The combination of AI transmission and wavelength division transmission equipment meets differentiated needs in different scenarios through “high-bandwidth physical transmission + intelligent traffic scheduling”. The core application scenarios are as follows:
- I. Interconnection between data centers (IDC) and clusters
- Distributed training of large AI models
Scenario requirements: When multiple GPU servers (such as thousands of A100s) are trained collaboratively, massive model parameters (such as trillion-level parameter gradient updates) need to be transmitted in real time, requiring bandwidth exceeding 100Gbps and latency < 1ms.
Solution implementation:
Wavelength division equipment uses CWDM/DWDM to establish optical transmission links between cabinets and computer rooms in the data center. A single fiber carries 40+ wavelengths, with a total bandwidth of Tbps, meeting the needs of multi-node parallel data transmission.
The AI transmission algorithm analyzes the traffic characteristics of the training task (such as the bandwidth peak of forward propagation and reverse propagation), dynamically adjusts the wavelength allocation of the wavelength division equipment, and avoids transmission congestion caused by “gradient explosion”.
- High-speed storage interconnection within the data center
Scenario requirements: AI storage clusters (such as distributed object storage) and computing nodes frequently read and write massive amounts of data (such as training data sets and inference results), requiring low latency and high throughput.
Solution implementation:
Wavelength division devices deploy short-distance optical transmission modules (such as 100G SR4) within the data center, and connect computing servers and storage arrays through multimode optical fibers, with a transmission delay of < 100μs.
The AI transmission optimization algorithm schedules the wavelength resources of the wavelength division device according to the storage access priority (such as hot data > cold data) to ensure that high-frequency access data is transmitted first.
- Inter-data center (DCI) and cross-regional AI collaboration
- Inter-regional AI training cluster interconnection
Scenario requirements: When AI clusters in multiple data centers (such as Beijing, Shanghai, and Shenzhen) are jointly trained, TB-level model data needs to be transmitted, requiring cross-city delays of < 10ms and bandwidths to be stable at more than 10Gbps.
Solution implementation:
Wavelength division devices use long-distance DWDM (such as C+L band extension) to open dedicated wavelength channels in cross-city optical cables. Single wavelength supports 100G/400G transmission, and the delay of 300 kilometers across the city is about 1.5ms (optical transmission speed is about 200km/ms).
The AI transmission scheduling system monitors the traffic load between data centers in real time. When a wavelength link is congested, it automatically switches to the backup optical path (such as through OTN electrical layer crossover) to ensure uninterrupted training tasks.
- Multi-cloud AI service interconnection
Scenario requirements: AI service data (such as user portraits and inference results) needs to be synchronized in real time between multiple regional cloud platforms of cloud service providers (such as AWS and Alibaba Cloud), and cross-cloud AI applications (such as remote disaster recovery and elastic expansion) need to be supported.
Solution implementation:
Wavelength division devices establish wavelength-level dedicated lines between cloud data centers, and use optical layer encryption technology to ensure the security of AI data transmission (such as AES-256 encryption).
AI algorithms predict cross-cloud business traffic peaks (such as smart recommendation requests during e-commerce promotions) and apply for additional wavelength resources from wavelength division devices in advance to avoid service jams caused by burst traffic.
III. Edge computing and end-cloud AI collaboration
- Intelligent transportation and autonomous driving
Scenario requirements: Roadside cameras and vehicle-mounted sensors return video streams in real time (single camera 8K video is about 100Mbps). After the edge AI server completes real-time detection (such as obstacle recognition), it is necessary to upload key data (such as coordinates, speed) to the central cloud platform at high speed, requiring end-to-end delay < 50ms.
Solution implementation:
Wavelength division devices deploy medium-distance optical modules (such as 10G LR) between edge nodes (such as transportation hubs) and the central cloud, and transmit through single-mode optical fiber, with a delay of about 0.1ms for 20 kilometers.
AI transmission strategies prioritize data types: autonomous driving control instructions (such as brake signals) are transmitted through dedicated wavelengths of wavelength division devices, with a delay of < 10ms; ordinary surveillance videos are transmitted through shared wavelengths to balance bandwidth and cost.
- Industrial smart manufacturing
Scenario requirements: Thousands of sensors in the factory collect equipment operating data (such as temperature and vibration) in real time. After the edge AI gateway completes abnormality detection, it needs to upload the warning data to the cloud AI platform, requiring transmission reliability > 99.99% and dynamically adjustable bandwidth.
Solution implementation:
Wavelength division equipment deploys a ring network topology (such as an optical layer protection ring) in the factory. When a certain section of optical fiber fails, the backup optical path automatically switches (<50ms) to ensure that AI data is not lost.
