Internet DRAFT - draft-yang-alto-deliver-functions-over-networks
draft-yang-alto-deliver-functions-over-networks
ALTO WG S. Yang
Internet-Draft L. Cui
Intended status: Standards Track Shenzhen University
Expires: 22 September 2022 M. Xu
Tsinghua University
Y. Yang
Yale University
W. Xiao
Research Institute of Tsinghua University in Shenzhen
21 March 2022
Delivering Functions over Networks: Traffic and Performance Optimization
for Edge Computing using ALTO
draft-yang-alto-deliver-functions-over-networks-03
Abstract
As the rapid development of internet, massive data are produced.
Service providers typically need to deploy services near the edge
networks to better satisfy user_s demand. In order to obtain better
quality of the networks, computing functions and user traffic need to
be scheduled properly. However, it is challenging to efficiently
schedule resources among the distributed edge servers because of the
lack of network information, such as network topology, traffic
distribution, link delay/bandwidth and utilization/capability of
computing servers. In this standard, we employed the ALTO protocol
to help deliver functions and schedule traffic at the edge computing
platform. This protocol supplied information of multiple resources
for the distributed edge computing platform, thus enhancing the
efficiency of function delivery in edge computing platform.
Status of This Memo
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This Internet-Draft will expire on 22 September 2022.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions and Terminology . . . . . . . . . . . . . . . . . 3
3. Background . . . . . . . . . . . . . . . . . . . . . . . . . 3
3.1. Edge computing . . . . . . . . . . . . . . . . . . . . . 4
3.2. Features of ALTO protocol . . . . . . . . . . . . . . . . 4
3.3. Resources and services/functions . . . . . . . . . . . . 5
4. Scenario of delivering function . . . . . . . . . . . . . . . 6
5. Delivering functions by ALTO over edge computing . . . . . . 7
6. Implementation and Deployment . . . . . . . . . . . . . . . . 9
7. Management of Functions . . . . . . . . . . . . . . . . . . . 9
8. Multi-domain System . . . . . . . . . . . . . . . . . . . . . 10
9. Scheduling Framework . . . . . . . . . . . . . . . . . . . . 11
10. Security Considerations . . . . . . . . . . . . . . . . . . . 12
11. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 12
12. References . . . . . . . . . . . . . . . . . . . . . . . . . 12
12.1. Normative References . . . . . . . . . . . . . . . . . . 12
12.2. Informative References . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 12
1. Introduction
For recent years internet has been developing rapidly since it is
promising to be applied in industrial upgrading. In many scenarios
of industrial internet, massive data are produced and require to be
processed with high efficiency in real time. Typically, various
functions or services are delivered in these scenarios according to
the users_ demand. These functions or services could be (1)
surveillance videos that need analysis by AI, (2) Hi-Definition
videos that require to be encoded/decoded and (3) contents stored in
the edge network. It is noted that functions and services are
deployed widely in industrial internet. For instance, Function as a
service (FaaS) is applied and delivered more and more frequently in
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the cloud computation service for industrial internet.
Many functions and services demand high quality of the supporting
networks. For instance, the delay and jitter are expected to be as
tiny as possible in order to obtain good user_s experience.
Different from Kubernets and Mesos that are able to efficiently
schedule computing resources in a single computing cluster,
deployment of functions in wide area networks usually encounters much
more complexity.
Many resources, including network traffic, bandwidth, topology, link
delay and the computing capacity/utilization of each computing
cluster, should be taken in to considerations when functions being
deployed over distributed networks. This network status or
information requires to be collected with unified interfaces and
protocols because the resources typically need to be scheduled
crossing different domains for satisfying user_s demands. In
addition, resources scheduling algorithms SHOULD and network
performances, such as load balancing, also needs to be optimized to
improve user_s experience. In this standard, we propose an efficient
method to utilize the computing and network resources by delivering
functions over the edge computing networks.
