Internet DRAFT - draft-liu-alto-can-usecase
draft-liu-alto-can-usecase
Application-Layer Traffic Optimization P. Liu
Internet-Draft Y. Fu
Intended status: Informational China Mobile
Expires: 24 July 2022 20 January 2022
Computing-aware Networking Use case of ALTO
draft-liu-alto-can-usecase-01
Abstract
The ever-emerging new services are imposing more and more highly
demanding requirements on the network. In order to meet these
requirements, some new technology trends of network emerge as the
times require. On the one hand, for the selection of service node
and forwarding path, in addition to considering the network topology
and link state, more factors are also considered, such as the
computing properties of service node; On the other hand, network and
application present the trend of mutual perception, including
application to perceive the state of network path, or network to
perceive the demand of application.
This draft describes a new network scenario and architecture
considering computational properties, and assumes that Alto could be
used as an important node to realize the deployment of services, and
to assist in the selection of service nodes.
Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
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."
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This Internet-Draft will expire on 24 July 2022.
Copyright Notice
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Usage Scenarios of CAN . . . . . . . . . . . . . . . . . . . 4
2.1. AR/VR . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2. Internet of Vehicles . . . . . . . . . . . . . . . . . . 5
3. CAN Framework and ALTO . . . . . . . . . . . . . . . . . . . 5
4. Deployment of CAN with ALTO . . . . . . . . . . . . . . . . . 7
5. Scheduling of CAN with ALTO . . . . . . . . . . . . . . . . . 8
6. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
7. Security Considerations . . . . . . . . . . . . . . . . . . . 10
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 10
9. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 10
10. Informative References . . . . . . . . . . . . . . . . . . . 10
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 11
1. Introduction
For new services with heavy computing tasks, such as AR/VR, video
recognition and so on, the computing time and network transmission
delay are almost the same order of magnitude. In this kind of
scenario, computing attributes become more important than traditional
services.
The generation of edge computing is to solve such problems. Edge
computing is to deploy service nodes close to the user side, which
shortens the distance of network transmission. Moreover, it can
deploy specific computing resources, such as CPU/GPU, to meet the
needs of different services.
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It is predicted by Gartner that by 2025, more than 50% of the
computing data needs to be analyzed, processed, and stored at the
edge. Since the service providers begins to offer the edge computing
infrastructure to provide better response time and transfer rate.
There are also some challenges of edge computing itself, which are
pointed out in the work of dyncast [draft-liu-dyncast-ps-usecases-
01], [draft-li-dyncast-architecture-00] :
* Geographically Scattered Large Number of Edge Sites. The edge
sites are highly distributed and may not have proximate distances to
user.
* Resource Limitation. There are fewer servers of server per node.
* Heterogeneous Hardware, such as CPU, GPU, Memory, ASICs.
* Dynamic Load. The available resources may change quickly.
* Edge-cloud Coordination. Edge does not solve all requests.
* High Cost. On-site maintenance is expensive.
* Mission Critical. Users are counting on you for 100% reliability
of industry automation.
So how to collaboratively deploy and computing services based on the
computing resources in network to meet the diverse computing
requirements, and achieve the on-demand allocation and dispatch of
service request needs be studied.
Some existing works have begun to consider the computing attributes.
For example, coinrg initiated the consideration of computing and
storage resources. Dyncast
[I-D.liu-dyncast-ps-usecases][I-D.li-dyncast-architecture] proposed
how to introduce the scheme of computing metric at the routing level.
Semantic routing[], which also extends the semantics of routing in a
broad sense. However, today's routing system and technology has been
relatively good, the introduction of more metric in routing still
need more theoretical and experimental verification. In the work of
ITU, it is more from the perspective of architecture, such as ITU
Y.CAN-reqts [Y.CAN-reqts: "functional requirements of Computing-aware
Networking"] proposed a new network architecture-computing-aware
networking (CAN), CAN schedules service request to the optimal
computing site along optimal path to meet service requirements both
on network and computing. ITU.Y.CPN-arch [Y.CPN-arch: "Framework and
architecture of Computing power Network"]provides the framework and
architecture of Computing power Network, specifies its functional
entities and defines the functionalities of these functional
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entities. So the convergence of network and computing brought by
edge computing includes the issue of service deployment and service
request scheduling. ALTO has done the work of collect the network
information for application, it may help to do some work in the two
important issues:
How to deploy service nodes based on computing resources. For this
point, [draft-contras-alto-service-edge-02] gives the corresponding
idea of using Alto to deploy edge computing nodes. Alto can better
interact with the upper application, fully understand the
requirements of the application, including computing requirements and
collect the information of infrastructure resources.
