Internet DRAFT - draft-contreras-alto-service-edge
draft-contreras-alto-service-edge
ALTO L. M. Contreras
Internet-Draft Telefonica
Intended status: Informational S. Randriamasy
Expires: 14 September 2023 Nokia Bell Labs
J. Ros-Giralt
Qualcomm Europe, Inc.
D. Lachos
Benocs
C. Rothenberg
Unicamp
13 March 2023
Use of ALTO for Determining Service Edge
draft-contreras-alto-service-edge-07
Abstract
Service providers are starting to deploy computing capabilities
across the network for hosting applications such as AR/VR, vehicle
networks, IoT, and AI training, among others. In these distributed
computing environments, knowledge about computing and communication
resources is necessary to determine the proper deployment location of
each application. This document proposes an initial approach towards
the use of ALTO to expose such information to the applications and
assist the selection of their deployment locations.
About This Document
This note is to be removed before publishing as an RFC.
The latest revision of this draft can be found at
https://giralt.github.io/draft-contreras-alto-service-edge/#go.draft-
contreras-alto-service-edge.html. Status information for this
document may be found at https://datatracker.ietf.org/doc/draft-
contreras-alto-service-edge/.
Discussion of this document takes place on the WG Working Group
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Source for this draft and an issue tracker can be found at
https://github.com/giralt/draft-contreras-alto-service-edge.
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Status of This Memo
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Conventions and Definitions . . . . . . . . . . . . . . . . . 4
3. Computing Needs . . . . . . . . . . . . . . . . . . . . . . . 4
4. Usage of ALTO for Service Placement . . . . . . . . . . . . . 5
4.1. Integrating Compute Information in ALTO . . . . . . . . . 5
4.2. Association of Compute Capabilities to Network
Topology . . . . . . . . . . . . . . . . . . . . . . . . 5
4.3. ALTO Architecture for Determining Serve Edge . . . . . . 6
5. ALTO Design Considerations for Determining Service Edge . . . 7
5.1. Example of Entity Definition in Different Domains . . . . 7
5.2. Definition of Flavors in ALTO Property Map . . . . . . . 9
6. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . 11
6.1. Open Abstraction for Edge Computing . . . . . . . . . . . 12
6.2. Optimized placement of microservice components . . . . . 13
7. Security Considerations . . . . . . . . . . . . . . . . . . . 13
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8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 13
9. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 13
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 13
10.1. Normative References . . . . . . . . . . . . . . . . . . 13
10.2. Informative References . . . . . . . . . . . . . . . . . 14
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 15
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 15
1. Introduction
With the advent of network virtualization, operators can make use of
dynamic instantiation of network functions and applications by using
different techniques on top of commoditized computation
infrastructures, permitting a flexible and on-demand deployment of
services, aligned with the actual needs as demanded by the users.
Operators are starting to deploy distributed computing environments
in different parts of the network with the objective of addressing
different service needs including latency, bandwidth, processing
capabilities, storage, etc. This is translated in the emergence of a
number of data centers (both in the cloud and at the edge) of
different sizes (e.g., large, medium, small) characterized by
distinct dimension of CPUs, memory, and storage capabilities, as well
as bandwidth capacity for forwarding the traffic generated in and out
of the corresponding data center.
The proliferation of the edge computing paradigm further increases
the potential footprint and heterogeneity of the environments where a
function or application can be deployed, resulting in different
unitary cost per CPU, memory, and storage. This increases the
complexity of deciding the location where a given function or
application should be best deployed, as this decision should be
influenced not only by the available resources in a given computing
environment, but also by the network capacity of the path connecting
the traffic source with the destination.
It is then essential for a network operator to have mechanisms
assisting this decision by considering a number of constraints
related to the function or application to be deployed, understanding
how a given decision on the computing environment for the service
edge affects the transport network substrate. This would enable the
integration of network capabilities in the function placement
decision and further optimize performance of the deployed
application.
This document proposes the use of ALTO [RFC7285] for assisting such a
decision.
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2. Conventions and Definitions
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
3. Computing Needs
A given network function or application typically shows certain
requirements in terms of processing capabilities (i.e., CPU), as well
as volatile memory (i.e., RAM) and storage capacity. Cloud computing
providers such as Amazon Web Services or Microsoft Azure, typically
structure their offerings of computing capabilities by bundling CPU,
RAM and storage units as quotas, instances, and flavors that can be
consumed in an ephemeral fashion, during the actual lifetime of the
required function or application.
