise the graph and the weightings, and translates the revised graph to a more optimal topology that has redistributed the network entities to operate more efficiently, as measured by the weighted parameters.