Suurballe Cloud Service Embedding in Flexible-Grid Optical Networks
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Solution Overview
Problem
Current technologies fail to effectively map cloud services over software-defined flexible-grid optical transport networks, particularly in ensuring survivability against single link or node failures, due to constraints like wavelength continuity, spectral continuity, and modulation format selection, which are not adequately addressed in existing solutions for flexible-grid optical networks.
Innovation Solution
A Suurballe-based cloud service embedding procedure that maps working and backup virtual nodes and links over physical nodes and routes, using node-disjoint routes and spectrum allocation to maximize embedded cloud demands, ensuring survivability and resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud services are mapped over software-defined flexible-grid optical transport networks, then resource utilization and service capacity are improved, but network survivability against link or node failures deteriorates due to inadequate protection mechanisms
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing node-disjoint path pairs between all physical node pairs in an auxiliary graph structure before actual cloud service embedding occurs. This preprocessing enables rapid determination of protection paths when failures occur, ensuring both high service capacity and network survivability without real-time computation delays.
Solution Approach 2:
The patent segments the network routing problem into working paths and backup paths by finding node-disjoint path pairs. This segmentation ensures that if one path fails, the other remains intact, providing automatic protection while maximizing resource utilization through efficient spectrum allocation on both paths.
2Reliability
If node-disjoint paths are computed for every cloud service embedding, then network protection is improved, but computational complexity and processing time worsen
Solution Approach 1:
The patent dramatically reduces computational complexity by pre-computing all node-disjoint path pairs between physical node pairs and storing them in an auxiliary graph before cloud service embedding. This preliminary computation transforms the complex real-time path-finding problem into a simple lookup operation, maintaining full protection capability while enabling rapid service deployment.
3Productivity
If spectrum is allocated at lowest available wavelength, then resource efficiency is improved, but wavelength continuity constraint satisfaction becomes more difficult
Solution Approach 1:
The patent applies parameter changes by transforming the wavelength allocation problem into a path-finding problem in an auxiliary graph where spectrum availability is encoded as edge weights. This transformation automatically satisfies wavelength continuity constraints while allocating the lowest available wavelength, resolving the contradiction between resource efficiency and constraint satisfaction.
Data Source
AI summary
We propose an efficient procedure, namely disjoint pair procedure based cloud service embedding procedure that first maps working and backup virtual nodes over physical nodes while balancing computational resources of different types, and finally, maps working and backup virtual links over physical routes while balancing network spectral resources using the disjoint pair procedure.