Asset Lifecycle Management and Operations: A Problem Statement
draft-palmero-ivy-ps-almo-01
| Document | Type | Active Internet-Draft (individual) | |
|---|---|---|---|
| Authors | Marisol Palmero , Frank Brockners , Sudhendu Kumar , Camilo Cardona , Diego Lopez | ||
| Last updated | 2024-04-24 | ||
| Replaces | draft-palmero-opsawg-ps-almo | ||
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draft-palmero-ivy-ps-almo-01
IVY Working Group M. Palmero
Internet-Draft F. Brockners
Intended status: Informational Cisco Systems
Expires: 26 October 2024 S. Kumar
NC State University
C. Cardona
NTT
D. Lopez
Telefonica I+D
24 April 2024
Asset Lifecycle Management and Operations: A Problem Statement
draft-palmero-ivy-ps-almo-01
Abstract
This document presents a problem statement for assets lifecycle
management and operations. It describes a framework, the motivation
and requirements for asset-centric metrics including but not limited
to, asset adoption, usability, entitlements, supported capabilities,
and enabled capabilities. The document also defines an information
model. The primaty objectives for the problem statement document is
to validate and proof that the framework can measure and improve the
network operators' experience along the lifecycle journey, from
technical requirements and technology selection through renewal,
including the end of life of an asset.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet-
Drafts is at https://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on 26 October 2024.
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Copyright Notice
Copyright (c) 2024 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (https://trustee.ietf.org/
license-info) in effect on the date of publication of this document.
Please review these documents carefully, as they describe your rights
and restrictions with respect to this document. Code Components
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provided without warranty as described in the Revised BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 4
3. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . 5
4. Sample Use Cases . . . . . . . . . . . . . . . . . . . . . . 6
4.1. Entitlement Inventory and Activation . . . . . . . . . . 7
4.2. Risk Mitigation Check . . . . . . . . . . . . . . . . . . 8
4.3. Bug Fixes and Errata . . . . . . . . . . . . . . . . . . 8
4.4. Security Advisory . . . . . . . . . . . . . . . . . . . . 8
4.5. Optimal Software Version . . . . . . . . . . . . . . . . 9
4.5.1. Software Conformance . . . . . . . . . . . . . . . . 9
4.5.2. What-if Analysis . . . . . . . . . . . . . . . . . . 9
4.6. Asset Retirement - End of Life . . . . . . . . . . . . . 10
5. ALMO Information Model . . . . . . . . . . . . . . . . . . . 10
6. Security Considerations . . . . . . . . . . . . . . . . . . . 11
Change log . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 12
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Informative References . . . . . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 13
1. Introduction
The virtualization of hardware assets and the development of
applications using microservice architectures for cloud-native
infrastructures triggered new utilization and licensing models. For
example, a service can be deployed by composing multiple assets
together; where an asset refers to hardware, software, application,
system or another service. Also, services can be instantiated on-
demand. As an example, cloud-native services from one vendor may be
hosted on the physical server from another vendor or a combination of
multiple cloud-native functions from one or more vendors can be
combined to execute a composite service.
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Such dynamicity introduces challenges for both lifecycle and adoption
management of the assets. For example, an operator may need to
identify the capability availability of different assets or measure
the usage of each capability (or the combination thereof) from any
specific asset to measure, e.g., its optimal potential. Moreover, an
operator could pinpoint the reason of this non-nominal use of an
asset, e.g., the software application could not be optimally deployed
because is not simple to use, or the usage is not well documented,
etc. The operator may then feed this information back to the support
teams and the software developers, so they can focus their work
effort only on features that users are adopting, or even determine
when the lifecycle of the development could end.
This creates the need to collect and analyze asset-centric lifecycle
management and operations data. This data is referred to as Asset
Lifecycle Management and Operations (ALMO). Note that ALMO is not
limited to virtualized or cloud environments, it covers all types of
networking environments in which technology assets are deployed. No
assumption is made about which technology is used to implement an
asset.
