YANG data model for hardware management and power management

Scope

RFC8348: A YANG data model for Hardware Management:

https://datatracker.ietf.org/doc/html/rfc8348​: Provides a model for server hardware management but does not naturally extend to routers and other network elements. 

The data model includes configuration and system state (status information and counters for the collection of statistics).​

Draft-li-ivy-power-01: A YANG model for Power Management:

https://datatracker.ietf.org/doc/draft-li-ivy-power/​: Provides a YANG data model for power management. The gap in the RFC8348 (document above) is currently being addressed by this draft. 

Summary

This document defines a YANG data model [RFC7950] for the management of hardware on a single server. The data model includes configuration and system state (status information and counters for the collection of statistics).The data model in this document is designed to be compliant with the Network Management Datastore Architecture (NMDA) [RFC8342].  For implementations that do not yet support NMDA, a temporary module with system state data only is defined in Appendix A of RFC 8348.

Draft-li-ivy-power-01: A YANG model for Power Management:

Network sustainability is a key issue facing the industry.  Networks consume significant amounts of power at a time when the cost of power is rising and sensitivity about sustainability is very high. As an industry, we need to find ways to optimise the power efficiency of our networks both at a micro and macro level.  We have observed that traffic levels fluctuate and when traffic ebbs there is much more capacity than is needed. Powering off portions of network elements could save a significant amount of power, but to scale and be practical, this must be automated.The natural mechanism for enabling automation would be a Yet Another Next Generation (YANG) interface, so this document proposes a YANG model for power management.

Relevance for EXIGENCE

The format of the data models to extract power management metrics are relevant in EXIGENCE. They are particularly relevant in EXIGENCE, where they align with architecture by defining essential energy consumption metrics for network slicing architectures. This lays the groundwork for optimising energy efficiency in advanced network designs. Additionally, these models support reliable data collection and transparency in service consumption observability, essential for assessing and improving energy efficiency across diverse network environments. They also play a crucial role in resource interconnectivity, enabling dynamic adjustment of energy consumption profiles and resource utilisation.

Index