General Technical Capability Requirements for Intelligent Hybrid Cloud Platform
draft-lizihan-intelligent-hybrid-cloud-00
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draft-lizihan-intelligent-hybrid-cloud-00
Cloud Computing Open Source Industry Alliance Z. Li
Internet-DraftChina Academy of Information and Communications Technology
Intended status: Informational 3 July 2026
Expires: 4 January 2027
General Technical Capability Requirements for Intelligent Hybrid Cloud
Platform
draft-lizihan-intelligent-hybrid-cloud-00
Abstract
This document specifies the general technical capability requirements
for an intelligent hybrid cloud platform. An intelligent hybrid
cloud combines compute, storage, and network resources across
multiple cloud deployment models, leveraging artificial intelligence
algorithms to implement active hybrid cloud management functions such
as intelligent resource scheduling, intelligent analysis, intelligent
statistics, and intelligent prediction. It also provides support for
intelligent computing power and large model development-related
service technical capabilities within the hybrid cloud. This
document defines capability requirements across infrastructure,
unified platform management, model cross-cloud development, and
intelligent operations and maintenance.
This document is applicable to the design, development, and
deployment of intelligent hybrid cloud platforms by cloud service
providers, and provides reference and specifications for users
designing and deploying intelligent hybrid cloud platforms.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on 4 January 2027.
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Copyright Notice
Copyright (c) 2026 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
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Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Requirements Language . . . . . . . . . . . . . . . . . . 3
1.2. Terminology . . . . . . . . . . . . . . . . . . . . . . . 3
2. Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . 4
3. Overview of Capability Requirements Methodology . . . . . . . 5
4. Infrastructure Capability Requirements . . . . . . . . . . . 6
4.1. Computing Capability . . . . . . . . . . . . . . . . . . 6
4.2. Network Capability . . . . . . . . . . . . . . . . . . . 6
4.3. Storage Capability . . . . . . . . . . . . . . . . . . . 6
5. Unified Platform Management Capability . . . . . . . . . . . 6
5.1. Cross-Cloud Resource Collaboration Capability . . . . . . 6
5.2. Cross-Cloud Orchestration and Scheduling Capability . . . 7
5.3. Cross-Cloud Platform Analysis Capability . . . . . . . . 8
6. Model Cross-Cloud Development Capability Requirements . . . . 8
6.1. Cross-Cloud Training Scheduling Capability . . . . . . . 8
6.2. Model Fine-Tuning and Evaluation Collaboration
Capability . . . . . . . . . . . . . . . . . . . . . . . 9
6.3. Model Cross-Cloud Deployment Capability . . . . . . . . . 9
6.4. Model Cross-Cloud Inference Capability . . . . . . . . . 10
6.4.1. Service Manageability . . . . . . . . . . . . . . . . 10
6.4.2. Cloud-Edge Collaborative Inference Capability . . . . 10
6.5. Cross-Cloud Model Service Management Capability . . . . . 10
6.6. Knowledge Base Management Capability . . . . . . . . . . 11
7. Intelligent Operations and Maintenance Capability . . . . . . 11
7.1. Monitoring and Alerting Capability . . . . . . . . . . . 11
7.2. Log Management Capability . . . . . . . . . . . . . . . . 12
7.3. Fault Management . . . . . . . . . . . . . . . . . . . . 12
7.4. Automation Capability . . . . . . . . . . . . . . . . . . 12
7.5. Metering and Billing Capability . . . . . . . . . . . . . 13
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 13
9. Security Considerations . . . . . . . . . . . . . . . . . . . 13
10. Normative References . . . . . . . . . . . . . . . . . . . . 14
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Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 14
Author's Address . . . . . . . . . . . . . . . . . . . . . . . . 14
1. Introduction
Intelligent hybrid cloud combines compute, storage, and network
resources across multiple cloud deployment models, leveraging
artificial intelligence algorithms to implement active hybrid cloud
management functions such as intelligent resource scheduling,
intelligent analysis, intelligent statistics, and intelligent
prediction, including cloud-to-cloud collaboration. It also provides
support for intelligent computing power and large model development-
related service technical capabilities within the hybrid cloud. It
enables enterprises to flexibly allocate resources according to
business requirements, while improving efficiency and reliability
through intelligent dynamic monitoring, AI-driven analysis, and
decision-making capabilities.