ALMO data is the set of data required to measure asset-centric
lifecycle metrics including, but not limited to, asset adoption and
usability, entitlement, supported capabilities , and enabled
capabilities.
The main challenge in collecting ALMO data, especially in a
multivendor environment, is the ability to produce and consume such
data in a vendor-agnostic, consistent, and synchronized way. Another
challenge is to maintain/cleanup the data (e.g., update it with
external data such as End of Support (EoS)).
This document describes the motivation for ALMO, lists sample use
cases, followed by an ALMO information model. The use cases motivate
the need for new functional blocks and their interactions. The
current version of this document focuses on assets, entitlement
information, features, and feature's usage. A more specific document
will follow this reference with the specification of the YANG data
model for the specific four modules [RFC7950].
This document aims extend and provide a snapshot of the data model
draft's purpose, the issues it tackles, and the manner in which it
approaches the problem, all while being concise and summarizing the
key points from the more detailed future scope section.
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2. Terminology
The document makes use of the following terms:
* Asset: refers to hardware, software, applications, or services.
An asset can be physical or virtual. The granularity of what
constitutes an asset is deployment and implementation specific.
* Consumer: refers to an entity that utilizes the ALMO data. A
consumer can be an operator, an asset developer, or some other
interested third party. Also, network controllers or
orchestrators may be considered as consumers under some
conditions.
* Developer: refers to the entity that creates or develops an asset
or an asset component.
* Features: are options or functional capabilities offered by an
asset.
* Entitlement: commonly implemented as license; it represents the
rights obtained by the user, that allow them to access and utilize
certain capabilities of the asset, including a feature or set of
features.
* Event report: refers to record of details of an incident that an
operator reports, e.g., a trouble ticket.
* Lifecycle Management and Operations (LMO) connects to:
1. Assets as a generalized entity subject to LCM.
2. Entitlements as the essential policy root for LCM.
3. Metrics as the evidence for LCM transitions.
4. Reports (no incidents) as the way of collecting input from users.
* Optimal Software Version (OSV): refers to the elected software
version considered optimal in the consumer environment.
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* Operator: refers to the owner or consumer of the asset. An
operator belongs to an organization. Within the organization
there are entities that use the assets in their operations and
those who manage the assets. For example, supply chain and risk
management teams will also be operators by running “what-if”
scenarios to anticipate decommissioning operations or assess the
impact of component exhaustion, etc. This document uses the terms
"user" and "operator" interchangeably.
* Usage: refers to how an asset is being used (e.g., which features
are used).
* User Experience: how a user interacts with and experiences a
particular asset. It includes a user's perceptions of ease of
use, efficiency, and utility of an asset.
Abbreviations:
* ALMO: Asset Lifecycle Management and Operations
* EoL: End of Life
* EoS: End of Support
* LCM: Lifecycle Management
* PID: Product Identifier
3. Motivation
The operator experience with a specific asset can be organized into
four classes:
1. Asset characteristic class: covering a set of data that
characterize an asset, entitlement, features, etc.
2. Utilization class: measuring how the assets, including the
features, are used, duration of usage, uptime, etc.
3. Notification class: covering any security advisory, retirement,
etc.
4. Incident class: recording and reporting problems that an operator
has faced with an asset.
The ability to measure, produce, and consume ALMO data could benefit
the operator in addressing issues such as:
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* Entitlements may not have been obtained at the nominal level of
usage for a given asset or feature, where a user might have bought
entitlements that are not activated. Defining a nominal level is
local to each deployment.
* Features of an asset might not be used as needed in all
deployments within the organization and for various reasons.
* Resolution of incidents involving an asset and the developer of a
technology used within the asset.
* Optimal and correct form of entitlements have not been purchased
for virtualized asset lifecycle events, such as scaling or
migration.