How to select the most suitable node for service request. Alto can
also help this kind of work to a certain extent. Centralized
selection of service nodes and paths is relatively easy to implement,
such as SDN. However, emerging service requests require high real-
time performance, and there may be some efficiency and complexity
problems, which have been analyzed in the work of dyncast.
2. Usage Scenarios of CAN
Any multi-point deployment service that requires high computing power
or low latency will involve the joint scheduling of network and
computing resources.
2.1. AR/VR
Take the AR/VR as an example. The upper bound latency for motion-to-
photon (MTP) is less than 20 milliseconds to avoid the motion
sickness. It consists of four parts, and the frame rendering
computing delay is 5.5 milliseconds, so the network delay demand can
be calculated as 5.1milliseconds.
+-----------------------+---------------------+
| Total MTP delay | 50ms |
+-----------------------+---------------------+
| sensor sampling delay | <1.5ms |
+-----------------------+---------------------+
| display refresh delay | 5 ms |
+-----------------------+---------------------+
| frame rendering delay | 5.5ms |
| computing with GPU | |
+-----------------------+---------------------+
| network delay | 20-1.5-5.5-7.9=5.1ms|
+-----------------------+---------------------+
Figure 1: Delay of MTP
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So the budgets for computing delay and network delay are almost
equivalent. Considering another factor that the computing resources
have a big difference in different edges,it is necessary to apply
dynamically steer traffic to the 'best' edge.
2.2. Internet of Vehicles
Under the scenario of Internet of Vehicles, the services are divided
into auxiliary driving and on-board entertainment services . For the
auxiliary driving function, for road traffic conditions outside the
line of sight due to obstructions, blind areas, etc., the edge
computing node obtains comprehensive road traffic information around
the location of the vehicle, performs unified data processing, and
sends out warning signals for vehicles with potential safety hazards,
to assist the safe driving of vehicles.
Apparently, there are obviously differences between services
requirements of auxiliary driving services and entertainment
services, which needs to be processed by different edge nodes
3. CAN Framework and ALTO
In order to realize ubiquitous computing and service awareness,
interconnection and collaborative scheduling, the computing-aware
networking architecture can be divided into computing service layer,
computing resource layer, computing routing layer, network resource
layer, and computing and network management layer. Among them, the
computing routing layer contains the control plane and data plane
which is shown in Figure 2. Based on the ubiquitous computing
resources of the network, the computing resource layer abstracts and
models based on a unified measurement and modeling template, and
announces computing information to the computing routing layer. And
then the computing routing layer comprehensively considers user needs
and network resource status and computing resource status, to
schedule service requests to appropriate computing nodes. In
addition, the computing management layer completes the control and
management of computing resources.
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Computing-aware Networking Framework
+---------------------------------------+ +------------------------+
| Computing Service Layer |<->| Computing and Network |
+---------------------------------------+ | Management Layer |
| Computing Resource Layer |<->|+----------------------+| +---------+
+---------------------------------------+ || Service Orchestration|<--------| |
| Computing-aware Routing Layer | |+----------------------+| | |
| +---------------+ +---------------+ |<->|| Computing OAM |<--------| |
| | Control Plane | | Data Plane | | |+----------------------+| | ALTO |
| +---------------+ +---------------+ |<->|| Routing Management |<--------| |
+---------------------------------------+ |+----------------------+| | |
| Network Resource Layer |<->|| Resource Management |<--------| |
+---------------------------------------+ |+----------------------+| +---------+
Figure 2: CAN Framework and ALTO
* Computing service layer: computing service layer is computing
service provider, which deploys, operates and manages many kinds of
computing services and applications. In addition, it can realize the
functions of service decomposition and service scheduling through the
API gateway.