This document applies to the design, development, and deployment of
intelligent hybrid cloud platforms by cloud service providers, and
provides reference and specifications for users designing and
deploying intelligent hybrid cloud platforms.
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.
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.
1.2. Terminology
This document uses the following terms and definitions established in
[T_CCSA385.1] and [GB_T32400].
hybrid cloud: A cloud deployment model that contains at least two
different cloud deployment models. [Source: [GB_T32400], 3.2.23]
intelligent hybrid cloud: A hybrid cloud deployment model enhanced
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by artificial intelligence technology, which implements active
hybrid cloud management functions such as intelligent resource
scheduling, intelligent analysis, intelligent statistics, and
intelligent prediction through AI algorithms, and provides support
for intelligent computing power and large model development-
related service capabilities during hybrid cloud operation
processes.
hardware resource splitting: The dynamic allocation capability to
divide physical GPUs, bare metal servers, and other heterogeneous
hardware resources into virtual units on demand.
multi-model integration management: The capability to deploy and
manage TensorFlow, PyTorch, PaddlePaddle, and Large Language
Models (LLM) within the same computing resource pool.
intelligent resource orchestration: The capability to achieve
optimal allocation and elastic scaling of cross-cloud resources
based on multi-objective optimization algorithms.
artificial intelligence for IT operations (AIOps): An intelligent
operations and maintenance system that utilizes AI technology to
implement operations data collection, anomaly detection, root
cause analysis, and self-healing processing.
2. Abbreviations
The following abbreviations are used in this document:
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+==============+=======================================+
| Abbreviation | Full Name |
+==============+=======================================+
| API | Application Programming Interface |
+--------------+---------------------------------------+
| ARM | Advanced RISC Machines |
+--------------+---------------------------------------+
| CPU | Central Processing Unit |
+--------------+---------------------------------------+
| GPU | Graphics Processing Unit |
+--------------+---------------------------------------+
| LLM | Large Language Model |
+--------------+---------------------------------------+
| NPU | Neural Network Processing Unit |
+--------------+---------------------------------------+
| SDK | Software Development Kit |
+--------------+---------------------------------------+
| SSD | Solid State Drives |
+--------------+---------------------------------------+
| TLS | Transport Layer Security |
+--------------+---------------------------------------+
| VPN | Virtual Private Network |
+--------------+---------------------------------------+
| VXLAN | Virtual eXtensible Local Area Network |
+--------------+---------------------------------------+
Table 1: Abbreviations Used in This Document
3. Overview of Capability Requirements Methodology
This document primarily adopts technical testing and document review
methods to conduct technical testing and protocol/SLA clause
compliance checks on the resource integration capability, platform
management capability, model development capability, and operations
and maintenance capability of the evaluated hybrid cloud platform,
and scores according to the pass status. Infrastructure capabilities
are mandatory, while other capabilities require passing more than 80%
of the evaluation items.
This standard applies to the intelligent transformation of existing
hybrid cloud products based on hybrid cloud architecture, covering
the integration of public cloud, private cloud, and edge cloud
resources and the fusion of intelligent capabilities. It also
applies to the needs of enterprises, government agencies, and other
organizations to achieve intelligent services during digital
transformation, helping enterprises clarify their capability levels
and upgrade targets.
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4. Infrastructure Capability Requirements
4.1. Computing Capability
This indicator defines the heterogeneous cloud resource integration
capability. The platform MUST meet the following requirements:
* Support unified access to resources including public cloud,
private cloud (dedicated cloud), edge cloud, and endpoint nodes;
* Support unified management of computing resources, storage
resources, network resources, database resources, middleware
resources, and container platforms;
* Provide edge node access capability, supporting local autonomous
operation in offline network environments.