This same approach is being taken for characterizing bundles of
resources on the so-called Network Function Virtualization
Infrastructure Points of Presence (NFVI-PoPs) being deployed by the
telco operators. For instance, the Common Network Function
Virtualization Infrastructure Telecom Taskforce (CNTT) [CNTT],
jointly hosted by GSMA [GSMA] and the Linux Foundation, intends to
harmonize the definition of instances and flavors for abstracting
capabilities of the underlying NFVI, facilitating a more efficient
utilization of the infrastructure and simplifying the integration and
certification of functions. (Here certification means the assessment
of the expected behavior for a given function according to the level
of resources determined by a given flavor.) An evolution of this
initiative is Anuket [Anuket], which works to consolidate different
architectures for well-known tools such as OpenStack and Kubernetes.
Taking CNTT as an example, the flavors or instances can be
characterized according to:
* _Type of instance (T):_ Used to specify the type of instances,
which are characterized as B (Basic), or N (Network Intensive).
The latter includes network acceleration extensions for offloading
network intensive operations to hardware.
* _Interface Option (I):_ Used to specify the associated bandwidth
of the network interface.
* _Compute flavor (F):_ Used to specify a given predefined
combination of resources in terms of virtual CPU, RAM, disk, and
bandwidth for the management interface.
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* _Optional storage extension (S):_ Used to specify additional
storage capacity.
* _Optional hardware acceleration characteristics (A):_ Used to
specify acceleration capabilities for improving the performance of
the application.
The naming convention of an instance is thus encoded as TIFSA.
4. Usage of ALTO for Service Placement
ALTO can assist the deployment of a service on a specific flavor or
instance of the computing substrate by taking into consideration
network cost metrics. A generic and primary approach is to take into
account metrics related to the computing environment, such as
availability of resources, unitary cost of those resources, etc.
Nevertheless, the function or application to be deployed on top of a
given flavor must also be interconnected outside the computing
environment where it is deployed, therefore requiring the necessary
network resources to satisfy application performance requirements
such as bandwidth or latency.
The objective then is to leverage ALTO to provide information about
the more convenient execution environments to deploy virtualized
network functions or applications, allowing the operator to get a
coordinated service edge and transport network recommendation.
4.1. Integrating Compute Information in ALTO
CNTT proposes the existence of a catalogue of compute infrastructure
profiles collecting the computing capability instances available to
be consumed. Such a catalogue could be communicated to ALTO or even
incorporated to it.
ALTO server queries could support TIFSA encoding in order to retrieve
proper maps from ALTO. Additionally, filtered queries for particular
characteristics of a flavor could also be supported.
4.2. Association of Compute Capabilities to Network Topology
It is required to associate the location of the available instances
with topological information to allow ALTO construct the overall map.
The expectation is that the management of the network and cloud
capabilities will be performed by the same entity, producing an
integrated map to handle both network and compute abstractions
jointly. While this can be straightforward when an ISP owns both the
network and the cloud infrastructure, it can in general require
collaboration between multiple administrative domains. Details on
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potential scenarios will be provided in future versions of this
document.
At this stage, four potential solutions could be considered:
* To leverage (and possibly extend)
[I-D.ietf-teas-sf-aware-topo-model] for disseminating topology
information together with the notion of function location (that
would require to be adapted to the existence of available compute
capabilities). A recent effort in this direction can be found in
[I-D.llc-teas-dc-aware-topo-model].
* To extend BGP-LS [RFC7752], which is already considered as a
mechanism for feeding topology information in ALTO, in order to
also advertise computing capabilities as well.
* To combine information from the infrastructure profiles catalogue
with topological information by leveraging the IP prefixes
allocated to the gateway providing connectivity to the NFVI PoP.
* To integrate with Cloud Infrastructure Managers that could expose
cloud infrastructure capabilities as in [CNTT] and [GSMA].
The viability of these options will be explored in future versions of
this document.
4.3. ALTO Architecture for Determining Serve Edge
The following logical architecture defines the usage of ALTO for
determining service edges.
+--------+ Topological +---------+
| | Information | |
| |<--------------->| e.g.BGP |
ALTO | | | |
+--------+ protocol | | +---------+
| Client |<----------->| ALTO |
+--------+ | Server |
| | Computing +---------+
| | Information | e.g., |
| |<--------------->| Infra. |
| | |Catalogue|
+--------+ +---------+
Figure 1: Service Edge Information Exchange.
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In order to select the optimal edge server from both the network
(e.g., the path with lower latency and/or higher bandwidth) and the
cloud perspectives (e.g., number of CPUs/GPUs, available RAM and
storage capacity), there is a need to see the edge server as both an
IP entity (as in [RFC7285]) and an Abstract Network Element (ANE)
entity (as in [RFC9275]).
