Contact us
Contact us

Exciting Announcement! In celebration of launching our AI Certification, we’re thrilled to offer a 50% discount exclusively. Seize this unique chance—don’t let it slip by!

Learn more

Day: November 6, 2024

Categories
All

Transforming AI Fabric with ONES: Enhanced Observability for GPU Performance

Explore the latest in AI network management with our ONES 3.0 series

Future of Intelligent Networking for AI Fabric Optimization

If you’re operating a high-performance data center or managing AI/ML workloads, ONES 3.0 offers advanced features that ensure your network remains optimized and congestion-free, with lossless data transmission as a core priority.

In today’s fast-paced, AI-driven world, network infrastructure must evolve to meet the growing demands of high-performance computing, real-time data processing, and seamless communication. As organizations build increasingly complex AI models, the need for low-latency, lossless data transmission, and sophisticated scheduling of network traffic has become crucial. ONES 3.0 is designed to address these requirements by offering cutting-edge tools for managing AI fabrics with precision and scalability.

Building on the solid foundation laid by ONES 2.0, where RoCE (RDMA over Converged Ethernet) support enabled lossless communication and enhanced proactive congestion management, ONES 3.0 takes these capabilities to the next level. We’ve further improved RoCE features with the introduction of PFC Watchdog (PFCWD) for enhanced fault tolerance, Scheduler for optimized traffic handling, and WRED for intelligent queue management, ensuring that AI workloads remain highly efficient and resilient, even in the most demanding environments.

Why RoCE is Critical for Building AI Models

As the next generation of AI models requires vast amounts of data to be transferred quickly and reliably across nodes, RoCE becomes an indispensable technology. By enabling remote direct memory access (RDMA) over Ethernet, RoCE facilitates low-latency, high-throughput, and lossless data transmission—all critical elements in building and training modern AI models.

In AI workloads, scheduling data packets effectively ensures that model training is not delayed due to network congestion or packet loss. RoCE’s ability to prioritize traffic and ensure lossless data movement allows AI models to operate at optimal speeds, making it a perfect fit for today’s AI infrastructures. Whether it’s transferring large datasets between GPU clusters or ensuring smooth communication between nodes in a distributed AI system, RoCE ensures that critical data flows seamlessly without compromising performance.

Enhancing RoCE Capabilities from ONES 2.0 to ONES 3.0

In ONES 3.0, we’ve taken RoCE management even further, enhancing the ability to monitor and optimize Priority Flow Control (PFC) and ensuring lossless RDMA traffic under heavy network loads. The new PFC Watchdog (PFCWD) ensures that any misconfiguration or failure in flow control is detected and addressed in real-time, preventing traffic stalls or congestion collapse in AI-driven environments.

Additionally, ONES 3.0’s Scheduler allows for more sophisticated data packet scheduling, ensuring that AI tasks are executed with precision and efficiency. Combined with WRED (Weighted Random Early Detection), which intelligently manages queue drops to prevent buffer overflow in congested networks, ONES 3.0 provides a holistic solution for RoCE-enabled AI fabrics.

The Importance of QoS and RoCE in AI Networks

Quality of Service (QoS) and RoCE are pivotal in ensuring that AI networks can handle the rigorous demands of real-time processing and massive data exchanges without performance degradation. In environments where AI workloads must process large amounts of data between nodes, QoS ensures that critical tasks receive the required bandwidth, while RoCE ensures that this data is transmitted with minimal latency and no packet loss.

With AI workloads demanding real-time responsiveness, any network inefficiency or congestion can slow down AI model training, leading to delays and sub-optimal performance. The advanced QoS mechanisms in ONES 3.0, combined with enhanced RoCE features, provide the necessary tools to prioritize traffic, monitor congestion, and optimize the network for the low-latency, high-reliability communication that AI models depend on.

In ONES 3.0, QoS features such as DSCP mapping, WRED, and scheduling profiles allow customers to:

  • 1. Prioritize AI-related traffic over other types of traffic, ensuring faster model training and lower latency.
  • 2. Avoid congestion and packet loss, especially during periods of high traffic.

By leveraging QoS in combination with RoCE, ONES 3.0 creates an optimized environment for AI networks, allowing customers to confidently build and train next-generation AI models without worrying about data bottlenecks.