* Softwares versions go out of conformance; ALMO data can help in
facilitating DevOps deployment, including automated testing,
validation, pre-production, production, and certification
* Supply chain disruption; ALMO data can help with a link to supply
chain management and/or connect it with the provenance
information.
ALMO could help developer organizations to optimize their features.
For example, they could consider deprecating features that are used
infrequently, focus on introducing more features for the assets that
are widely deployed in various infrastructures, or adjust the design
to better ease usability or ease integration, etc.
ALMO provides a structured approach for users to provide feedback
about an asset (e.g., potential deficiency of a feature, feature
enhancement request, inadequacy of a usage-based license model). An
administrator in the user organization may define and thus include
specific metrics that identify a potential problem of a specific
asset feature . A developer can determine the impact of the potential
deficiency from the number of users providing feedback or the amount/
periodicity of feedback (e.g., enrollment, updates). Note that this
channel is different from a "call to a Technical Assistance Center"
in which the user may request help in resolving specific operational
issues with the asset.
4. Sample Use Cases
This section presents some use cases where ALMO is useful.
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4.1. Entitlement Inventory and Activation
An Ops team would like to understand which entitlements are activated
and which are being used, or consumed. It is also important for
asset operators to understand which features might need an
entitlement and how to activate them.
It is relatively straightforward to have an inventory of existing
entitlements when there is only one asset developer (providing the
asset) and one asset family.
But complexity grows when there are many different developers,
systems and processes involved. New service offerings have
introduced new attributes and datasets and require alignment with new
business models (pay-per-product, subscription model, pay-as-you-go
model, etc.). They might support different entitlement types and
models: asset activation keys, trust-based model, systems that act as
proxy from the back end owned by the asset developer to support the
control of entitlements, etc.
Sometimes it is a challenge to report which entitlements have been
bought by the asset user, or who in the user organization owns that
entitlement because that information might rely on different asset
developers; even within the same asset developer, entitlements may
correspond to different types or groups of assets. Asset users often
need to interact with different entitlement systems and processes.
Information on how assets are entitled could be delivered from a
combination of attributes such as: sales order, purchase order, asset
activation key, serial number, etc.
If there is no consistency on how to deal with those data points,
complexity increases for the consumer, potentially requiring
expensive manual procedures to correlate information. Automating
those steps or exceptions becomes time-consuming, eventually leading
to higher costs for the asset consumer.
Having a common ALMO reference eases the integration between
different data sources, processes, and consolidation of the
information under a common reference.
The ALMO information model and companion data models will include
data to support metrics that might be required by consumption-based
charging and entitlement of asset usage.
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4.2. Risk Mitigation Check
Asset operators require tracking issues known by the asset developer
that are causing assets to crash or perform badly so that they can
act to remediate the issue quickly, or even prevent the crash if
alerts are triggered on time. There are analytics tools that can
process memory core dumps and crash-related files, providing the
ability to the asset developers to determine the root cause.
Accordingly, asset users can remediate the problem, automate the
remedy to enable incident deflection, allowing the support staff to
focus on other problems.
Risk Mitigation Check (RMC) could also include the possibility to be
aware of current and historical restarts allowing network and
software engineers to enhance the service quality to asset users.
Also RMC could run what-if scenarios to identify which assets would
be impacted when a certain component is faulty.
4.3. Bug Fixes and Errata
Both hardware and software critical issues or Errata need development
to automate asset user matching:
* Hardware Errata match on product identifiers (PIDs) + serial
numbers along with additional hardware attributes.
* Software Errata match on software type and software version along
with some additional device attributes.
Engineering might develop the logic to check whether any critical
issue applies to a single serial number or a specific software
release.
The information to be correlated includes customer identification,
activated features, and asset information that the asset user might
own. All this information needs to be correlated with hardware and
software errata, and EOL information to show which part of the asset
inventory might be affected.
4.4. Security Advisory
The Security Advisory use case automates the matching of asset user
data to security bulletins published by asset developers.
