Implementation Report for Service Function Chain Scheduling Algorithm
draft-wu-ietf-sfc-scheduling-implementation-report-01
| Document | Type |
Expired Internet-Draft
(individual)
Expired & archived
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|---|---|---|---|
| Authors | wu xiaochun , Chen Hong , Chuanhuang Li | ||
| Last updated | 2023-06-26 (Latest revision 2022-12-23) | ||
| Replaces | draft-wu-sfc-scheduling-implementation-report | ||
| RFC stream | (None) | ||
| Intended RFC status | (None) | ||
| Formats | |||
| Stream | Stream state | (No stream defined) | |
| Consensus boilerplate | Unknown | ||
| RFC Editor Note | (None) | ||
| IESG | IESG state | Expired | |
| Telechat date | (None) | ||
| Responsible AD | (None) | ||
| Send notices to | (None) |
This Internet-Draft is no longer active. A copy of the expired Internet-Draft is available in these formats:
Abstract
This document provides the application examples of mapping and deployment algorithms to address the problems of large resource overhead and end-to-end latency in Service Function Chain(SFC) that cannot meet the requirements of latency-sensitive applications in terms of both latency optimization and resource optimization.In terms of delay-optimized mapping and deployment of SFC, the application example of granularity-variable SFC mapping algorithm based on microservice architecture reduces the number of instantiated microservice units and the number of end-to-end routing hops by merging redundant microservice units within the service function chain and reusing microservice units between the service function chains.In terms of resource-optimized mapping and deployment of SFC, the application example of SFC mapping algorithm based on improved gray wolf optimization algorithm optimizes the mapping of SFC through two strategies of reverse learning initialization and nonlinear convergence factor, and improves the efficiency of the mapping scheme.
Authors
wu xiaochun
Chen Hong
Chuanhuang Li
(Note: The e-mail addresses provided for the authors of this Internet-Draft may no longer be valid.)