Use Cases and Requirements for Implementing Lossless Techniques in Wide Area Networks
draft-huang-rtgwg-wan-lossless-uc-00
| Document | Type | Active Internet-Draft (individual) | |
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| Authors | Hongyi Huang , Tao He , Tianran Zhou | ||
| Last updated | 2024-03-03 | ||
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draft-huang-rtgwg-wan-lossless-uc-00
Networking H. Huang, Ed.
Internet-Draft Huawei
Intended status: Standards Track T. He
Expires: 5 September 2024 China Unicom
T. Zhou
Huawei
4 March 2024
Use Cases and Requirements for Implementing Lossless Techniques in Wide
Area Networks
draft-huang-rtgwg-wan-lossless-uc-00
Abstract
This document outlines the use cases and requirements for
implementing lossless data transmission techniques in Wide Area
Networks (WANs), motivated by the increasing demand for high-
bandwidth and reliable data transport in applications such as high-
performance computing (HPC), genetic sequencing, and audio/video
production. The challenges associated with existing data transport
protocols in WAN environments are discussed, along with the proposal
of requirements for enhancing lossless transmission capabilities to
support emerging data-intensive applications.
Status of This Memo
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Copyright Notice
Copyright (c) 2024 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1. High-Performance Computing (HPC) Services for Scientific
Research . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2. Rapid Transmission Services for Genetic Sequencing for
Timely Medical Services . . . . . . . . . . . . . . . . . 4
2.3. Stable Transmission Services for Large-Scale Audio/Video
Data Migration . . . . . . . . . . . . . . . . . . . . . 4
3. Problem Analysis and Goal . . . . . . . . . . . . . . . . . . 4
3.1. Problem Analysis . . . . . . . . . . . . . . . . . . . . 4
3.1.1. Impact of Packet Loss . . . . . . . . . . . . . . . . 5
3.2. Goal . . . . . . . . . . . . . . . . . . . . . . . . . . 6
4. Challenges and Requirements . . . . . . . . . . . . . . . . . 6
5. Security Considerations . . . . . . . . . . . . . . . . . . . 7
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 7
7. Informative References . . . . . . . . . . . . . . . . . . . 7
Appendix A. Appendix-title . . . . . . . . . . . . . . . . . . . 8
A.1. Appendix-subtitle . . . . . . . . . . . . . . . . . . . . 8
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 8
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 8
1. Introduction
The big data is the very foundation of innovation across numerous
fields. From high-performance computing (HPC) in scientific research
to the latest advancements in genetic sequencing and the production
of high-definition multimedia content, the need for rapid, reliable,
and lossless data transmission across wide area networks (WANs) has
never been more critical. Traditional network protocols, designed in
an era before these immense data demands, struggle to keep up,
particularly when it comes to ensuring zero data loss over long
distances.
This document focuses on the pressing need for lossless data
transmission techniques in WANs, driven by the requirements of data-
intensive applications that form the backbone of scientific, medical,
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and creative industries. For example, the Energy Sciences Network
(ESnet) [ESnet] supports vast amounts of scientific data movement
that underpin groundbreaking research. Similarly, in the healthcare
sector, the explosion of data from genetic sequencing calls for
unprecedented levels of data transmission reliability and efficiency.
The media and entertainment industry also faces challenges in moving
large volumes of raw content with stable network instead of manual
tranportation of physical storage.
These scenarios underscore a growing disconnect between the
capabilities of existing WAN protocols and the evolving demands of
modern applications. The challenges of ensuring zero-loss
transmission in an infrastructure not originally designed for such
demands highlight the need for new solutions.
This document aims to shed light on the necessity for advanced
lossless transmission technologies in WANs. By identifying the
limitations of current network protocols and outlining the
requirements for new developments, we hope to pave the way for a new
generation of WANs. These networks will not only meet the current
demands of data-intensive applications but will also support the next
wave of digital innovation.
2. Use Cases
The necessity for implementing lossless data transmission techniques
in Wide Area Networks (WANs) is underscored by several critical
application areas. These use cases highlight the imperative for
reliable, high-speed data transfer capabilities to support the
demanding requirements of modern data-intensive operations.
