Internet DRAFT - draft-yu-alto-arch-metro-computing-optical-network
draft-yu-alto-arch-metro-computing-optical-network
alto Working Group TK.Yu, H.Yang, ZJ.Sun, QY.Yao
Internet-Draft Beijing University of Posts and Telecommunications
Intended status: Standards Track Y.Zhao,S.Liu,YB.Li
Expires: 06 March 2024 China Mobile Research Istitute
06 September 2023
Architecture of Metro Computing Power Optical Network
draft-yu-alto-arch-metro-computing-optical-network-00
Abstract
This document describes the architecture of metro computing
power optical network.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Requirements Language . . . . . . . . . . . . . . . . . . 3
2. Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1. Network Resource Acquirement . . . . . . . . . . . . . . . 3
3. The architecture of Metro Optical Computing Power Network . . . 4
3.1. Edge computing power management platform . . . . . . . . . 4
3.2. Traffic scheduling management platform . . . . . . . . . . 4
4. Manageability Considerations . . . . . . . . . . . . . . . . 4
5. Security Considerations . . . . . . . . . . . . . . . . . . . 4
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 4
7. References . . . . . . . . . . . . . . . . . . . . . . . . . 5
7.1. Normative References . . . . . . . . . . . . . . . . . . 5
7.2. Informative References . . . . . . . . . . . . . . . . . 5
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 5
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 5
1. Introduction
The emergence of novel computing services, such as large-scale models
and virtual reality, has introduced considerable challenges to
communication networks due to their substantial data transmission and
computational demands. These challenges are particularly pronounced
within metropolitan networks. Heterogeneous computing devices pose
difficulties in uniform measurement scheduling, accurately pinpointing
diverse user computing intents, and handling scattered datasets,
thereby hindering effective information utilization. These issues
underscore the necessity to establish real-time transmission
connections with significant bandwidth capacity.
The metropolitan optical network, grounded in Optical transport Network
(OTN) devices, facilitates optical connections and configures a
three-layer hierarchical topology spanning access, aggregation, and
core metro nodes. This architecture offers a favorable framework for
the judicious allocation of computating power to individual users in
accordance with service requirements. Optical networks expand the scope
of computing networks, thereby affording more expansive avenues for
implementing flexible and efficient scheduling strategies, optimizing
the utilization of computating resources. This flexibility supports
the deployment of ultra-large capacity, ultra-low latency, and highly
efficient computing services.
The architecture of the metropolitan optical computing power network
facilitates diverse applications, encompassing computing power network
measurement, perception, traffic prediction and control, as well as
anomaly detection. Businesses seeking computing services can harmonize
computing power and network resources to offer an optimized user
experience. This architectural framework synergizes computational
prowess with the metro optical network, facilitating collaborative
resource allocation at the network edge.
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1.1. Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in BCP
14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
2. Scenarios
With the prevalence of cloud services, enterprise services and other
services, the architecture of computing power optical network has
become the choice to solve supported services. The following scenarios
provide some typical applications.
2.1. Network Resource Acquirement
The network traffic scheduling platform receives computing service
information from clients, acquires comprehensive user data, and
forwards it to the edge computing management platform for demand
analysis. Subsequently, the scheduling platform conducts bandwidth
resource forecasting and traffic scheduling based on these requirements.
3. The architecture of Metro Computing Power Optical Network
----------------------------------------------------------------------------
| --------------------------- ---------------------------- |
| | Edge computing power | | Traffic scheduling | |
| Metro | management | | management | |
| optical | ----------------------- | | ---------------------- | |
| computing | | CP cooperative process| | | | Service priorities | | |
| power | ----------------------- |..| -.--------------.----- | |
| network | ----------------------- | | -.------- ----.----- | |
| management | | CP intent mapping | | | | Traffic | | Routing | | |
| | ----------------------- | | | forecast| | and | | |
| | ----------------------- | | | based | | spectrum | | |
| | | CP state perception | | | | on AI | |assingment| | |
| | ----------------------- | | ---.----- -------.-- | |
| | ----------------------- | | ---.---------------.-- | |
| | | CP measurement | |..| | Service Interference | | |
| | ----------------------- | | ---------------/--/--- | |
| --------------------------- -----------------/--/------- |
-------------------------------------------------------------/--/-----------
- Reporting edge computing power service information / / Scheduling
-----------------------------------------------------------/--/-- scheme
| metro backbone |
| Optical OE ------------------ OE |
| layer / \ / |
| resources / \ metro core / |
| OE - OE----------- OE |
| / / \ -- / / \ |
| -- / \ / / \ |
| / OE OE OE OE OE |
| OE . . metro aggregation . |
-----------------.----.-----.-------------.------.-------.-------
- . . . . . .
