Optical Switch Topology Optimization for Network Balance
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Solution Overview
Problem
Current network configurations in cloud computing, particularly in intra-datacenter and inter-datacenter networks, face challenges in optimizing logical topologies for efficient data routing and network balance, leading to suboptimal performance and complexity in managing high-speed optical transmission networks.
Innovation Solution
A method and system for configuring networks using a controller to establish and compare different logical topology candidates by exchanging switch configurations between optical switches, determining fitness based on 1-hop and 2-hop connections and network balance, and implementing the most optimal topology, leveraging rotational symmetry and random connections to improve network efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional network configuration methods are used, then network setup is simple, but network balance and routing efficiency are suboptimal
Solution Approach 1:
The system performs self-configuration by automatically generating, evaluating, and selecting optimal logical topologies without manual intervention. The controller autonomously exchanges switch configurations, evaluates fitness metrics, and implements optimized topologies, enabling the network to self-optimize its routing efficiency while managing complexity internally
Solution Approach 2:
The system changes topological parameters by exchanging switch configurations to generate different logical topology candidates. By varying the internal interconnection patterns between optical switches through configuration exchange, the system explores multiple topology states to find optimal routing paths, thereby improving network efficiency
2Reliability
If manual topology optimization is performed, then routing efficiency improves, but management complexity increases
Solution Approach 1:
The network controller automatically performs topology optimization through fitness evaluation and configuration exchange without requiring manual management. The system self-manages the complex task of achieving network balance by autonomously evaluating topology candidates and implementing optimal configurations, thereby maintaining reliability while preserving ease of operation
Solution Approach 2:
The system uses fitness evaluation as feedback to guide topology optimization. By calculating fitness metrics for each logical topology candidate and using this feedback to select and implement superior configurations, the system achieves network balance through automated iterative improvement rather than manual tuning
3Productivity
If complex logical topologies are implemented, then data routing efficiency improves, but configuration management becomes more difficult
Solution Approach 1:
The controller automatically manages complex switch configurations through systematic exchange and evaluation of logical topology candidates. By self-managing the complexity of configuring multiple optical switches to achieve efficient routing, the system delivers high data routing efficiency while shielding operators from configuration complexity
Solution Approach 2:
The system performs preliminary evaluation of multiple logical topology candidates before implementation. By pre-calculating fitness metrics and selecting optimal configurations in advance, the system prepares complex routing topologies that ensure efficient data routing while reducing the complexity of real-time configuration management
Data Source
AI summary
The present disclosure presents a system and method for determining a logical topology of a network, given the network's physical topology. More particularly, a logical topology is implemented across a plurality of optical circuit switches that interconnect the nodes of a network. Each of the optical circuit switches includes an initial internal configuration. The internal configuration of the optical circuit switches are swapped to generate new logical topologies. A fitness is determined for each of the generated topologies. The fitnesses of the topologies are then ranked and the most fit logical topology is implemented in the network.