4.2. Network Capability
This indicator defines the hybrid cloud network access capability.
The platform MUST meet the following requirements:
* Support the adoption of high-speed interconnection technologies,
optimization of network topology structures, and other methods to
improve data transmission efficiency and reduce network latency;
* Implement intelligent network security protection, supporting
dynamic threat detection and response.
4.3. Storage Capability
This indicator defines the hybrid cloud storage capability. The
platform MUST meet the following requirements:
* Implement high-performance storage (such as SSD, NVMe SSD) and
storage tiered management;
* Support cross-cloud storage scheduling and dynamic performance
specification adjustment.
5. Unified Platform Management Capability
5.1. Cross-Cloud Resource Collaboration Capability
This indicator defines the specific capability requirements for
heterogeneous resources and multi-source resource collaboration that
an intelligent hybrid cloud SHOULD possess. The platform MUST meet
the following requirements:
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* Support mapping different forms of similar resource services to
unified standardized service APIs, enabling the system to identify
and register different types of computing, storage, and network
resources;
* Support compatibility with mainstream GPU types and models;
* Support compatibility with mainstream CPU hardware architectures
(x86/ARM/RISC-V) for mixed deployment;
* Support at least two types of heterogeneous hardware, such as
CPU/GPU/FPGA/NPU;
* Support multi-source heterogeneous data storage, such as
structured, semi-structured, and unstructured data;
* Support cross-cloud storage scheduling and dynamic performance
specification adjustment;
* Support optimizing cross-cloud traffic routing strategies based on
AI algorithms (reinforcement learning/time series prediction);
* Support automatic selection of low-latency links according to
network congestion status;
* Support predicting peak periods and proactively expanding
bandwidth resources in advance.
5.2. Cross-Cloud Orchestration and Scheduling Capability
This indicator defines the cross-cloud resource orchestration and
scheduling capability requirements that an intelligent hybrid cloud
SHOULD possess. The platform MUST meet the following requirements:
* Support multi-cloud collaborative scheduling capability,
supporting real-time resource scheduling across 3 or more cloud
platforms (public cloud/private cloud/edge cloud);
* Support automatic cross-cloud scaling under burst load, with
resource recovery rate greater than or equal to 90% during scaling
down;
* Provide global capacity monitoring and capacity scheduling based
on monitoring analysis results;
* Support dynamic policy optimization, including multi-objective
optimization algorithms based on cost, performance, and carbon
emissions;
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* Predict business load changes through AI models;
* Support visualized composite orchestration of basic resources;
* Support generating orchestration templates, dynamically adding
resource nodes to adjust orchestration;
* Support cross-cloud resource orchestration recommendations based
on scheduling strategies such as cost optimization and performance
optimization.
5.3. Cross-Cloud Platform Analysis Capability
This indicator defines the capability of an intelligent hybrid cloud
to uniformly manage and intelligently analyze cross-cloud resources,
tasks, and applications. The platform MUST meet the following
requirements:
* Support custom log storage for monitoring prediction, scheduling
prediction, and cost analysis;
* Support generating resource optimization reports and cost
optimization reports;
* Support multi-dimensional cost analysis and resource optimization
recommendations;
* Support cross-cloud log and metric correlation analysis;
* Support implementing a visualized hybrid cloud management,
operations, and operation interface.
6. Model Cross-Cloud Development Capability Requirements
6.1. Cross-Cloud Training Scheduling Capability
This indicator defines the unified scheduling capability that an
intelligent hybrid cloud platform can provide through collaborative
resources. The platform MUST meet the following requirements:
* Support dynamic allocation of hybrid cloud resources (public cloud
computing power peak expansion, private cloud sensitive data
processing) for AI model training;
* Support sharding of training tasks, supporting deployment of
compute-intensive tasks (such as large model pre-training) to
public cloud, while retaining data preprocessing tasks in the
local private cloud.