Currently there is no mechanism (neither in [RFC9275] nor [RFC9240])
to see the same edge server as an entity in both domains. The design
of ALTO, however, allows extensions that could be used to identify
that an entity can be defined in several domains. These different
domains and their related properties can be specified in extended
ALTO property maps, as proposed in the next sections.
5. ALTO Design Considerations for Determining Service Edge
This section is in progress and gathers the ALTO features that are
needed to support the exposure of both networking capabilities and
compute capabilities in ALTO Maps.
In particular, ALTO Entity Property Maps defined in [RFC9240] can be
extended. [RFC9240] generalizes the concept of endpoint properties
to domains of other entities through property maps. Entities can be
defined in a wider set of entity domains such as IPv4, IPv6, PID, AS,
ISO3166-1 country codes or ANE. In addition, RFC 9240 specifies how
properties can be defined on entities of each of these domains.
5.1. Example of Entity Definition in Different Domains
As there can be applications that do not necessarily need both
compute and networking information, it is fine to keep the entity
domains separate, each with their own native properties. However,
some applications need information on both topics, and a scalable and
flexible solution consists in defining an ALTO property type, that:
* Indicates that an entity can be defined in several domains;
* Specifies, for an entity, the other domains where this entity is
defined.
For instance, one possible approach is to introduce entity properties
that list other entity domains where an edge server is identified.
This property type should be usable in all entity domains types. The
following provides an example where the property "entity domain
mapping" lists the other domains in which an entity is defined.
Suppose an edge server is identified as follows:
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* In the IPV4 domain, with an IP address, e.g., ipv4:1.2.3.4;
* In the ANE domain, with an identifier, e.g., ane:DC10-HOST1.
To get information on this edge server as an entity in the "ipv4"
entity domain, an ALTO client can query the properties "pid" and
"entity-domain-mappings" and obtain a response as follows:
POST /propmap/lookup/dc-ip HTTP/1.1
Host: alto.example.com
Accept: application/alto-propmap+json,application/alto-error+json
Content-Length: TBC
Content-Type: application/alto-propmapparams+json
{
"entities" : ["ipv4:1.2.3.4"],
"properties" : [ "pid", "entity-domain-mappings"]
}
HTTP/1.1 200 OK
Content-Length: TBC
Content-Type: application/alto-propmap+json
{
"meta" : {},
"property-map": {
"ipv4:1.2.3.4" :
{"pid" : DC10,
"entity-domain-mappings" : ["ane"]}
}
}
To get information on this edge server as an entity in the "ane"
entity domain, an ALTO client can query the properties "entity-
domain-mappings" and "network-address" and obtain a response as
follows:
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POST /propmap/lookup/dc-ane HTTP/1.1
Host: alto.example.com
Accept: application/alto-propmap+json,application/alto-error+json
Content-Length: TBC
Content-Type: application/alto-propmapparams+json
{
"entities" : ["ane:DC10-HOST1"],
"properties" : [ "entity-domain-mappings", "network-address"]
}
HTTP/1.1 200 OK
Content-Length: TBC
Content-Type: application/alto-propmap+json
{
"meta" : {},
"property-map": {
"ane:DC10-HOST1"
{"entity domain mappings : ["ipv4"]",
"network-address" : ipv4:1.2.3.4}
}
}
Thus, if the ALTO Client sees the edge server as an entity with a
network address, it knows that it can see the server as an ANE on
which it can query relevant properties.
Further elaboration will be provided in future versions of this
document.
5.2. Definition of Flavors in ALTO Property Map
The ALTO Entity Property Maps [RFC9240] generalize the concept of
endpoint properties to domains of other entities through property
maps. The term "flavor" or "instance" refers to an abstracted set of
computing resources, with well-specified properties such as CPU, RAM
and Storage. Thus, a flavor can be seen as an ANE with properties
defined in terms of TIFSA. A flavor or instance is a group of 1 or
more elements that can be reached via one or more network addresses.
So an instance can also be seen as a PID that groups one or more IP
addresses. In a context such as the one defined in CNTT, an ALTO
property map could be used to expose TIFSA information of potential
candidate flavors, i.e., potential NFVI-PoPs where an application or
service can be deployed.
Figure 2 below shows an example of an ALTO property map with property
values grouped by flavor name.
