Networking T. He, Ed.
Internet-Draft China Unicom
Intended status: Informational H. Huang, Ed.
Expires: 6 January 2025 Huawei
Z. Han
N. Wang
China Unicom
T. Zhou
Huawei
5 July 2024
Framework for Implementing Lossless Techniques in Wide Area Networks
draft-he-huang-rtgwg-wan-lossless-framework-00
Abstract
This document proposes a comprehensive framework to address the
challenges of efficient, reliable, and cost-effective large volume
data transmission over Wide Area Networks (WANs). The framework
focuses on planning and managing traffic paths, network slicing, and
utilizing multi-level network buffers. It introduces dynamic path
scheduling and advanced resource allocation techniques to optimize
network resouce and minimize congestion. By leveraging cross-device
buffer coordination and real-time adjustments, the framework ensures
high throughput and low latency, meeting the demands of modern, data-
intensive applications while providing a robust solution for large-
scale data transmission.
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This Internet-Draft will expire on 6 January 2025.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Network Challenges Posed by Large Volume Data Transmission . 3
2.1. Limited Network Capacity . . . . . . . . . . . . . . . . 3
2.2. Congestion Hotspots . . . . . . . . . . . . . . . . . . . 4
2.3. Inefficient Buffer Utilization . . . . . . . . . . . . . 4
3. Framework . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.1. Adaptive Planning and Management of Network Resouce . . . 4
3.1.1. Specific Requirements: . . . . . . . . . . . . . . . 5
3.2. Use and Management of Multi-Level Network Buffers . . . . 5
3.2.1. Specific Requirements: . . . . . . . . . . . . . . . 5
3.3. Requesting Source Rate Control . . . . . . . . . . . . . 6
3.4. Performing Adaptive Path Adjustment . . . . . . . . . . . 6
4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 7
5. Security Considerations . . . . . . . . . . . . . . . . . . . 7
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 7
7. Informative References . . . . . . . . . . . . . . . . . . . 7
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 7
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 7
1. Introduction
In recent years, the demand for reliable and efficient transmission
of large volumes of data across Wide Area Networks (WANs) has surged.
[I-D.huang-rtgwg-wan-lossless-uc] highlighted several critical use
cases that emphasize the necessity of low packet loss and high
throughput in WANs. These requirements are driven by applications
that handle massive datasets, such as scientific research, financial
transactions, and multimedia content delivery, while the locations of
data production and consumption differ, requiring efficient and
timely transmission across WANs. The characteristics and
requirements of large data transmission are listed as follows:
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* Large Volume. The datasets involved in these transmissions often
reach terabyte levels. Traditional fixed bandwidth dedicated
lines, while reliable, can be prohibitively expensive.
Enterprises must balance the need for high-capacity data
transmission with cost considerations. This necessitates
exploring more flexible and economical solutions that can handle
large-volume data without incurring excessive costs.
* Timeliness. Timeliness is a critical factor for data transmission
over WANs. For instance, in the field of genetic research, the
timely transmission of genetic data can significantly influence
diagnostic and treatment outcomes. Delays in data transmission
can render the data obsolete, e.g., leading to incorrect results
and conclusions. Therefore, ensuring that data is transmitted
within a specific time window is essential for maintaining its
utility and accuracy.
* Predictability. Large-volume data transmission tasks typically
have predictable patterns, allowing for better planning and
resource allocation. This predictability helps in designing
network solutions that can efficiently manage the anticipated data
load. By leveraging predictable traffic patterns, network
administrators can optimize resource allocation, minimize
congestion, and enhance overall network performance.
This document proposes a comprehensive framework aimed at addressing
the challenges associated with large volume data transmission over
WANs. The framework focuses on enhancing traffic management and
resource allocation strategies to ensure efficient, reliable, and
cost-effective data transmission. By implementing these strategies,
the framework aims to meet the demands of modern, data-intensive
applications, providing a robust solution for large volume data
transmission in WAN environments.
2. Network Challenges Posed by Large Volume Data Transmission
2.1. Limited Network Capacity
WANs have finite carrying capacities. When a significant amount of
traffic enters the network simultaneously, it can lead to traffic
conflicts, resulting in queuing and jitter. These issues are
exacerbated by the continuous nature of large data transfers, which
can strain network resources over extended periods. Addressing these
challenges requires advanced traffic management techniques that can
efficiently utilize available network capacity.
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2.2. Congestion Hotspots
Packet loss often occurs due to probabilistic simultaneous influxes
of large volumes of traffic. This congestion is exacerbated by
mechanisms such as Equal-Cost Multi-Path (ECMP) routing, where
multiple flows compete for certain bottleneck links, leading to
congestion and packet loss. Packet loss in WANs does not lead to
permanent data loss since lost packets can be retransmitted.
However, retransmissions increase transmission latency, causing
delays in data delivery. Moreover, packet loss can trigger
congestion control mechanisms, which reduce the network's throughput
to prevent further congestion. This reduction in throughput can
significantly affect the performance of data-intensive applications,
making it critical to minimize packet loss.
2.3. Inefficient Buffer Utilization
The network itself has a certain buffer capacity to partially
mitigate short-term processing deficiencies. However, current
mechanisms only utilize the local device's buffer and do not fully
exploit the overall buffer capacity across multiple devices. This
fragmented buffer utilization leads to inefficiencies in handling
bursty traffic. Advanced congestion management strategies are
necessary to coordinate buffer usage across the network, maintaining
high throughput and low latency to ensure efficient and reliable data
transmission.
3. Framework
This document proposes a comprehensive framework to address the
challenges of efficient, reliable, and cost-effective large volume
data transmission over Wide Area Networks (WANs). The framework
focuses on the planning and management of traffic paths, network
slicing, and the use and management of multi-level network buffers.