2.1. High-Performance Computing (HPC) Services for Scientific Research
High-Performance Computing (HPC) services are fundamental to
scientific advancements, where collaborative efforts across various
geographical regions are commonplace. For instance, the study of
PSII proteins, which are crucial for understanding how water
molecules split to produce oxygen, generates between 30 to 120 high-
resolution images per second during experiments. This results in
60-100 GB of data every five minutes, necessitating rapid and
lossless data transfer from the National Renewable Energy
Laboratory's equipment back to analysis labs such as the Lawrence
Berkeley National Laboratory. The efficiency and reliability of WANs
in this context are not just beneficial but essential for
facilitating the seamless collaboration between scientists in
different domains, enabling them to share and analyze large datasets
effectively.
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2.2. Rapid Transmission Services for Genetic Sequencing for Timely
Medical Services
The field of genetic sequencing has seen exponential growth, driven
by the decreasing costs and widespread application of sequencing
technologies. This growth is matched by the burgeoning data volumes
generated, which require efficient and lossless transmission to cloud
or private data centers for analysis. For example, sequencing a
single human genome produces 100GB to 200GB of data. With daily data
production rates reaching 6TB to 12TB and annual data management
needs surpassing 1.6PB, the demand for high-speed, reliable data
transfer is evident. The existing network transfer efficiencies
present significant bottlenecks, extending the turnaround times for
sequencing services and impacting the timely delivery of precision
medicine.
2.3. Stable Transmission Services for Large-Scale Audio/Video Data
Migration
The competitive landscape of the audio and video industry, coupled
with the shift towards cloud-based post-production processes,
necessitates the transfer of large volumes of raw footage across
WANs. Traditional methods of data transportation, involving physical
media and manual transfer, are not only time-consuming but also
inefficient. For instance, film crews generating 2TB of data daily
resort to physically moving storage media to processing locations, a
process that significantly delays the production cycle and weakens
market responsiveness. The requirement for a network infrastructure
capable of handling such extensive data transfers quickly and without
loss is critical for maintaining the pace of production and ensuring
the quality of the final multimedia content.
3. Problem Analysis and Goal
3.1. Problem Analysis
The primary objective in the realm of Wide Area Networks (WANs) is to
provide long-term, stable, and high-capacity network services that
can accommodate the sudden surges in data transmission demands,
essential for data migration across diverse geographical locations.
This goal is predicated on leveraging the inherent statistical
multiplexing advantage of IP networks, which allows for cost-
effective bandwidth allocation and enhanced overall network
throughput. The ability to meet these data transmission requirements
efficiently is crucial for supporting the backbone of today’s data-
driven applications, ranging from scientific research to global
financial transactions and multimedia content delivery.
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Despite the advantages of statistical multiplexing in IP networks,
such as cost reduction and throughput optimization, this model
introduces significant challenges in ensuring absolute resource
guarantee and, consequently, zero packet loss. The practice of
overprovisioning bandwidth, common among service providers, does not
equate to lossless data transmission, which is a critical shortfall
when compared to dedicated light networks or resources with hard
isolation.
3.1.1. Impact of Packet Loss
In the scenarios outlined for data migration—whether for high-
performance computing services, genetic sequencing, or audio/video
data migration—the reliance on traditional transmission protocols
like TCP or RDMA [RoCEv2] is common. However, both protocols are
adversely affected by packet loss, especially over long-haul
transmissions.
For TCP, algorithms such as CUBIC, a loss-based congestion control
mechanism, see a dramatic throughput decline of up to 89.9% with just
a 2% packet loss when the Round-Trip Time (RTT) is 30ms. BBR,
another TCP congestion control that bases on bandwidth and delay,
also suffers significantly when packet loss exceeds 5%, with
throughput plummeting in scenarios where packet loss reaches 20%. The
cost of retransmissions in these conditions is notably high, with
slight packet loss (<1%) scenarios showing a retransmission rate 6-10
times higher than CUBIC, and in severe packet loss scenarios, the
rate can increase exponentially.
RDMA, often used within data centers for inter-node data access over
UDP, relies on a goBackN retransmission mechanism. Its throughput
dramatically decreases with packet loss rates greater than 0.1%, and
a 2% packet loss rate effectively reduces throughput to zero. To
maintain unaffected throughput, the packet loss rate must be kept
below one in a hundred thousand.
These challenges underscore a critical gap in the current
capabilities of IP networks to support the demanding requirements of
modern, data-intensive applications. The inability to ensure zero
packet loss across WANs not only impacts application performance but
also limits the potential for innovation and collaboration across key
sectors reliant on rapid and reliable data transmission.