We use the ALTO (Application-Layer Traffic Optimization) [RFC7285] to
optimize network traffic and performance by delivering functions over
the edge computing network. ALTO can provide global network
information for the distributed applications, while the information
can not be retrieved or computed by the applications themselves
[RFC5693]. Specifically, the information of network for the
distributed edge cluster, such as network traffic, link delay and
other cost metrics, is collected and computed by ALTO. Subsequently,
by using the pre-defined scheduling algorithms, functions will be
delivered by system to the most suitable edge clusters based on the
information offered by ALTO.
For brevity, in this document, we will use the terminologies
introduced in [RFC7285] and [I-D.ietf-alto-unified-props-new].
2. Conventions and Terminology
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 [RFC2119].
3. Background
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3.1. Edge computing
There are many applications and scenario of network that are very
sensitive to the quality of network. For instance, remote
controlling for machine tool typically needs enhanced broadband and
low-latency communication in real time. However, the scale of the
data generated by the equipment or sensors are usually very large (up
to be hundreds of GB per day). In addition, the data produced from
various equipment will require support of heterogeneous computing.
In this case, uploading these data to the centralized cloud
computation platform is difficult, expensive but not so useful.
Similarly, delivering functions or services from the cloud
computation platform to the local equipment is also hard and the
real-time performance will not be guaranteed. Alternatively, new
framework of computation and network SHOULD be adopted to address
these problems that hinder the edge computation to acquire broader
promotion.
Edge computing was designed to enhance the quality of network,
including the packet loss, security, bandwidth and latency. In order
to decrease the distance between the servers and users, people
typically will deploy servers at the edge nearing the users in the
edge computing infrastructure. In this framework, User_s tasks can
be submitted to the edge servers that will handle with the tasks
close to the user and return the computation output back to the user
promptly. As a consequence, the latency, bandwidth and network
traffic performance of edge computing will be better than that of the
centralized cloud computing. With these advantages, edge computing
has been applied in various applications in network, such as AI
supporting HD videos and VR/AR.
In this standard, functions and services will be delivered over the
edge computing so that we can schedule the computing functions
dynamically in a distributed edge computing network to enhance the
performance of network. Nevertheless, it should be noted that during
the deployment of the functions to edge servers, there are multiple
resources, such as bandwidth, computing and link resources, that
SHOULD be allocated to satisfy the requirements in terms of latency
and throughput.
3.2. Features of ALTO protocol
Application-Layer Traffic Optimization (ALTO) [RFC7285] is designed
to provide network information for distributed applications.
Specifically, the resource scheduling process for distributed
applications will be guided by the network states and information
that is provided by the ALTO server. Otherwise, the information will
fail to be retrieved by the applications. In this case, much
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essential network information in the resource selection process, such
as network traffic, cost map, and cost metrics, will be offered by
the ALTO protocol. As a consequence, network traffic can be managed
by the distributed applications. In addition, they could make better
choice in selecting the path that contains lower delay to access the
network and handle with the computation tasks.
Because of being distributed over different areas of network, the
edge computing clusters would comprise various network states, such
as topology, network traffic, link delay and the computing capacity/
utilization. Generally, in order to obtain better performance, the
scheduling decisions require to be adaptive to the network states
during the delivering of functions. Thus, the network information
and traffic can be managed by ALTO according to its mentioned
advantage. As a consequence, functions and services will be
delivered to a proper edge computing cluster.
3.3. Resources and services/functions
Generally, we have both limited resources and massive network
services/functions on the network. Some common limited resources are
as below.
* Computing resource: it usually represents the computing powers of
CPU or GPU. CPUs have different architectures, including ARM and
x86. The power of a CPU is affected by the working status, such
as current load, total space and available space.
* Link/Path: They are a physical or logical communication channel
between network devices, such as routers, servers and clients.
Properties of a link or path include hop count, bandwidth and
communication latency.
* Storage: that refers to space to store the data. The property of
storage includes the amount of space to save the data.
