ALTO WG S. Yang
Internet-Draft L. Cui
Intended status: Standards Track Shenzhen University
Expires: January 14, 2021 M. Xu
Tsinghua University
Y. Yang
Tongji/Yale
R. Huang
Research Institute of Tsinghua University in Shenzhen
July 13, 2020
Delivering Functions over Networks: Traffic and Performance Optimization
for Edge Computing using ALTO
draft-yang-alto-deliver-functions-over-networks-01
Abstract
As the rapid development of the Internet, huge amounts of data are
being generated. To satisfy user demands, service providers deploy
services near the edge networks. In order to achieve better
performances, computing functions and user traffic need to be
scheduled properly. However, it is challenging to efficiently
schedule resources among the distributed edge servers due to the lack
of underlying information, e.g., network topology, traffic
distribution, link delay/bandwidth, utilization/capability of
computing servers. In this document, we employ the ALTO protocol to
help deliver functions and schedule traffic within the edge computing
platform. The protocol will provide information of multiple
resources for the distributed edge computing platform. The usage of
ALTO will improve the efficiency of function delivery in edge
computing.
Status of This Memo
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This Internet-Draft will expire on January 14, 2021.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions and Terminology . . . . . . . . . . . . . . . . . 3
3. Background . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.1. Edge computing . . . . . . . . . . . . . . . . . . . . . 4
3.2. Benefits of ALTO protocol . . . . . . . . . . . . . . . . 4
3.3. List of resources and services/functions . . . . . . . . 5
4. Scenario of delivering function . . . . . . . . . . . . . . . 6
5. Delivering functions over edge computing with ALTO protocol . 7
6. Implementation and Deployment . . . . . . . . . . . . . . . . 8
6.1. Implementation . . . . . . . . . . . . . . . . . . . . . 8
6.2. Deployment . . . . . . . . . . . . . . . . . . . . . . . 9
6.3. ALTO Integration . . . . . . . . . . . . . . . . . . . . 9
7. Management of Functions . . . . . . . . . . . . . . . . . . . 9
8. Multi-domain System . . . . . . . . . . . . . . . . . . . . . 10
9. Scheduling Framework . . . . . . . . . . . . . . . . . . . . 10
10. Security Considerations . . . . . . . . . . . . . . . . . . . 11
11. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
12. References . . . . . . . . . . . . . . . . . . . . . . . . . 12
12.1. Normative References . . . . . . . . . . . . . . . . . . 12
12.2. Informative References . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 12
1. Introduction
Internet of Things (IoT), artificial intelligence, virtual reality
and augmented reality (VR/AR) are developing rapidly, holding promise
for the future. The new applications are generating huge amounts of
data that need to be processed efficiently. The processing
applications involve kinds of functions/services according to user
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demands. For example, 1) surveillance video could be analysed by AI
functions; 2) Hi-Definition video or VR/AR video should be encoded/
decoded; 3) Content can be stored in edge networks, which can also be
seen as a function/service. Function as a service (FaaS) is becoming
more and more popular among cloud computing providers, e.g., Amazon
Lambda and IBM Openwhisk. It is expected that functions/services
would be deployed anywhere in networks.
Some of the functions/services put strong requirements on quality of
services provided by underlying networks, e.g., the delay and jitter
should be as small as possible to guarantee user experiences.
Different with Mesos and Kubernetes, which can schedule computing
resources efficiently in a computing cluster, deploy functions in
wide area networks is much more complex.
Firstly, properly deploying functions over distributed networks takes
multiple resources into considerations, including network traffic,
topology, link delay/bandwidth, computing capacity/utilization of
each computing cluster, etc. Besides, the resources are usually
scheduled across multiple domains to satisfy user demands. Thus,
these information needed to be collected with unified interfaces and
protocols, and resources scheduling algorithms SHOULD be optimized to
improve user experiences, and network performances, such as load
balancing. In this document, we will deliver functions over the edge
computing networks to utilize the computing and network resources
more efficiently.
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]. Generally, the ALTO protocol will collect and compute
network information for the distributed edge clusters, including link
delay, network traffic, and other cost metrics. Finally, based on
pre-defined scheduling algorithms, the system will deliver the
functions to the most appropriate edge clusters according to the
information provided by the ALTO protocol.
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].
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3. Background
3.1. Edge computing
Edge computing was proposed to improve network performance in terms
of latency, security, bandwidth, etc. In edge computing
infrastructure, servers are deployed at the edge to reduce the
distance between users and servers. Users can submit their tasks to
the edge servers, which will process the tasks and return the
computational results back to the users. Compared with traditional
centralized computing, the latency, bandwidth and network traffic
performance of edge computing is better. Nowadays, edge computing is
used in different areas, e.g., latency-sensitive applications such as
IoT, artificial intelligence, 5G, VR/AR, etc.
To improve network performance, we will deliver functions over edge
computing, such that computing functions can be dynamically scheduled
in a distributed edge computing network. However, when deploying
functions to edge servers, multiple resources, including bandwidth,
computing and link resources, should be allocated to meet the
requirements in terms of latency and throughput.
3.2. Benefits of ALTO protocol
Application-Layer Traffic Optimization (ALTO) [RFC7285] is designed
to provide network information for distributed applications. More
specifically, the ALTO server will offer necessary network states and
information to guide the resource scheduling process for distributed
applications, which cannot retrieve the information by themselves.
The ALTO protocol will provide the essential network information,
including network traffic, cost map, and cost metrics, which are all
necessary in the resource selection process. In this case, the
distributed applications are allowed to manage the network traffic,
and select a better path with low delay to access the network and
process the computation tasks.