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6.2. Model Fine-Tuning and Evaluation Collaboration Capability
This indicator defines the model inference and evaluation
capabilities that an intelligent hybrid cloud platform can provide
through collaborative multi-cloud environments. The platform MUST
meet the following requirements:
* Support three fine-tuning modes: full update, low-rank adaptation
(LoRA), and Prompt Tuning. Users MAY comprehensively select fine-
tuning modes considering factors such as computing power, dataset
size, downstream task type, and base model;
* Support implementing model fine-tuning in public cloud and model
testing in private cloud;
* Support online testing functionality, allowing users to verify the
accuracy and response effectiveness of models created on the
platform online. Online testing supports selecting services and
applications on the testing workbench for parameter configuration,
inputting or referencing prompt templates for input, and
completing testing;
* Support evaluating pre-configured large models and trained models,
supporting evaluation of models that have not been published as
online services or have been published as online services; support
configuring evaluation tasks to use public resource pools or
dedicated resource pools, as well as resource size configuration.
6.3. Model Cross-Cloud Deployment Capability
This indicator defines the multi-cloud deployment capability that an
intelligent hybrid cloud platform can provide. The platform MUST
meet the following requirements:
* Support seamless migration of TensorFlow/PyTorch models between
X86 and ARM architectures;
* Support multi-environment deployment of models in hybrid cloud,
including deployment in private cloud, edge nodes, and other
environments;
* Support diversified model deployment strategies, such as blue-
green deployment, canary deployment, and multi-replica deployment;
* Support public resource pool and dedicated resource pool
configuration. When services are published in a dedicated
resource pool, services exclusively occupy resources, and
corresponding computing units can be set to guarantee QPS;
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* Support deploying multiple models or multiple versions of a single
model in the hybrid cloud.
6.4. Model Cross-Cloud Inference Capability
6.4.1. Service Manageability
The platform MUST meet the following requirements:
* Support model inference service management, including start, stop,
and traffic limiting;
* Support elastic scaling of model inference services;
* Support version updates of inference services, supporting updating
specified versions for launch, and also supporting offline
operations for published services, supporting smooth version
replacement without directly affecting currently running version
services;
* Support a visualized interface for model inference services,
including displaying model call frequency, inference performance
metrics, computing resource occupancy, and other metrics;
* Support abnormal data collection for model inference.
6.4.2. Cloud-Edge Collaborative Inference Capability
The platform MUST meet the following requirements:
* Support cloud-edge training and inference capabilities, achieving
distributed intelligence;
* Support data cloud-edge collaborative deployment capability,
allocating differently according to hot and cold data identifiers;
* Support model lightweight compression technologies (such as
quantization, pruning), adapting to resource constraints of edge
devices.
6.5. Cross-Cloud Model Service Management Capability
This indicator defines the model management service capability that
an intelligent hybrid cloud platform can provide. The platform MUST
meet the following requirements:
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* Support viewing information of models deployed on the hybrid
cloud, including model name, type, creation/modification time,
etc.;
* Support lifecycle management of large models already listed on the
hybrid cloud, including model listing, publishing, querying,
removing, and delisting;
* Support version management of deployed large models, including
version tracking, difference comparison between different
versions, and version rollback;
* Support at least 2 different model file storage formats.
6.6. Knowledge Base Management Capability
This indicator defines the multi-cloud knowledge base management
capability that an intelligent hybrid cloud platform can provide.
The platform MUST meet the following requirements:
* Support building knowledge bases from documents in multiple
formats, including pdf, txt, md, docx, etc.;
* Support text splitting by character, length, and semantics, as
well as document cleaning;
* Support vector databases to store vectorized text fragments, and
support vector database similarity retrieval;
* Support viewing the total number of documents and total number of
characters in the knowledge base, and support document-level
function configuration.
7. Intelligent Operations and Maintenance Capability
7.1. Monitoring and Alerting Capability
The capability to perform intelligent operations and maintenance
optimization on the intelligent hybrid cloud platform and provide
related monitoring data collection for intelligent operations and
maintenance. The platform MUST meet the following requirements:
* Support large-screen monitoring, including resource usage rate,
allocation rate, alerts, and other information;
* Support querying historical monitoring data within custom time
periods;
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* Support noise reduction, classification, grading, and notification
of alert information and events based on the platform's own
algorithms;
* Support unified convergence and status tracking of alarm
information.