* Computing resource layer: it is based on the existing computing
infrastructure, and includes a combination of computing resources
from single-core CPU to multi-core CPU, CPU+GPU+FPGA, etc. And it
could supply computing modeling function, computing API function,
computing resource identification and other functions to meet the
diverse computing needs of different applications based on physical
computing resources.
* Computing-aware routing layer: It contains control plane and data
plane, performs computing-aware routing and generates service
scheduling policy in network layer. Based on the discovery of
abstracted computing and network resources, computing routing layer
generates new routing tables which include the information of
computing in network, considers the state of network and computing
comprehensively, and thus generates routing policy for different
service requests. Network resource layer: It utilizes the existing
network infrastructure, which includes access network, metropolitan
area network and backbone network, to provide ubiquitous network
connection.
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* Computing and network management layer: It adds management
functions towards computing resources and computing services based on
the traditional network management function. Therefore, the
computing and network management layer performs service
orchestration, resource management, routing measurement and computing
OAM. In addition, the computing and network management layer could
be used to perform the pre-configuration function and management
function, which interacts with each functional layer.
* Network resource layer: using the existing network infrastructure
to provide network connection, network infrastructure includes access
network, metropolitan area network and backbone network.
Based on the five functional modules defined above, computing-aware
networking can realize the awareness, control and scheduling of
computing and network resources, and further perform dynamic and on-
demand service scheduling. The function of computing and network
management layer may be realized by Alto or by opening interface with
Alto. Considering a single ALTO Client as part of the Computing and
Network Management Layer aggregating all the requests towards the
ALTO Server, this also could decouple the solution from a specific
CAN architecture.
4. Deployment of CAN with ALTO
With the development of edge computing, there is multiple services
duplication deployed in different edge nodes. To improve the
effectiveness of service deployment, the problem of how to choose
optimal edge node to deploy services needs to be solved. More stable
static information should be considered in service deployment,
[I-D.contreras-alto-service-edge]introduces the consideration of
depoly applications or functions to the edge, such as the type of
instance, interface option associated bandwidth of the network
interface, compute flavor of CPU/GPU, etc, optional storage
extension, optional hardware acceleration characteristics.
Besides those, more network and service factors may to be considered
to meet the CAN Framework, such as
* Network and computing resource topology: the overall consideration
of network access, connectivity, path protection or redundancy. and
the location and overall distribution of computing resources in
network, and the relative position towards network topology.
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* Location: the number of users brought, the differentiation of
service types and number of connections requested by users, etc. For
edge nodes located in popular area, which with large amount of users
and service requests, the service duplication can be deployed more
than other areas.
* Capacity of multiple edge nodes: not only a single node, but also
the total number of requests that can be processed by the resource
pool composed of multiple nodes
* Service category: For example, whether the business is multi-user
interaction, such as video conferencing, games, or just resource
acquisition, such as short video viewing Alto can help to obtain one
or more of the above information, so as to provide suggestions or
formulate principles and strategies for service deployment.
For the collection of those information, seconds level or minutes
level frequency is enough, while serious real-time processing isn't
necessary. For example, periodically collecting the total
consumption of computing resources, or the total number of sessions
accessed, to notify where to depoly more VMS or containers. Unlike
the scheduling of request, service deployment should still follow the
principle of proximity. The more local access, the more resources
should be deployed. If the resources are insufficient, the operator
can be informed to increase the hardware resources. Alto can be used
to collect and reprot thoes information.
5. Scheduling of CAN with ALTO
Compared to the deployment, scheduling needs to consider more dynamic
information to select and adjust the optimal (rather than the
shortest) path in real time, such as:
* Mobility: CAN schedules service request to the optimal service node
among several service nodes near to users. So when user mobiles, the
nearby service nodes changes and new scheduling are needed to chooses
new path and new service node.
* Real time delay of network: network delay is always in the process
of dynamic change, and more and more services propose strict time
requirements, which is one of the most important factors affecting
user experience.
* Real time status of computing resources: computing resources change
frequently and the status of computing resources heavily affect the
completion time of service, which is also one of the most important
factors affecting user experience.