* Radio resource: It means the radio information in wireless
communication systems, such as cellular networks and wireless
local area networks. In 5G network, radio resources can be
combined with slicing technology to provide customized network
property for user_s differentiated network demand.
Except the limited resources above, as the network technology rapidly
develops, there are many network services and functions that could
supply abundant computation service to users.
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* Software as a service: It supplies software services as a
platform. Software services, such as wechat and Alipay, are
deployed on the SaaS vendors_ servers, for users to access to and
use.
* AI as a service: it supplies artificial intelligence services as a
platform. Various artificial intelligence-based services, such as
face recognition, speech recognition and big data analysis, are
provided by vendors.
* Encoding/Decoding as a service: it supplies encoding and decoding
services for high-definition videos and VR/AR videos. It has been
widely applied in areas of smart city and online entertainment.
* Function as a service: it supplies function services as a
platform. Functions can be encapsulated docker images or pieces
of code. Usually, in order to let user access to FaaS easily and
conveniently, the vendor will expose the function APIs. One of
the main features of FaaS is allowing network resources to be
dynamically allocated to computing clusters. Therefore, users do
not need to handle with the complicated environment configuration
and resource management process, as they can utilize the function-
based computation services, such as face recognition, speech
recognition and big data analysis.
* Content: it supplies storage services as a platform. Utilizing
the content service, users can store their data such that users
could spare their limited local storage. Meanwhile, users can
retrieve the data from different terminals.
4. Scenario of delivering function
Suppose a scenario in IoT, in which unmanned aerial vehicle (UAV) are
connected via the network to apply for the face recognition computing
services. When a UAV submits a task, the face recognition function
will be delivered to an edge server to process the task. Then the
recognition results will be returned to the UAV. During this
process, network information, such as link delay and other cost
metrics, would be requested and retrieved by the ALTO protocols from
ALTO servers and clients in the network system. According to the
information supplied by ALTO, the function and task will be delivered
to the most suitable edge server that would provide best performance
to the UAVs. The schematic diagram of this framework is shown in
Figure 1 below.
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+---------------+ +-------------------+
| | | |
| | | |
| ALTO Server |<---------------->| ALTO Client |
| | | |
| | | |
+---------------+ +------^-----+------+
| |
| |
| |
+--+-----v--+
| Cluster |
+-------+ Client +------+
| +-----------+ |
| |
| |
| |
+------v-------+ +-------v------+
|Edge Computing| |Edge Computing|
| | ...... | |
| Cluster 1 | | Cluster N |
+--------------+ +--------------+
Figure 1. Scenario of delivering function over edge network in IoT
5. Delivering functions by ALTO over edge computing
Since lots of edge clusters and servers are distributing in the
network, the system MUST handle the huge amount of edge devices and
their corresponding network traffic. A cluster client is employed to
manage the connectivity and traffic information of the distributed
edge clusters. The ALTO client will communicate with the cluster
client and provide the necessary network information. The usage of
ALTO is to optimize the network traffic and guide the function
delivering process in edge computing. It will provide the overall
network states with information for the distributed edge clusters,
and decide the appropriate edge cluster to deploy the functions.
More specifically, the ALTO server will collect and compute the
network cost metrics; including the link delay, availability, network
traffic, bandwidth, and etc. The information will then be sent to
the ALTO client. The ALTO client will select the target appropriate
edge clusters to deploy the target function. Finally, the system
will connect and deploy the function to the target servers, so that
users can submit their computation task to the selected edge
clusters.
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+---------------+ +-------------------+
| | (1) Network | |
| | Information | |
| ALTO Server |<---------------->| ALTO Client |
| | | |
| | | |
+---------------+ +------^-----+------+
| |
(2)Get clusters | | (3)Select Cluster List
| |
+--+-----v--+
| Cluster |
+-------+ Client +------+
| +-----------+ |
| |
| (4) Connect to Cluster |
| and deliver function |
+------v-------+ +-------v------+
|Edge Computing| |Edge Computing|
| | ...... | |
| Cluster 1 | | Cluster N |
+--------------+ +--------------+
Figure 2. Delivering process in edge computing platform with ALTO
Figure 2 illustrates the infrastructure and function delivering
process of the edge computing platform.