1. Comprehensive Interface and Performance Metrics

The UI showcases essential network performance indicators such as In/Out packet rates, errors, and discards, all displayed in real time. These metrics give customers the ability to:
  • 1. Track traffic patterns and identify congestion points.
  • 2. Quickly detect and troubleshoot network anomalies, ensuring smooth data transmission.
By having access to real-time and historical data, customers can make data-driven decisions to optimize network performance without sacrificing the quality of their AI workloads.

2. RoCE Config Visualization

RoCE (RDMA over Converged Ethernet) is a key technology used to achieve high-throughput and low-latency communication, especially when training AI models, where data packets must flow without loss. In ONES 3.0, the RoCE tab within the UI offers full transparency into how data traffic is managed:
  • 1. DSCP Mapping: Differentiated Service Code Point (DSCP) values are mapped to specific traffic queues, ensuring proper prioritization of packets.
  • 2. 802.1p Mapping: Allows customers to map Layer 2 traffic to different queues based on priority, which helps in optimizing scheduling for time-sensitive traffic.
  • 3. WRED and Scheduler Profiles: The Weighted Random Early Detection (WRED) profile and Scheduler profile work together to prevent congestion, ensuring that traffic is queued and forwarded efficiently, and that no packet is lost, which is critical for AI data pipelines.
  • 4. PFC and PFC Watchdog: Priority Flow Control (PFC) is a cornerstone feature that allows customers to create lossless data paths for high-priority traffic, ensuring that important data is never dropped, even during network congestion. The PFC Watchdog monitors and ensures that flow control remains active, preventing data bottlenecks.

3. Visual Traffic Monitoring: A Data-Driven Experience

The UI doesn’t just give you raw data—it helps you visualize it. With multiple graphing options and real-time statistics, customers can easily monitor:
  • 1. Transmit Packets: Keep an eye on both RoCE and normal traffic, and make sure that high-priority AI data packets are transmitted efficiently across the network.
  • 2. PFC Counters: Get detailed insights into PFC activity, including inbound and outbound traffic, ensuring that flow control mechanisms are functioning as intended.
  • 3. Queue Drop Counters: Understand where and when packet drops happen. By tracking packet discards, you can identify and address congestion issues, improving your network’s overall performance.
  • 4. Congestion Notification Packets (CNP): RoCE relies heavily on lossless data transmission, and CNPs play a crucial role in signaling congestion. Monitoring CNP activity ensures that your network responds dynamically to traffic demands, minimizing packet loss and delay.

4. Flexible Time-Based Monitoring and Analysis

Customers have the option to track metrics over various time periods, from live updates (1 hour) to historical views (12 hours, 2 weeks, etc.). This flexibility allows customers to:
  • 1. Analyze short-term network behavior for immediate troubleshooting.
  • 2. Review long-term trends for capacity planning and optimization.
This feature is especially valuable for customers running AI workloads, where consistent performance over extended periods is vital for the accuracy and efficiency of model training.

Centralized QoS View

ONES 3.0 offers a unified interface for all QoS configurations, including DSCP to TC mappings, WRED/ECN, and scheduler profiles, making traffic management simpler for network admins.
  • 1. Convenient UI Access: Eliminates the need for manual CLI commands by providing a UI-based platform to view all QoS configurations across switches, saving time and effort.
  • 2. Real-Time Monitoring: Enables immediate detection and resolution of traffic issues with live updates on queue and profile status, reducing troubleshooting time.
This page provides administrators with comprehensive insights into how traffic flows through the network, allowing them to fine-tune and optimize their configurations to meet the unique demands of modern workloads.
QoS Profile List
Fig 1 – QoS Profile List