Security Advisory logic implemented by developers could apply to a
specific software release.
This is a specific use case of Section 4.3.
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4.5. Optimal Software Version
The objective of the Optimal Software Version (OSV) use case is that
consumers can mark software images as OSV for their assets; based on
this, it is easier for them to control and align their hardware and
software assets to the set of OSVs.
Based on the logic of OSV, use cases like software compliance, risk
trend analysis, acknowledge bugs, security advisories, errata, what-
if analysis, etc., could be realized.
4.5.1. Software Conformance
All the assets should normally be running at their latest recommended
software version; notably in case of a required security update for a
specific feature. The recommended version may be a decision of the
vendor or the operator.
The Software Conformance use case provides a view to the asset users
and informs the users whether the assets that belong to a specific
group conforms to the OSV. It can provide the users with a report,
including a representation of software compliance for the entire
network and software applications. This report could include the
current software version running on the asset and the recommended
software version. The report could enable users to quickly highlight
which group of assets might need the most attention to inspire
appropriate actions.
The Software Conformance use case uses data that might not be
provided by the asset itself. Data needs to be provided and
maintained also by the asset developers, through e.g., asset catalog
information. Similar logic applies to a feature catalog, where the
asset developer maintains the data and updates it adequately based on
existing bugs, security advisories, etc.
The Software Conformance process needs to correlate the Software
catalog information with the software version running on the asset.
4.5.2. What-if Analysis
The What-if Analysis use case allows asset users to plan for new
hardware or software, giving them the possibility to change the
configuration parameters or model how new hardware or software might
change the software suggestions generated by OSV.
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OSV and the associated use cases involve dependencies on attributes
that might need to be collected from assets directly, including
related inventory information (serial numbers, asset identifiers,
software versions, etc.), but also dynamic information could be
required, like:
* Information on features that might be enabled on the particular
asset.
* Catalogs, that might include information related to release notes.
For example, consider a feature catalog. This catalog could
include software versions that support a specific feature; the
software releases that a feature is supported in; or the latest
version that a feature is supported in, in case the feature is
EOL.
* Data sources to correlate information coming from reports on
critical issues or errata, security advisory, End of Life, etc.
Those catalogs and data sources with errata information, EOL, etc.
need to be maintained and updated by asset developers, making sure,
that the software running on the assets is safe to run and up to
date.
4.6. Asset Retirement - End of Life
Hardware EOL reports need to map Hardware EOL PIDs, focusing on base
PIDs so that bundles, spares, non-base PIDs, etc., do not provide
false EOL reporting to asset users.
Software EOL reports are used to automate the matching of user
software type and software version to software EOL bulletins.
5. ALMO Information Model
The broad metric classes defined in Section 3 that quantify user
experience can be modeled as shown in Figure 1. There is a reference
to the assets that the user possesses. Each asset may be entitled to
one or more entitlements; an entitlement may contain one or more sub-
entitlements. The level of usage for each feature and entitlement
associated with the asset is measured.
For example, a user needs to measure the utilization of a specific
entitlement for a specific type of asset. The information about the
entitlement may reside in an entitlement server. The state
(activated or not) of the entitlement may reside with the asset
itself or a proxy. They can be aggregated/correlated as per the
information model shown in Figure 1 to give information to the user
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regarding the utilization of the entitlements. The user experience
is thus enhanced by having accurate knowledge about the utility of
the given entitlement.