1. The ALTO client requests the information, such as network map
and cost map of distributed edge clusters from the ALTO server, by
using ALTO protocol.
2. The Cluster Client requests an edge cluster list of the
network.
3. The ALTO Client returns the edge cluster list and
corresponding resource information about the clusters computed by
ALTO servers according to the network state.
4. The Cluster Client connects and delivers function to the
corresponding edge computing cluster according to the information,
and the cluster will process and return the computation results to
users.
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Note that the data transfer process is using the ALTO protocol
described in [RFC7285] to guarantee the efficiency and security of
the delivering process. In this case, the edge computing clusters
are allowed to retrieve the network information, so that the function
can be delivered to the proper ones to achieve a better performance
in terms of latency, throughput, etc.
6. Implementation and Deployment
We have implemented a prototype, where the edge cluster resources are
managed by K8S and Docker. When users request for edge computing
services, the appropriate edge cluster will be selected by ALTO
according to network map and cost information.
We have deployed the prototype in real network in the China Mobile
network. The preliminary results of our deployment show that 1) the
performance of edge computing will be greatly improved by using the
supplied network information and 2) collection and scheduling
policies of this information need to be standardized to obtain
coordination among different domains.
7. Management of Functions
As function standardization in our system could be useful in managing
functions efficiently, We will introduce this technique as below.
Our system can standardize the functions and expose standard APIs for
users to easily access to and apply for function-based computation
services. Above the function-based computation services, certain
function codes and docker images can be updated and replaced based on
the user_s demand or standard upgrade. This would be useful to the
function management of the platform. Specifically, function
standardization is composed of several steps below.
Specifically, function standardization consists of:
* Function repository: Generate the repository that stores all the
functions for users to apply for.
* Function registry/discovery: A service MUST be registered before
being applied for. After the registry, the information of service
will be broadcast to a registry server. In this circumstance,
when delivering the functions, the system will recognize which
node is registered with the function information by accessing to
the registry server. As a consequence, appropriate node can be
determined by our system in order to deliver functions with high
efficiency.
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* Function status update: When there are updates, functions in all
the network nodes MUST be updated accordingly.
As function standardization is powerful in delivering function, users
can conveniently process their tasks by sending requests to the
interfaces of the system in which the standard APIs are exposed by
the process of function standardization. As described above, this
mechanism could help users to bypass the complexity of resource
deployment and configuration. In addition, the function
standardization is also useful in managing the system, because system
operators can update or replace the target functions more
efficiently. When users applying for functions, they can easily
locate the target edge servers since each function on the platform is
saved and registered in certain c edge servers before.
8. Multi-domain System
A function delivery platform can be a multi-domain system. For
example, there may be multiple service providers offering the
function-based computation service. In this case, we should consider
how to collect and manage the network information from different
domains, in order to achieve better function delivery performance in
networks. Consequently, we SHOULD develop additional designs for our
platform.
On the one hand, we introduce the layered design for function
delivery. More specifically, we deploy multiple distributed registry
servers in the lower layer, each of which processes the function
registry in its domain. Then we deploy a centralized registry server
in the upper layer to collect and manage the distributed registry
servers in the lower layer. A server in the lower layer will report
and send network information of its domain to the centralized server
in the upper layer periodically. And the centralized server will
coordinate the domains by sending instructions to the distributed
servers in the lower layer, which will make adjustment according to
the instructions of the centralized registry server. In this case,
the centralized registry server is able to manage the distributed
function and network information easily and efficiently, which is
beneficial to multi-domain system management.