Comprehensive Topology View

ONES offers a comprehensive, interactive map of network devices and their connectivity, ideal for monitoring AI/ML and RoCE environments. It provides an actionable overview that simplifies network management.
AI-ML Topology View
Fig 2 – AI-ML Topology View
Key features include:
  • 1. Real-Time Device Status: Users can monitor individual devices, such as servers and switches, viewing critical details like availability, region, port speeds, and telemetry, helping to quickly detect and resolve issues like non-streaming devices.
  • 2. Fault Detection: The interface highlights issues such as faulty fans, PSUs, or downed links, enabling swift corrective action to prevent network disruptions.
  • 3. Detailed Device Information: Upon selecting a device, detailed metadata is displayed, including hardware specifics (e.g., GPU/CPU info), agent details, uptime, and port configuration, aiding in troubleshooting and performance assessment.
  • 4. Link Details: When clicking on a link in the topology map, users can view all devices connected via that link, providing deeper insight into traffic paths and dependencies. This feature is crucial for diagnosing link-related issues and understanding how data flows between devices.
  • 5. Traffic Monitoring: Analyze metrics like traffic load across links to detect bottlenecks and optimize traffic flows, ensuring smooth performance for high-priority workloads like AI/ML tasks.
  • 6. Easy Navigation and Filtering: The ability to filter by topology type, device statuses, and regions simplifies the monitoring of large, complex networks, increasing management efficiency.
Overall, the Topology Page in ONES enhances network visibility and control, making it easier to optimize performance, troubleshoot issues, and ensure the smooth operation of AI/ML and RoCE workloads.

Proactive Monitoring and Alerts with the Enhanced ONES Rule Engine

The ONES Rule Engine has been a standout feature in previous releases, providing robust monitoring and alerting capabilities for network administrators. With the latest update, we’ve enhanced the usability and functionality, making rule creation and alert configuration even smoother and more intuitive. Whether monitoring RoCE metrics or AI-Fabric performance counters, administrators can now set up alerts with greater precision and ease. This new streamlined experience allows for better anomaly detection, helping prevent network congestion and data loss before they impact performance.

The ONES Rule Engine offers cutting-edge capabilities for proactive network management, enabling real-time anomaly detection and alerting. It provides deep visibility into AI-Fabric metrics like queue counters, PFC events, packet rates, and link failures, ensuring smooth performance for RoCE-based applications. By allowing users to set custom thresholds and conditions for congestion detection, the Rule Engine ensures that network administrators can swiftly address potential bottlenecks before they escalate.

With integrated alerting systems such as Slack and Zendesk, administrators can respond instantly to network anomalies. The ONES Rule Engine’s automation streamlines monitoring and troubleshooting, helping prevent data loss and maintain optimal network conditions, ultimately enhancing the overall network efficiency.

Conclusion

In an era where AI and machine learning are driving transformative innovations, the need for a robust and efficient network infrastructure has never been more critical. ONES 3.0 ensures that AI workloads can operate seamlessly, with minimal latency and no packet loss.
Categories
All

Global Reach, Local Insight: ONES 3.0 Delivers Seamless Data Center Management

Explore the latest in AI network management with our ONES 3.0 series

ONES 3.0 introduces a range of exciting new features, with a focus on scaling data center deployments and support. In this blog post, we’ll dive into two standout features: ONES Multisite, a scalable solution for global data center deployments, and enhanced support for SONiC through tech support, servicenow integration and syslog message filtering. Let’s explore how these innovations can benefit your operations.

ONES Multi-site

The ONES rule engine enables incident detection and alert generation, but this data is limited to the specific site managed by each controller. While site data center administrators can use this information to address and resolve issues, enterprise-level administrators or executives seeking an overview of all data centers’ health must access each ONES instance individually, which can be inefficient.

To address this challenge, we introduce ONES Multisite—an application that provides a geospatial overview of anomalies across geographically distributed sites, offering a comprehensive view of the entire network’s health.

ONES instances in different data centers (DCs) around the globe can register with a central multisite application. Upon successful registration, the multisite system periodically polls each site for data related to the number of managed devices (endpoints) and the number of critical alerts. This information is displayed on a map view, showing individual sites, their health status, and last contact times. ONES Multisite also allows users to log in to individual data centers for more detailed information if needed.

ONES Multisite showing DCs across the globe
Fig 1 – ONES Multisite showing DCs across the globe
To provide a quick overview of the health conditions at various sites, different colors and blinking patterns are used
  • 1. Green blink : Site is reachable with no critical alerts.
  • 2. Amber blink : Site is reachable but there are critical alerts.
  • 3. Red : Site is not reachable.