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* Comprehensive status of network status and computing status: the
update frequency of computing status and network status is different,
it is necessary to generate a comprehensive value to reflect the
status of them. Collecting the information from multi-protocols will
bring the issue of synchronization, which is not easy and cause some
additional expenses. (If the network is deterministic network that
support synchronization such as detnet, it will be better.) One
protocol may be the right way.
* Various service requirements: different services propose different
service requirements on computing and network, including bandwidth,
latency, computing resources etc, and the latency includes both
transmission latency in network and processing latency in service
node, for transmission-intensive services, the transmission latency
accounts a lot , so the network latency of services are more
important.
* Availability of network or computing resources: such as temporary
unavailability caused by network equipment or service node failure.
Alto can still help collect network and service node information,
* Providing the best choice of network and service nodes. Based on
the collected network information, computing information, and service
requirements on network and computing, Of course, there are still
some real-time and complexity problems.
* Providing data analysis and policy distribution, real-time node
selection still depends on distributed routing, such as dyncast.
[I-D.contreras-alto-service-edge]introduces how to use BGP to realize
it. BGP is an option where [RFC7971]shows BGP prefixes, AS numbers,
AS distances, or other BGP metrics could be collected. However, The
ALTO service may not know the instantaneous congestion status of the
network, all link bandwidths, all information about the actual
routing and whether the candidate endpoint itself is overloaded. So
it may not be satisfied for the application with very real time
requirements. "real-time" could be relative and may affect the result
of scheduling. If it is good, the scheduling can be more accurate
and better meet the application requirements; If the real-time
performance is not good, the network or node state may change when
the flows arrive, resulting in the demand can not be met. So how to
improve the performance of BGP or announce the corresponding
information through other ways need to be research.
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6. Summary
The converge of network and computing, as well as the interaction
with applications has become one of the current technical development
directions. This draft analyzes the development status of the
current computing aware network and the functional modules in its
architecture that can interact with Alto. As a protocol to connect
networks and applications, Alto may play a more important role.
7. Security Considerations
TBD.
8. IANA Considerations
TBD.
9. Acknowledgements
Thanks to Yizhou Li, Qin Wu, Tianji Jiang for helpful suggestion.
Thanks go to Dirk Trossen, Luigi Iannone, and Carsten Bormann for
their inspiring Dyncast work.
10. Informative References
[I-D.contreras-alto-service-edge]
Contreras, L. M., Lachos, D. A., Rothenberg, C. E., and S.
Randriamasy, "Use of ALTO for Determining Service Edge",
Work in Progress, Internet-Draft, draft-contreras-alto-
service-edge-03, 12 July 2021,
<https://www.ietf.org/archive/id/draft-contreras-alto-
service-edge-03.txt>.
[I-D.li-dyncast-architecture]
Li, Y., Iannone, L., Trossen, D., and P. Liu, "Dynamic-
Anycast Architecture", Work in Progress, Internet-Draft,
draft-li-dyncast-architecture-00, 15 February 2021,
<https://www.ietf.org/archive/id/draft-li-dyncast-
architecture-00.txt>.
[I-D.liu-dyncast-ps-usecases]
Liu, P., Willis, P., and D. Trossen, "Dynamic-Anycast
(Dyncast) Use Cases & Problem Statement", Work in
Progress, Internet-Draft, draft-liu-dyncast-ps-usecases-
01, 15 February 2021, <https://www.ietf.org/archive/id/
draft-liu-dyncast-ps-usecases-01.txt>.
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[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>.
[RFC7971] Stiemerling, M., Kiesel, S., Scharf, M., Seidel, H., and
S. Previdi, "Application-Layer Traffic Optimization (ALTO)
Deployment Considerations", RFC 7971,
DOI 10.17487/RFC7971, October 2016,
<https://www.rfc-editor.org/info/rfc7971>.
Authors' Addresses
Peng Liu
China Mobile
Beijing
100053
China
Email: liupengyjy@chinamobile.com
Yuexia Fu
China Mobile
Beijing
100053
China
Email: fuyuexia@chinamobile.com
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