+------entitled_by/entitles------+
| |
V V
+-------------+ +------------+ +-------------+
| Entitlement | | Feature |<-->| Usage |
| |<----+ +---->+ | +-------------+
+-------------+ | | +------------+ | |
| Entitlement | | | | Feature | | Usage |
| attributes | | | | attributes | | attributes |
+-------------+ | | +------------+ +-------------+
entitled_by/| |
entitles | | tracked_by/
| | tracks
+---v---v-------+ +---------------+
| Asset +<------------>| Future |
+---------------+ future_ | Expansion |
| Asset | association +---------------+
| attributes |
+---------------+
Figure 1: ALMO Information Model
To simplify the information model diagram, it has not been included a
representation that considers assets, to be defined by other assets;
as well as entitlements and features. Entitlements could be
considered as a composition of one of more entitlements, and a
feature could also be part of other features. The model allows for
future expansion by new metrics that will quantify user experience.
Notice that future association relationship and future expansion
might be linked to asset or to one of the other datasets: Feature,
Feature Usage or Entitlements.
6. Security Considerations
ALMO will bring several security and privacy implications because of
the various components and attributes when defining the YANG modules
of the information model. For example, each functional component can
be tampered with to give manipulated data. ALMO when used alone or
with other relevant data, can identify an individual, revealing
personal data. Misconfigurations can lead to data being accessed by
unauthorized entities.
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The transport entity of the functional model must implement methods
for secure transport. This document also contains an Information
model in which some of the objects defined are writable. If the
objects are deemed sensitive in a particular environment, access to
them must be restricted using appropriately configured security and
access control rights. Appropriate authentication and audit
procedures must be in place for all the consumers of ALMO.
Change log
RFC Editor Note: This section is to be removed during the final
publication of the document.
version 01
* Introduce a Scope section, that will tackle key points to consider
when defining the corresponding YANG data models and how to relate
them.
version 00
* Reference to [I-D.draft-palmero-opsawg-dmlmo-10] draft and
[I-D.draft-palmero-opsawg-ps-almo-00], looking to simplify the
objective and understanding of it, with focus on the framework and
the main functional models, reducing scope to assets, features,
usage and entitlements; with special highlight to the
interrelation between them.
* Work moved to IVY working group.
Acknowledgments
The authors wish to thank Martin Beverley, Mohamed Boucadair, Ignacio
Dominguez Martinez-Casanueva and Gonzalo Salgueiro (by alphabetical
order) for their helpful comments and suggestions.
Contributors
This document was created by meaningful contributions (by
alphabetical order) from Shwetha Bhandari, Yenu Gobena, Nagendra
Kumar Nainar, Jan Lindblad, Josh Suhr, Dhiren Tailor, Yannis
Viniotis, and Éric Vyncke.
Informative References
[I-D.draft-palmero-opsawg-dmlmo-10]
Palmero, M., Brockners, F., Kumar, S., Bhandari, S.,
Cardona, C., and D. Lopez, "Data Model for Lifecycle
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Management and Operations", Work in Progress, Internet-
Draft, draft-palmero-opsawg-dmlmo-10, 7 July 2023,
<https://datatracker.ietf.org/doc/html/draft-palmero-
opsawg-dmlmo-10>.
[I-D.draft-palmero-opsawg-ps-almo-00]
Palmero, M., Brockners, F., Kumar, S., Cardona, C., and D.
Lopez, "Asset Lifecycle Management and Operations, Problem
Statement", Work in Progress, Internet-Draft, draft-
palmero-opsawg-ps-almo-00, 29 June 2023,
<https://datatracker.ietf.org/doc/html/draft-palmero-
opsawg-ps-almo-00>.
[RFC7950] Bjorklund, M., Ed., "The YANG 1.1 Data Modeling Language",
RFC 7950, DOI 10.17487/RFC7950, August 2016,
<https://www.rfc-editor.org/rfc/rfc7950>.
Authors' Addresses
Marisol Palmero
Cisco Systems
Email: mpalmero@cisco.com
Frank Brockners
Cisco Systems
Email: fbrockne@cisco.com
Sudhendu Kumar
NC State University
Email: skumar23@ncsu.edu
Camilo Cardona
NTT
Email: camilo@ntt.net
Diego Lopez
Telefonica I+D
Email: diego.r.lopez@telefonica.com
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