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+--------+------------+-------+-----+------+------+-----+---+---+
| flavor | type (T) | inter | f-c | f-ra | f-di | f-b | S | A |
| -name | | face | pu | m | sk | w | | |
| | | (I) | (F) | (F) | (F) | (F) | | |
+--------+------------+-------+-----+------+------+-----+---+---+
| small- | basic | 1 | 1 | 512 | 1 GB | 1 G | | |
| 1 | | Gbps | | MB | | bps | | |
.................................................................
| small- | network- | 9 | 1 | 512 | 1 GB | 1 G | | |
| 2 | intensive | Gbps | | MB | | bps | | |
.................................................................
| medium | network- | 25 | 2 | 4 GB | 40 | 1 G | | |
| -1 | intensive | Gbps | | | GB | bps | | |
.................................................................
| large- | compute- | 50 | 4 | 8 GB | 80 | 1 G | | |
| 1 | intensive | Gbps | | | GB | bps | | |
.................................................................
| large- | compute- | 100 | 8 | 16 | 160 | 1 G | | |
| 2 | intensive | Gbps | | GB | GB | bps | | |
+--------+------------+-------+-----+------+------+-----+---+---+
Figure 2: ALTO Property Map.
The following example uses ALTO's filtered property map to request
properties "type", "cpu", "ram", and "disk" on five ANE flavors named
"small-1", "small-2", "medium-1", "large-1", "large-2" defined in the
example before.
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POST /propmap/lookup/ane-flavor-name HTTP/1.1
Host: alto.example.com
Accept: application/alto-propmap+json,application/alto-error+json
Content-Length: 155
Content-Type: application/alto-propmapparams+json
{
"entities" : ["small-1",
"small-2",
"medium-1",
"large-1"],
"large-2"],
"properties" : [ "type", "cpu", "ram", "disk"]
}
HTTP/1.1 200 OK
Content-Length: 295
Content-Type: application/alto-propmap+json
{
"meta" : {
},
"property-map": {
"small-1":
{"type" : "basic", "cpu" : 1,
"ram" : "512MB", "disk" : 1GB},
"small-2":
{"type" : "network-intensive", "cpu" : 1,
"ram" : "512MB", "disk" : 1GB},
"medium-1":
{"type" : "compute-intensive", "cpu" : 2,
"ram" : "4GB", "disk" : 40GB},
"large-1":
{"type" : "compute-intensive", "cpu" : 4,
"ram" : "8GB", "disk" : 80GB},
"large-2":
{"type" : "compute-intensive", "cpu" : 8,
"ram" : "16GB", "disk" : 160GB},
}
}
Figure 3: Filtered Property Map query example.
6. Use Cases
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6.1. Open Abstraction for Edge Computing
As shown in this document, modern applications such as AR/VR, V2X, or
IoT, require bringing compute closer to the edge in order to meet
strict bandwidth, latency, and jitter requirements. While this
deployment process resembles the path taken by the main cloud
providers (notably, AWS, Facebook, Google and Microsoft) to deploy
their large-scale datacenters, the edge presents a key difference:
datacenter clouds (both in terms of their infrastructure and the
applications run by them) are owned and managed by a single
organization, whereas edge clouds involve a complex ecosystem of
operators, vendors, and application providers, all striving to
provide a quality end-to-end solution to the user. This implies
that, while the traditional cloud has been implemented for the most
part by using vertically optimized and closed architectures, the edge
will necessarily need to rely on a complete ecosystem of carefully
designed open standards to enable horizontal interoperability across
all the involved parties. This document envisions ALTO playing a
role as part of the ecosystem of open standards that are necessary to
deploy and operate the edge cloud.
As an example, consider a user of an XR application who arrives at
his/her home by car. The application runs by leveraging compute
capabilities from both the car and the public 5G edge cloud. As the
user parks the car, 5G coverage may diminish (due to building
interference) making the home local Wi-Fi connectivity a better
choice. Further, instead of relying on computational resources from
the car and the 5G edge cloud, latency can be reduced by leveraging
computing devices (PCs, laptops, tablets) available from the home
edge cloud. The application's decision to switch from one domain to
another, however, demands knowledge about the compute and
communication resources available both in the 5G and the Wi-Fi
domains, therefore requiring interoperability across multiple
industry standards (for instance, IETF and 3GPP on the public side,
and IETF and LF Edge [LF-EDGE] on the private home side). ALTO can
be positioned to act as an abstraction layer supporting the exposure
of communication and compute information independently of the type of
domain the application is currently residing in.
Future versions of this document will elaborate further on this use
case.