The AI algorithm analyzes the periodicity of industrial services (such as traffic mutations when the production line is started and stopped) and dynamically adjusts the wavelength bandwidth of the wavelength division equipment: 10G wavelength is allocated when the production line is running, and it is reduced to 1G when it is on standby, reducing energy consumption and costs.
- Telecom-grade AI services and backbone network transmission
- Operator AI cloud network integration
Scenario requirements: Telecom operators provide AIaaS services (such as intelligent customer service, AI quality inspection) to government and enterprise customers through backbone networks. They need to transmit massive user data across the country, requiring backbone network bandwidth to reach 100Tbps and flexible cross-domain scheduling.
Solution implementation:
Wavelength division equipment deploys ultra-high-speed DWDM (such as 800G/1.6T wavelength) in the backbone network. Through C+L+O band expansion, the single-fiber bandwidth exceeds 100Tbps, supporting the convergence of AI business traffic across the country.
The AI transmission control system is linked to the wavelength division equipment through SDN (software defined network). According to the SLA requirements of customer AI services (such as the financial industry requires a delay of < 5ms), it automatically plans the optimal optical path (such as bypassing congested nodes) to achieve end-to-end intelligent scheduling.
- Broadcasting and Media AI Content Distribution
Scenario Requirements: In scenarios such as 4K/8K live video and VR content production, AI editing and rendering servers need to transmit TB-level media data in real time, requiring stable transmission bandwidth and jitter < 1ms.
Solution Implementation:
Wavelength division equipment opens a dedicated wavelength channel between the media data center and the distribution node, supports 100G/400G transmission, and ensures that multiple ultra-high-definition video streams are transmitted simultaneously without lag.
AI Transmission Optimization: The AI algorithm analyzes user viewing behavior (such as a sudden increase in popular live broadcast traffic), dynamically adjusts the wavelength priority of the wavelength division equipment, and increases the transmission bandwidth of popular content by 200%, ensuring a smooth user viewing experience.
- AI Data Transmission in Scientific Research and Supercomputing
- Data Transmission for High-Energy Physics Experiments
Scenario Requirements: Particle collision experiments (such as the Large Hadron Collider) generate PB-level data, which needs to be transmitted to the global AI analysis center through a cross-continental network, requiring bandwidth > 100Gbps and bit error rate < 1e-15.
Solution implementation:
Wavelength division equipment deploys coherent optical transmission technology (such as 16QAM modulation) in transnational optical cables. A single wavelength supports 400G transmission, with a transatlantic (6,000 km) delay of about 30ms, and the bit error rate is reduced to 1e-18 through FEC (forward error correction).
AI transmission algorithm optimizes data compression and fragmentation strategy: key data required for AI analysis (such as particle trajectories) are directly transmitted through the dedicated wavelength of the wavelength division equipment, and ordinary background data is compressed and transmitted through shared wavelengths, which improves the overall transmission efficiency by 30%.
- Weather forecast AI model data interaction
Scenario requirements: The temperature, humidity, air pressure and other data collected in real time by global weather stations (about 10TB/day) need to be transmitted to the supercomputing center for the AI model to make short-term/medium-term and long-term forecasts, requiring data integrity of 100% and transmission delay of < 1 hour.
Solution implementation:
Wavelength division devices are networked through OTN (Optical Transport Network) to establish a ring optical link between global meteorological data centers, and wavelength-level protection mechanisms are used to ensure zero data loss.
The AI transmission scheduling system dynamically allocates wavelength resources of wavelength division devices according to the timeliness priority of meteorological data (such as real-time observation data > historical data): real-time data is directly transmitted through 100G wavelengths, and historical data is transmitted through idle wavelengths during the night off-peak period, reducing the cost of intercontinental transmission by 50%.
Summary: Full-scenario coverage from core to edge
The collaborative application of AI transmission and wavelength division devices covers the entire chain of “cloud-edge-end”: supporting high-speed interconnection for large model training within the data center, realizing collaborative optimization of AI clusters in cross-regional scenarios, ensuring low-latency transmission of real-time AI services on the edge side, and meeting the ultra-large-scale, high-reliability AI data flow requirements in the telecommunications backbone network and scientific research fields. The combination of the two not only solves the transmission bottleneck in the AI era, but also realizes the upgrade from “transmission pipeline” to “intelligent network” through “physical layer transmission + intelligent layer scheduling”.
HTF 32T, 64T DWDM help you build more better AI using enviroment. Want to accelerate your DWDM/optical/AI networking product growth in your country? Get in touch with us and we can help. ivy@htfuture.com