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3.2. Goal
The overarching goal in the evolution of Wide Area Networks (WANs) to
serve the afore-mentioned use cases is to enable lossless, zero-
packet-loss transmission services tailored for the seamless migration
of data across different geographical areas. In an age where digital
data's volume, velocity, and variety are expanding exponentially,
ensuring the lossless transmission of this data during inter-regional
migration activities becomes indispensable. This is critically
important for applications and operations that rely on the integrity
and timeliness of data, such as AI/HPC computing and data backup and
recovery.
4. Challenges and Requirements
The quest for lossless data transmission in Wide Area Networks (WANs)
is confronted with significant challenges, notably the phenomenon of
elephant flows—large, bursty data transfers that can cause
instantaneous congestion and packet loss within network device
queues. This not only increases application latency but also
diminishes throughput, adversely affecting application performance.
In data centers, certain lossless technologies are deployed to
enhance the performance of such applications:
* *Priority-based Flow Control (PFC)*: Widely adopted for its
ability to manage traffic flow, PFC [PFC] works by halting the
transmission of specific queues when downstream congestion is
detected, thereby achieving zero packet loss. The foundational
flow control mechanism, defined by IEEE 802, involves sending a
pause frame from a receiving device to a sending device to
temporarily halt traffic, allowing time for congestion to clear
before resuming transmission.
* *Explicit Congestion Notification (ECN) with Data Center Quantized
Congestion Notification (DCQCN)*: DCQCN [DCQCN], the most
extensively used congestion control algorithm in RDMA networks,
requires network devices to support ECN functionality [RFC3168],
with other protocol functionalities implemented on the network
card of the host machine. DCQCN ensures high throughput in RDMA
networks needing zero packet loss by signaling congestion through
ECN markers sent from congested nodes to the sender, prompting a
reduction in sending rate.
However, the application of these data center-oriented lossless
techniques to WANs encounters obstacles due to the larger scale and
longer RTTs inherent in WAN environments. Challenges and
corresponding requirements arise such as:
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* *Backpressure from PFC*: The widespread application of PFC in
large-scale networks can lead to head-of-line blocking, deadlocks,
and congestion spreading, which degrade network throughput. Such
challenges make the traditional PFC backpressure mechanisms poorly
suited for the high stability demands of WANs, necessitating
innovation in protocol design to alleviate issues like deadlocks
and PFC storms. *Requirement 1*: Innovate and improve upon the PFC
backpressure mechanism for WANs, addressing and mitigating the
risk of deadlocks and congestion spreading to ensure stable and
lossless data transmission.
* *ECN-Based Congestion Control Limitations*: While ECN facilitates
sender rate control through network collaboration, its
effectiveness diminishes over longer distances typical of WANs.
The delayed congestion notifications result in prolonged control
loops, making it challenging to quickly alleviate congestion.
*Requirement 2*: Optimize the ECN control loop for WANs, enhancing
the network's ability to manage congestion through improved
routing and control strategies, thereby ensuring efficient and
lossless transmission across vast geographical distances.
These challenges underscore the need for tailored solutions that
address the unique demands and conditions of WANs. By adapting and
innovating on existing lossless transmission technologies from data
center networks, the goal of achieving zero packet loss in WANs
becomes attainable, paving the way for enhanced data mobility and
application performance.
5. Security Considerations
TBD.
6. IANA Considerations
TBD.
7. Informative References
[RFC3168] Ramakrishnan, K., Floyd, S., and D. Black, "The Addition
of Explicit Congestion Notification (ECN) to IP",
RFC 3168, DOI 10.17487/RFC3168, September 2001,
<https://www.rfc-editor.org/rfc/rfc3168>.
[RoCEv2] "Supplement to InfiniBand architecture specification
volume 1 release 1.2.2 annex A17 - RoCEv2 (IP routable
RoCE).", n.d..
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[DCQCN] et.al., Y. Z., "Congestion Control for Large-Scale RDMA
Deployments", August 2015,
<https://conferences.sigcomm.org/sigcomm/2015/pdf/papers/
p523.pdf>.
[PFC] "IEEE Standard for Local and metropolitan area networks--
Media Access Control (MAC) Bridges and Virtual Bridged
Local Area Networks--Amendment 17- Priority-based Flow
Control", n.d..
[ESnet] "Energy Sciences Networks", n.d..
Appendix A. Appendix-title
A.1. Appendix-subtitle
Acknowledgements
TBD.
Contributors
TBD.
Authors' Addresses
Hongyi Huang (editor)
Huawei
Beijing
China
Email: hongyi.huang@huawei.com
Tao He
China Unicom
Email: het21@chinaunicom.cn
Tianran Zhou
Huawei
Email: zhoutianran@huawei.com
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