3.1. Adaptive Planning and Management of Network Resouce
When users seek efficient transmission of large datasets, they can
rent temporary network bandwidth in addition to their fixed leased
lines (a.k.a guranteed bandwidth). This temporary bandwidth is
cheaper by sharing but offers weaker Service Level Agreements (SLAs).
Due to the predictable nature of the traffic, users can pre-request
resource scheduling from the network, including traffic paths and
even network slices. The network can allocate resources based on
availability, avoiding prolonged congestion through effective
planning. If serious congestion occurs, the network scheduler can
recalculate paths and slice resources. Network devices can flexibly
choose the best available path from multiple pre-allocated paths,
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particularly when head-end devices detect local or remote congestion.
By adjusting the current and incoming traffic path selection, network
devices can optimize traffic distribution and alleviate congestion
dynamically.
3.1.1. Specific Requirements:
* *Network Resource Reporting and User Request*: Network devices
report attributes such as bandwidth, latency, and buffer capacity
through control plane protocols like IGP and BGP-LS. Users
provide the overall bandwidth needs for large volume data
transmission, including guaranteed dedicated resources and
flexible resources with weaker guarantees.
* *Network Resource Allocation and Policy Distribution*: Controllers
calculate out IP-based dedicated lines (IP tunnels with segment
routing) within the WAN domain based on available flexible
bandwidth and buffers. Using SR-policy, data traffic is steering
into IP tunnels at ingress nodes and directed to dedicated network
slicing. Configuration of buffer allocations are distributed via
protocols like BGP and PCEP from the controller to the network
devices who are executing and enforcing these configurations.
* *Network State Measurement and Telemetry*: Real-time bandwidth
measurement based on measurement packets helps in sensing utilized
and available bandwidth on network links. This information is
reported to the controller via telemetry mechanisms and used to
adjust paths and slice resources. For example, when a link nears
its bandwidth limit, traffic can be rerouted to idle path
resources to improve overall network bandwidth utilization.
3.2. Use and Management of Multi-Level Network Buffers
Since temporary bandwidth is shared and not dedicated, it exhibits
weaker SLA guarantees. If traffic experiences jitter during
transmission, network device buffers can absorb packets to reduce
packet loss.
3.2.1. Specific Requirements:
* *Single Device Buffer Sharing and Management*: Single devices
should implement fine-grained buffer divisions based on traffic
priority and slice. These buffers should be isolated to avoid
mutual interference. Initial buffer resource allocation is
determined by the controller and configured across all devices in
the domain via control plane protocols.
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* *Cross-Device Buffer Coordination*: Given the nature of large data
transmissions, a single device's buffer might be insufficient for
absorbing bursty traffic. Therefore, multiple devices' buffers of
the same fine-grained type (e.g., same priority and slice) should
be used collectively. For example, if device C in the path
A->B->C is congested and its buffer is insufficient, it should
notify upstream devices B or A to utilize their similar buffers to
absorb some traffic. This involves:
- Control Signaling: Using control signaling packets to notify
upstream devices to buffer packets, reducing the burden on the
congested device. If upstream device buffers also reach a
threshold, further notifications should be triggered upstream.
Control signaling should include buffer index (e.g., slice ID),
control instructions, and parameters. Controller configuration
or segment routing can help determine upstream device
addresses. Upon congestion relief, upstream devices should be
notified to release buffered traffic. This notification
mechanism can be inspired by IEEE PFC mechanisms but requires
more granular backpressure.
- Trigger Conditions for Buffer Coordination: The local device-
triggering cross-device buffer coordination requires pre-set
conditions. Controllers can configure device-specific
thresholds to customize trigger conditions for each device,
slice, and priority.
3.3. Requesting Source Rate Control
Network devices can send rate control requests to the source via data
packet marking or separate control packets. This method is useful
during widespread network congestion, leveraging source rate
reduction to manage traffic. Although this feedback mechanism
involves a larger control loop and slower adjustments, efficiency can
be improved through fast reverse notifications.
3.4. Performing Adaptive Path Adjustment
Network devices can flexibly choose the best available path from
multiple pre-allocated paths, particularly when head-end devices
detect local or remote congestion. By adjusting the current and
incoming traffic path selection, network devices can optimize traffic
distribution and alleviate congestion dynamically.
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4. Conclusion
The proposed framework addresses the challenges of large volume data
transmission over WANs by enhancing traffic management and resource
allocation strategies. By implementing dynamic path scheduling,
advanced resource allocation, and efficient buffer management, the
framework ensures efficient, reliable, and cost-effective data
transmission. This approach meets the demands of data-intensive
applications, providing a robust solution for large volume data
transmission in WAN environments.
5. Security Considerations
TBD.
6. IANA Considerations
TBD.
7. Informative References
[I-D.huang-rtgwg-wan-lossless-uc]
Huang, H., He, T., and T. Zhou, "Use Cases and
Requirements for Implementing Lossless Techniques in Wide
Area Networks", Work in Progress, Internet-Draft, draft-
huang-rtgwg-wan-lossless-uc-00, 3 March 2024,
<https://datatracker.ietf.org/doc/html/draft-huang-rtgwg-
wan-lossless-uc-00>.
Acknowledgements
TBD.
Contributors
TBD.
Authors' Addresses
Tao He (editor)
China Unicom
Beijing
China
Email: het21@chinaunicom.cn
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Hongyi Huang (editor)
Huawei
Beijing
China
Email: hongyi.huang@huawei.com
Zhengxin Han
China Unicom
Email: hanzx21@chinaunicom.cn
Nan Wang
China Unicom
Email: wangn161@chinaunicom.cn
Tianran Zhou
Huawei
Email: zhoutianran@huawei.com
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