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6.2. Optimized placement of microservice components
Current applications are transitioning from a monolithic service
architecture towards the composition of microservice components,
following cloud-native trends. The set of microservices can have
associated SLOs which impose constraints not only in terms of
required compute resources (CPU, storage, ...) dependent on the
compute facilities available, but also in terms of performance
indicators such as latency, bandwidth, etc, which impose restrictions
in the networking capabilities connecting the computing facilities.
Even more complex constrains, such as affinity among certain
microservices components could require complex calculations for
selecting the most appropriate compute nodes taken into consideration
both network and compute information.
Thus, service/application orchestrators can benefit from the
information exposed by ALTO at the time of deciding the placement of
the microservices in the network.
7. Security Considerations
TODO Security
8. IANA Considerations
This document has no IANA actions.
9. Conclusions
Telco networks will increasingly contain a number of interconnected
data centers and edge clouds of different sizes and characteristics,
allowing flexibility in the dynamic deployment of functions and
applications for advanced services. The overall objective of this
document is to begin a discussion in the ALTO WG regarding the
suitability of the ALTO protocol for determining where to deploy a
function or application in these distributed computing environments.
The result of these discussions will be reflected in future versions
of this draft.
10. References
10.1. Normative References
[RFC7285] Alimi, R., Ed., Penno, R., Ed., Yang, Y., Ed., Kiesel, S.,
Previdi, S., Roome, W., Shalunov, S., and R. Woundy,
"Application-Layer Traffic Optimization (ALTO) Protocol",
RFC 7285, DOI 10.17487/RFC7285, September 2014,
<https://www.rfc-editor.org/info/rfc7285>.
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[RFC7752] Gredler, H., Ed., Medved, J., Previdi, S., Farrel, A., and
S. Ray, "North-Bound Distribution of Link-State and
Traffic Engineering (TE) Information Using BGP", RFC 7752,
DOI 10.17487/RFC7752, March 2016,
<https://www.rfc-editor.org/info/rfc7752>.
[RFC9275] Gao, K., Lee, Y., Randriamasy, S., Yang, Y., and J. Zhang,
"An Extension for Application-Layer Traffic Optimization
(ALTO): Path Vector", RFC 9275, DOI 10.17487/RFC9275,
September 2022, <https://www.rfc-editor.org/info/rfc9275>.
[RFC9240] Roome, W., Randriamasy, S., Yang, Y., Zhang, J., and K.
Gao, "An Extension for Application-Layer Traffic
Optimization (ALTO): Entity Property Maps", RFC 9240,
DOI 10.17487/RFC9240, July 2022,
<https://www.rfc-editor.org/info/rfc9240>.
[I-D.ietf-teas-sf-aware-topo-model]
Bryskin, I., Liu, X., Lee, Y., Guichard, J., Contreras, L.
M., Ceccarelli, D., Tantsura, J., and D. Shytyi, "SF Aware
TE Topology YANG Model", Work in Progress, Internet-Draft,
draft-ietf-teas-sf-aware-topo-model-11, 12 March 2023,
<https://datatracker.ietf.org/doc/html/draft-ietf-teas-sf-
aware-topo-model-11>.
[I-D.llc-teas-dc-aware-topo-model]
Lee, Y., Liu, X., and L. M. Contreras, "DC aware TE
topology model", Work in Progress, Internet-Draft, draft-
llc-teas-dc-aware-topo-model-02, 11 July 2022,
<https://datatracker.ietf.org/doc/html/draft-llc-teas-dc-
aware-topo-model-02>.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
10.2. Informative References
[CNTT] "Cloud Infrastructure Telco Taskforce Reference Model",
https://github.com/cntt-n/CNTT/wiki , June 2022.
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[GSMA] "Cloud Infrastructure Reference Model, Version 2.0",
https://www.gsma.com/newsroom/wp-content/
uploads//NG.126-v2.0-1.pdf , October 2021.
[Anuket] "Anuket Project", https://wiki.anuket.io/ , October 2022.
[LF-EDGE] "Linux Foundation Edge", https://www.lfedge.org/ , March
2023.
Acknowledgments
TODO acknowledge.
Authors' Addresses
Luis M. Contreras
Telefonica
Email: luismiguel.conterasmurillo@telefonica.com
Sabine Randriamasy
Nokia Bell Labs
Email: sabine.randriamasy@nokia-bell-labs.com
Jordi Ros-Giralt
Qualcomm Europe, Inc.
Email: jros@qti.qualcomm.com
Danny Lachos
Benocs
Email: dlachos@benocs.com
Christian Rothenberg
Unicamp
Email: chesteve@dca.fee.unicamp.br
Contreras, et al. Expires 14 September 2023 [Page 15]