7.2. Log Management Capability
The capability to perform intelligent operations and maintenance log
optimization on the intelligent hybrid cloud platform. The platform
MUST meet the following requirements:
* Support calling log analysis interfaces and querying log analysis
system data;
* Support cross-cloud log and metric correlation analysis.
7.3. Fault Management
The capability to perform intelligent operations and maintenance
fault optimization on the intelligent hybrid cloud platform. The
platform MUST meet the following requirements:
* Meet anomaly detection and pre-analysis for CPU/memory/ network;
* Support root cause analysis, locating the root cause of complex
faults within hours;
* Support rapid recovery capability for minor faults within a short
time;
* Support building a fault knowledge base;
* Support fault learning capability, building operations and
maintenance intelligent agents.
7.4. Automation Capability
The capability to perform intelligent operations and maintenance
automation on the intelligent hybrid cloud platform. The platform
MUST meet the following requirements:
* Support self-healing capability through intelligent algorithms
(causal reasoning graph engines): achieving 100% automatic repair
for known fault modes (disk full, service process crash);
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* Support automated inspection: periodic health check coverage rate
greater than or equal to 99%, executable rate of generated repair
recommendations greater than or equal to 80%;
* Support dynamic resource reclamation: identifying idle resources
(utilization less than 10% for 24 consecutive hours),
automatically releasing or hibernating them.
7.5. Metering and Billing Capability
The capability to perform intelligent operations and maintenance
metering and billing on the intelligent hybrid cloud platform. The
platform MUST meet the following requirements:
* Support basic resource usage metering and bill generation;
* Provide a unified multi-cloud billing view;
* Provide mixed billing modes for on-demand/reserved/spot instance
AI tasks, such as resource-based billing, monthly subscription
billing, and billing by GPU and model service (training/inference/
knowledge base) call volume;
* Support viewing bills on bill details and cost analysis pages;
* Support multi-dimensional cost analysis and resource optimization
recommendations;
* Implement intelligent cost prediction and dynamic resource
adjustment to optimize billing.
8. IANA Considerations
This memo includes no request to IANA.
9. Security Considerations
This document specifies technical capability requirements for
intelligent hybrid cloud platforms. Implementers SHOULD consider the
following security aspects when deploying such platforms:
* Data confidentiality and integrity when transferring data across
public and private cloud boundaries;
* Access control and authentication mechanisms for multi-tenant
environments;
* Network security for cross-cloud communication channels;
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* Model security, including protection of intellectual property for
deployed AI models;
* Compliance with applicable data protection regulations.
10. Normative References
[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/rfc/rfc2119>.
[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/rfc/rfc8174>.
[GB_T32400]
Standardization Administration of China, "Information
Technology - Cloud Computing - Overview and Vocabulary",
2015.
[T_CCSA385.1]
China Communications Standards Association, "Cloud
Computing - Intelligent Hybrid Cloud - Part 1", 2022.
Acknowledgements
This document was drafted in accordance with the provisions of GB/T
1.1-2020 "Directives for Standardization - Part 1: Rules for the
Structure and Drafting of Standardizing Documents."
Please note that certain contents of this document may involve
patents. The publishing organization of this document assumes no
responsibility for identifying such patents.
This document was proposed and administered by the China
Communications Standards Association.
The following organizations contributed to the development of this
document: China Academy of Information and Communications Technology,
China Unicom Cloud Data Co., Ltd., Alibaba Cloud Technology Co.,
Ltd., China Mobile (Suzhou) Software Technology Co., Ltd., JD Cloud
Computing Co., Ltd., Tianyi Cloud Technology Co., Ltd., and Lenovo
(Beijing) Co., Ltd.
Author's Address
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Zihan Li
China Academy of Information and Communications Technology
China
Email: lizihan1@caict.ac.cn
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