ALTO WG S. Yang
Internet-Draft L. Cui
Intended status: Standards Track Shenzhen University
Expires: June 1, 2020 M. Xu
Tsinghua University
H. Shen
China Telecom
L. Chen
China Mobile
November 29, 2019
Delivering Functions over Networks: Traffic and Performance Optimization
for Edge Computing using ALTO
draft-yang-alto-deliver-functions-over-networks-00
Abstract
With development of Internet of Thing (IoT), artificial intelligence,
huge amount of data are generated and need to be processed. To
satisfy the user demands, service providers are deploying edge
computing across lots of data centers, which are closer to users. In
order to achieve better performances, computing functions need to be
scheduled properly over networks. However, it is challenging to
deploy functions to the distributed edge servers efficiently due to
the lack of network traffic information. [RFC5693] and [RFC7285]
introduce and define the Application-Layer Traffic Optimization, or
ALTO, to compute and provide the network information for the
distributed applications using the ALTO protocol. In this document,
we employ the ALTO protocol to deliver functions in edge computing
platform, where the protocol will provide the network information for
the distributed edge computing servers and guide the delivery
process. The usage of ALTO will improve the efficiency of function
delivering in edge computing.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions and Terminology . . . . . . . . . . . . . . . . . 3
3. Background . . . . . . . . . . . . . . . . . . . . . . . . . 3
3.1. Edge computing . . . . . . . . . . . . . . . . . . . . . 3
3.2. Benefits of ALTO protocol . . . . . . . . . . . . . . . . 4
4. Scenario of delivering function . . . . . . . . . . . . . . . 4
5. Delivering functions over edge computing with ALTO protocol . 5
6. Implementation and Deployment . . . . . . . . . . . . . . . . 7
6.1. Implementation . . . . . . . . . . . . . . . . . . . . . 7
6.2. Deployment . . . . . . . . . . . . . . . . . . . . . . . 7
6.3. ALTO Integration . . . . . . . . . . . . . . . . . . . . 7
7. Security Considerations . . . . . . . . . . . . . . . . . . . 7
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 8
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 8
9.1. Normative References . . . . . . . . . . . . . . . . . . 8
9.2. Informative References . . . . . . . . . . . . . . . . . 8
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 8
1. Introduction
Internet of Things (IoT), artificial intelligence, virtual reality
and augmented reality (VR/AR) are developing rapidly and promising in
the future. The new applications are generating huge amount of data
that need to be processed efficiently. The emergence of edge
computing improves the performance by deploying servers at the edge,
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such that the selected servers would be closer to users, and the
latency/bandwidth between users and edge servers would be guaranteed.
Function as a service (FaaS) is becoming more and more popular among
cloud computing providers, e.g., Amazon Lambda and IBM Openwhisk.
The current FaaS platform can schedule computing resources
efficiently in a computing cluster. However, deploying functions
over distributed networks is more challenging due to the lack of
network states and information, including network traffic, topology,
and other cost metrics, etc. In this document, we will deliver
functions over the edge computing networks, to utilize the computing
and network resource more efficiently.
We use the ALTO (Application-Layer Traffic Optimization) [RFC7285] to
optimize the network traffic and performance in delivering functions
over the edge computing network. ALTO can provide global network
information and network traffic for the distributed applications,
while the information can not be retrieved or computed by the
applications themselves [RFC5693]. Generally, ALTO protocol will
collect and compute the network information for the distributed edge
clusters, including link delay, network traffic, and other cost
metrics, and help guide the deliver decision process in edge
computing. Finally, the edge computing system will deliver the
functions to the most appropriate edge clusters according to the
information by 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].
3. Background
3.1. Edge computing
The proposal of edge computing improves the edge network performance
in terms of latency, security, bandwidth, etc. In edge computing
infrastructure, servers are deployed at the edge, where the network
performance between servers and users are better. Users can submit
their tasks to the edge servers, which will process the tasks and
return the computational results to the users. Compared with
traditional centralized computing, the latency, bandwidth and network
traffic performance of edge computing is better. Nowadays, edge
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computing is used in different areas, e.g., latency-sensitive
applications such as IoT, artificial intelligence, 5G, etc.
The 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.) from FaaS providers, and avoid the complicated environment
configuration and resource management process. Developers can focus
on their business and codes rather than environment management, which
will increase the efficiency of application development and save
costs for individuals and companies.
To improve the network performance, we will deliver functions over
edge computing, such that computing functions can be dynamically
scheduled in distributed edge computing network. However, when
deploying functions to edge servers, network traffic, topology and
other metrics will influence the performance in terms of latency and
throughput. Therefore, we SHOULD consider the network traffic, and
try to optimize the network performance of the platform.
3.2. Benefits of ALTO protocol
Application-Layer Traffic Optimization (ALTO) [RFC7285] is designed
to provide network information for the distributed applications.
More specifically, the ALTO server will offer necessary network
states and information and guide the resource scheduling process for
distributed applications that can not retrieve the information by
themselves. The ALTO protocol will provide the essential network
information, including network traffic, cost map, and cost metrics,
which are 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.
In edge computing, since the edge computing clusters are distributed
in the network, they have different network states, including the
link delay and network traffic. When delivering functions, the
delivery decision SHOULD be adaptive to the network states in order
to achieve a better latency. 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 with low latency
and users can enjoy a better edge computing service.
4. Scenario of delivering function
Suppose a scenario in Internet of Things (IoT), where the
surveillance cameras are distributed, connected via the Internet and
applying for object detection computing service. When a camera
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submit a task, the objection detection function will be delivered to
an edge server that handles the task and 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
5. Delivering functions over edge computing with ALTO protocol
In edge computing platform, 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 and information for the
distributed edge clusters, and decide the appropriate edge cluster to
deploy the functions.
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More specifically, the ALTO server will collect and compute the
network cost metrics, including the link delay, availability, network
traffic, bandwidth, etc. Then the information will 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, such 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
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 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.
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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, such 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 service,
and utilize the containerization technology to run the 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 the 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.
6.2. Deployment
We have implemented a prototype, and are deploying it in real
networks across different service providers (T.B.D).
6.3. ALTO Integration
T.B.D.
7. Security Considerations
T.B.D.
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8. IANA Considerations
This document includes no requests to IANA.
9. References
9.1. 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>.
9.2. 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
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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
Hongfei Shen
China Telecom
5055, Yitan Road
Shenzhen 518000
P.R. China
Email: 13360090006@189.cn
Lu Chen
China Mobile
19, Jiefang East Road
Hangzhou 310016
P.R. China
Email: chenglu@zj.chinamobile.com
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