CNLB Cloud Service Embedding in Flexible-Grid Optical Networks
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Solution Overview
Problem
Existing methods for cloud service embedding in software-defined flexible-grid optical transport networks are inefficient, particularly in quickly mapping virtual nodes and links over physical resources while ensuring resource isolation and spectral continuity, leading to suboptimal network performance.
Innovation Solution
The Compute followed by Network Load Balancing (CNLB) method arranges virtual nodes by resource demand and links by spectral requirements, mapping them over physical nodes and routes while balancing resource loads, and selects modulation formats and assigns wavelengths to optimize spectral usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing methods are used for cloud service embedding, then network connectivity can be established, but the embedding efficiency is low and network performance is suboptimal
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the k-shortest paths between all node pairs in a lookup table before embedding requests arrive. This pre-computation enables rapid path selection during embedding without real-time optimization overhead, thereby improving embedding efficiency while maintaining optimal network performance through pre-planned routing paths.
Solution Approach 2:
The patent segments the embedding process into distinct phases: virtual node mapping, virtual link mapping, and resource allocation. By dividing the complex embedding problem into manageable segments with specific optimization goals for each phase, the system achieves higher overall embedding efficiency while ensuring network performance requirements are met at each stage.
2Reliability
If resource allocation is optimized for specific data centers, then service performance improves, but resource utilization across the network becomes unbalanced
Solution Approach 1:
The patent applies local quality by allowing different virtual nodes to be mapped to physical nodes with locally optimized resource allocations based on their specific requirements. Each virtual link is routed through paths that locally optimize for spectral continuity and resource availability, while the overall system achieves balanced resource utilization through the flexible virtualization layer that can allocate resources dynamically across different physical locations.
3Productivity
If spectral resources are allocated flexibly, then spectral efficiency increases, but management complexity increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting spectral allocation parameters based on network conditions and service requirements. The system changes spectral width, wavelength, and modulation format parameters to optimize spectral efficiency for different virtual links, while the centralized controller automates these parameter adjustments to prevent management complexity from becoming a bottleneck.
Solution Approach 2:
The patent introduces a centralized SDN controller as an intermediary between the physical optical network and virtual services. This intermediary manages the complexity of flexible spectral resource allocation by providing automated path computation, resource allocation, and coordination, thereby enabling high spectral efficiency without proportionally increasing management complexity.
Data Source
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AI summary
A method entails an efficient procedure, namely Compute followed by Network Load Balancing (CNLB), that first maps virtual nodes over physical nodes while balancing computational resources of different types, and finally, maps virtual links over physical routes while balancing network spectral resources.