Since the edge computing clusters are distributed throughout the
network, they have different network states, including link delay,
topology, network traffic, computing capacity/utilization of each
cluster, etc. When delivering functions, the scheduling decisions
SHOULD be adaptive to the network states in order to achieve better
performance. Therefore, the ALTO protocol can help manage the
network information and traffic such that the function can be
delivered to a proper edge computing cluster.
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3.3. List of resources and services/functions
Network devices, including routers, servers and clients, are able to
communicate with each other. In a realistic network, on the one
hand, we have several limited resources, including:
o Computing resource: that refers to computing powers of CPUs and
GPUs. It is noticeable that CPUs have different architectures,
e.g., ARM and x86. The properties of a CPU include the current
load, total space and available space, etc.
o Link/Path: that refers to physical and logical channels between
network devices. A link/path has the properties of bandwidth,
communication latency, etc.
o Storage: that refers to space to store the data. The property of
storage includes the amount of space to save the data.
o Radio resource: that refers to radio information in wireless
communication systems, e.g., cellular networks, wireless local
area networks. Note that in 5G network, radio resources can be
reserved by slicing technology.
On the other hand, with the development of network technology, we
have several network services and functions providing efficient
computation service for network users, including:
o Software as a service: that provides software services as a
platform. SaaS vendors deploy software services on their servers,
allowing users to purchase and use the software services.
o AI as a service: that provides artificial intelligence services as
a platform. Vendors provide different artificial intelligence-
based services for different tasks, for example, object detection
and big data analysis.
o Encoding/Decoding as a service: that provides encoding and
decoding services for high-definition and VR/AR videos.
o Function as a service: that provides function services as a
platform. Functions can be pieces of code or encapsulated docker
images. The vendors will expose the function APIs, such that
users can access the FaaS services easily. FaaS technology allows
network resources to be dynamically allocated to computing
clusters. Users can apply for function-based computation services
(including object detection, big data analysis, etc.), and avoid
the complicated environment configuration and resource management
process.
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o Content: that provides storage services as a platform. Users can
store their data in the content service, which allows users to
spare their limited local storage and retrieve the data in
different terminals.
4. Scenario of delivering function
Suppose a scenario in Internet of Things (IoT), where surveillance
cameras are connected via the Internet that apply object detection
computing services. When a camera submits a task, the objection
detection function will be delivered to an edge server that handles
the task, then returns the results to the camera. The system will
request and retrieve the network information, including link delay
and other cost metrics, by the ALTO protocols from ALTO servers and
clients. According to the information provided by ALTO, the function
and task will be delivered to the most appropriate edge server that
has the best performance from the cameras. The infrastructure is
demonstrated in Figure 1.
+---------------+ +-------------------+
| | | |
| | | |
| 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
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5. Delivering functions over edge computing with ALTO protocol
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.
+---------------+ +-------------------+
| | (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
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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.
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
6.1. Implementation
We are inspired by the concept of Serverless Computing, which is a
new computing paradigm providing function-based computing services,
utilizing containerization technology to run functions. The
container, including the running code, library, and data
dependencies, will be deployed and orchestrated to target edge
servers and clusters by container orchestrator Kubernetes (or K8S).
The container orchestration scheme will be computed according to the
network information provided by ALTO.
We use IBM OpenWhisk as the FaaS platform in edge clusters, where the
resources are managed by K8S. Using containerization technology,
functions can be flexibly delivered to the target edge server. When
a user request for function-based edge computing services, its
request will be redirected to the edge server for better performance.
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6.2. Deployment
We have implemented a prototype, and are deploying it in real
networks of Zhejiang Province, China. The initial results show that,
1) the performance of edge computing will be greatly improved with
the provided underlying network information; 2) the information
collection and scheduling policies need to be standardized to achieve
coordination among different domains.
6.3. ALTO Integration
T.B.D.
7. Management of Functions
To manage the functions more efficiently, we introduce the function
standardization in our system. More specifically, functions in our
system can be standardized, and also expose the standard APIs, such
that users can access and apply for function-based computation
services very easily. On top of them, the specific function codes
and docker images can be updated and replaced according to standards
and user demands, which is beneficial to function management of the
platform.
More specifically, function standardization consists of:
o Function repository: The repository stores all the functions for
users to apply for.
o Function registry/discovery: A service MUST be registered at the
beginning. After registry, the service information will be
broadcast to a registry server. In this case, when delivering the
functions, by accessing the registry server, the system will know
which node is registered with the function information, such that
system can determine the appropriate node to deliver the
functions.
o Function status update: When there are updates, functions in all
the network nodes MUST be updated accordingly.
Note that function standardization is beneficial to the function
delivery. By exposing the standard APIs, users can easily accomplish
their tasks by sending requests to the interfaces of the system,
bypassing the complicated resource deployment and configuration
process. Meanwhile, function standardization is good for system
management. Each function in the platform is saved and registered in
specific edge servers, such that users can easily locate the target
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edge servers when applying for functions, and system operators can
update or replace the target functions easily.
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
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,
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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.
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.
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12. References
12.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", March 1997.
[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>.
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", draft-
ietf-alto-unified-props-new-09 (work in progress),
September 2019.
Authors' Addresses
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
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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
Y.R. Yang
Yale University/PCL
51 Prospect Street
New Haven, CT 06511
United States of America
Email: yry@cs.yale.edu
URI: http://www.cs.yale.edu/~yry/
Rui Huang
Research Institute of Tsinghua University in Shenzhen
Nanshan Hi-new Technology and Industry Park
Shenzhen 518060
P.R. China
Email: xw09@tsinghua.org.cn
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