Application-Layer Traffic Optimization P. Liu
Internet-Draft Y. Fu
Intended status: Informational China Mobile
Expires: January 12, 2022 July 11, 2021
Computing-aware Networking Use case of ALTO
draft-liu-alto-can-usecase-00
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
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provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on January 12, 2022.
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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 . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
7. Security Considerations . . . . . . . . . . . . . . . . . . . 9
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 9
9. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 9
10. Normative References . . . . . . . . . . . . . . . . . . . . 9
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 10
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 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 entities. So the
convergence of network and computing brought by edge computing
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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|
+-----------------------+---------------------+
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 |<--------| |
+---------------------------------------+ |+----------------------+| +---------+
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.
* 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
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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 server.
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, such
as:
* Network topology: the overall consideration of network access,
connectivity, path protection or redundancy
* The topology of computing resources: including the location and
overall distribution of computing resources in network, and the
relative position towards network topology.
* Types of computing resources of edge nodes: such as CPU / GPU, etc
* Location: the number of users brought, the differentiation of
service types requested by users, etc
* Location of edge nodes: 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: different types of services require different
computing resources. It's necessary to consider the e category of
computing resources required by the services to deploy services. For
example, whether the business is multi-user interaction, such as
video conferencing, games, or just resource acquisition, such as
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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, second level or minute 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 transmit 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 timesuch 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.
* 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.
* 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.
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Availability of network or computing resources: such as temporary
unavailability caused by network equipment or service node failure.
Alto can still help collect real-time 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.
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. Normative References
[I-D.li-dyncast-architecture]
Li, Y., Iannone, L., Trossen, D., and P. Liu, "Dynamic-
Anycast Architecture", draft-li-dyncast-architecture-00
(work in progress), February 2021.
[I-D.liu-dyncast-ps-usecases]
Liu, P., Willis, P., and D. Trossen, "Dynamic-Anycast
(Dyncast) Use Cases & Problem Statement", draft-liu-
dyncast-ps-usecases-01 (work in progress), February 2021.
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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>.
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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