-----------------.----.-----.-------------.-------.-------.------
| . . . . . . |
| Computing CP . . . . . |
| power CP CP . . CP |
| resources CP CP |
| |
-----------------------------------------------------------------
Fig.1 The architecture of metro optical computing power network.
3.1. Edge computing power management platform
To accurately fulfill the computational demands of services, the Edge
Computing Power Management Platform comprises four integral modules:
Computing Power measurement, Computing Power State Perception,
Computing Power Intention Mapping, and Computing Power Collaborative
Processing. The Edge Controller invokes the AI engine for embedded
deployment and algorithm adaptation. The platform interfaces with the
Traffic Scheduling Management Platform to process incoming service
characteristic data, ascertain customized computational power
requirements, and subsequently relay this information to the Traffic
Scheduling Module for unified service transmission. These processes
are incremental and sequential. Initially, it involves computation
and state perception, followed by intention analysis and mapping
based on service characteristics, ultimately concluding with the
querying of computational state resources for collaborative processing.
Computational power encompasses a diverse array of computational
infrastructure capabilities, including processing speed, memory
capacity, cache size, energy consumption, and failure rates. This
infrastructure encompasses a combination of various computing elements,
such as single-core central processing units (CPUs), graphics processing
units (GPUs), and network processors (NPUs). To accommodate the varied
computational requirements in the realm of edge computing, this layer
offers functionalities such as an algorithm library, computing
application programming interfaces (APIs), and the identification of
computing network resources.The state perception of computational power
involves monitoring the remaining resources of computational
infrastructure over time scales, mitigating resource shortages or
underutilization in metropolitan areas to achieve load balancing.
Computational intention mapping entails the analysis and mapping of
computational service intentions. It utilizes deep learning models to
precisely match network transmission resources with computing resources
in accordance with service needs. Computational power collaborative
processing refers to multi-point collaborative edge processing guided
by service intent and network resource states.
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3.2. Traffic scheduling management platform
The Traffic Scheduling Management Platform comprises several critical
components, including the Edge Service Interface, Deep Learning-Based
Traffic Prediction, Routing and Spectrum Resource Allocation, and
Service Priority Matching. This platform plays a pivotal role in the
efficient management of service requests. Service requests from clients
are channeled to the Traffic Scheduling Management Platform via the
service interface, where they are subsequently forwarded to the Edge
Computing Management Platform for processing. The platform, leveraging
predictions of computational traffic and the processing outcomes from
the computing management platform, proceeds to ascertain service
priorities. It then proceeds to manage routing, spectrum resource
allocation, and computing power scheduling.
The Edge Service Interfaces encompass modules for network resource
virtualization, which analyze underlying services and employ service
classification methodologies. Business characteristics and requirements
are conveyed to the Edge Computing Power Management Platform for further
processing. Deep Learning-Based Traffic Prediction encompasses models
for burst traffic detection, service type and arrival time prediction,
as well as predictions of required network bandwidth and computing power
resources. This segment is responsible for forecasting computational
power demands and offering corresponding guidance. The Routing and
Spectrum Resource Allocation model oversees downstream resource
allocation following the analysis of computational power processing
outcomes, efficiently managing resources for the corresponding
computational power services. The Service Priority Matching model
integrates priority-related processing algorithms, including those for
delay-sensitive scenarios and bandwidth fragmentation, to provide more
finely-tuned computational power services for service delivery.
4. Manageability Considerations
TBD
5. Security Considerations
TBD
6. IANA Considerations
This document requires no IANA actions.
7. References
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[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
Acknowledgments
TBD
Authors' Addresses
Tiankuo Yu
Beijing University of Posts and Telecommunications
Email: yutiankuo@bupt.edu.cn
Hui Yang
Beijing University of Posts and Telecommunications
Email: yanghui@bupt.edu.cn
Zhengjie Sun
Beijing University of Posts and Telecommunications
Email: sunzhengjie@bupt.edu.cn
Qiuyan Yao
Beijing University of Posts and Telecommunications
Email: yqy89716@bupt.edu.cn
Yang Zhao
China Mobile
Email: zhaoyangyjy@chinamobile.com
Sheng Liu
China Mobile
Email: liushengwl@chinamobile.com
Yunbo Li
China Mobile
Email: liyunbo@chinamobile.com
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