On the other hand, we introduce the policy management for multiple
domains. Note that different domains MAY have various delivery
policies, thus we need to provide a policy management tool for
multiple domains. When delivering functions in a multi-domain
system, the tool will provide the overall management policy to
synchronize and coordinate the distributed local policies in each
individual domain. In this case, the distributed multiple domains in
different policies are able to communicate and coordinate with each
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other, with the help of the policy management tool. Therefore, by
utilizing the policy management tool, we can manage the multiple
domains for efficient function delivery.
9. Scheduling Framework
Recently, with the development of high-capacity computing devices,
the computing power of networks has improved much. However, due to
the lack of efficient scheduling strategies, the current computing
platforms cannot achieve better computing throughput, i.e., the
ability to schedule the distributed computing power over a long
period. To improve the scheduling efficiency of the computing power,
researchers proposed some high-throughput computing scheduling
frameworks, for example, HTCondor, PBS, CPUsage, etc., which are able
to schedule the limited distributed computing power to achieve better
throughput of the network in a long period. Inspired by the high-
throughput computing scheduling frameworks, we develop the scheduling
framework for function delivery, in order to achieve better
performance of networks.
The objective of our scheduling framework for function delivery is to
minimize the computational latency. The basic idea is, our platform
will compute the function scheduling schemes, according to the
information collected by the ALTO server, including the network
congestion, resource utilization, etc. The users will access the
most appropriate edge server, which will provide the function-based
computation service and return the results to the users.
More specifically, when a user applies for the function delivery
service, it will send requests to the interface provided by the ALTO
server, along with its location and task information. The ALTO
server will also collect the resource utilization and network
information of the decentralized edge servers. Then, according to
the collected information, the ALTO server will compute the function
scheduling scheme, to determine the function delivery destination of
a specific edge server. The platform will select the edge server
with lowest computation latency for user. However, if the selected
edge server is overloaded, the platform will proceed to search other
edge server that satisfies the load balance demand, along with
achieving considerable latency performance. Finally, the user will
establish the communication channel with the target edge server,
which will provide the function-based service and return the results
to the users.
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By developing the scheduling framework and strategy for function
delivery, our platform can maintain the stable network condition and
guarantee the load balance over a long period, which is beneficial to
the reliability of system. And users can enjoy a low-latency and
high-throughput function delivery service at the same time.
10. Security Considerations
T.B.D.
11. IANA Considerations
This document includes no requests to IANA.
12. References
12.1. Normative References
[RFC5693] Seedorf, J. and E. Burger, "Application-Layer Traffic
Optimization (ALTO) Problem Statement", RFC 5693,
DOI 10.17487/RFC5693, October 2009,
<https://www.rfc-editor.org/info/rfc5693>.
[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>.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", March 1997.
12.2. Informative References
[I-D.ietf-alto-unified-props-new]
Roome, W., Randriamasy, S., Yang, Y., Zhang, J., and K.
Gao, "Unified Properties for the ALTO Protocol", Work in
Progress, Internet-Draft, draft-ietf-alto-unified-props-
new-09, 4 September 2019, <http://www.ietf.org/internet-
drafts/draft-ietf-alto-unified-props-new-09.txt>.
Authors' Addresses
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Shu Yang
Shenzhen University
South Campus, Shenzhen University
Shenzhen
518060
P.R. China
Phone: +86-755-2653-4078
Email: yang.shu@szu.edu.cn
Laizhong Cui
Shenzhen University
South Campus, Shenzhen University
Shenzhen
518060
P.R. China
Phone: +86-755-8695-6280
Email: cuilz@szu.edu.cn
Mingwei Xu
Tsinghua University
Department of Computer Science, Tsinghua University
Beijing
100084
P.R. China
Phone: +86-10-6278-5822
Email: xumw@tsinghua.edu.cn
Richard Yang
Yale University
51 Prospect St
New Haven, CT, 06511
United States of America
Email: yry@cs.yale.edu
Wei Xiao
Research Institute of Tsinghua University in Shenzhen
Nanshan Hi-new Technology and Industry Park
Shenzhen
518060
P.R. China
Email: xiaow@tsinghua-sz.org
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