Registering ONES instance with Multisite application

A simple user interface is provided for registering the ONES application to the multisite, requiring inputs such as the site name, multisite IP, and geographical coordinates ((latitude and longitude in N and E). By default, the current location coordinates of the site are auto-populated, but they can be overridden if necessary. License page of ONES application displays the status of registration status with the multisite application.
Multisite Registration Window
Fig 2 – Multisite Registration Window

Once registered, the multisite application will regularly gather data from each site regarding the number of managed devices (endpoints) and the count of critical alerts.

ONES Multisite streamlines the monitoring process across multiple data centers, enabling enterprise-level administrators to easily access vital information and maintain a holistic view of their network’s health. This enhanced visibility not only improves operational efficiency but also empowers teams to respond more effectively to incidents, ensuring optimal performance across all locations.

Enhanced support for SONiC using ONES 3.0

Tech support feature

SONiC Tech Support feature provides a comprehensive method for collecting system information, logs, configuration data, core dumps, and other relevant information essential for identifying and resolving issues. ONES 3.0 Tech Support feature offers an easier way to download the tech support dump from any managed switch. Users can simply select a switch and click on the Tech Support option. ONES controller connects to the switch, executes the tech support command, and notifies the user when the download file is ready. This powerful option allows data center administrators to easily retrieve tech support data without the cumbersome process of logging into each switch, executing the command, and downloading the file.
ONES Tech support page

Fig 3 – ONES Tech Support page

Filtering of syslog messages

The Syslog feature empowers data center operators to easily view and download syslog messages from any of the managed switches through the ONES UI. This functionality is essential for monitoring system performance and diagnosing issues.

To enhance this feature, we’ve introduced a new enhancement that allows users to filter messages based on severity levels, such as error, warning, or all messages. This capability enables operators to quickly identify and prioritize critical alerts, streamlining the troubleshooting process and improving overall operational efficiency. By focusing on the most relevant messages, data center teams can respond more effectively to potential issues, ensuring a more reliable and robust network environment.

Syslog messages with filter applied
Fig 4 – Syslog messages with filter applied

ServiceNow Integration

ServiceNow is a cloud-based platform widely used for IT Service Management, automating business processes, and Enterprise Service Management. One of its core components is the ServiceNow ticketing system, specifically the Incident Management feature. When a user encounters a disruption in any IT service, it is reported as an incident on the platform and assigned to the responsible user or group for resolution.

The ONES Rule Engine proactively monitors the data center for potential disruptive events by creating alerts for any breaches of user-configured thresholds. It tracks various factors, such as sudden surges in CPU usage, heavy traffic bursts, and component failures (e.g., PSU, FAN).

ONES 3.0 enhances this functionality by integrating ServiceNow ticketing with the ONES Rule Engine and Alerts Engine. This integration allows ONES to automatically log tickets in the ServiceNow platform whenever any ONES rule conditions are met.

Rule creation page with Service now integrated
Fig 5 – Rule creation page with Service now integrated
Service now platform with ONES tickets
Fig 6 – Service now platform with ONES tickets
In summary, ONES 3.0 brings significant advancements that cater to the evolving needs of data center management.

To unlock the full potential of ONES 3.0 and see how it can revolutionize your network operations, book your demo today

Categories
All

AI Fabric Orchestration: Supercharging AI Networks with SONiC NOS

Explore the latest in AI network management with our ONES 3.0 series

As the demand for high-performance parallel processing surges in the AI era, GPU clusters have become the heart of data-intensive workloads. But it’s not just about the GPUs themselves—intercommunication between GPU servers is the backbone of their overall performance. Enters the network switch fabric, which is pivotal in overcoming communication bottlenecks and ensuring seamless data flow between GPU servers. Technologies like RoCE (RDMA over Converged Ethernet) allow massive chunks of data to move efficiently between servers, but ensuring that these critical data streams remain lossless and uncongested requires a powerful solution.

That’s where SONiC’s QoS (Quality of Service) features come into play. SONiC enables you to prioritize critical data traffic, ensuring high-priority packets are always transferred ahead of other traffic and also that your important data is not lost. Using SONiC’s robust QoS capabilities and ONES 3.0’s orchestration, you can turn your switch fabric into a lossless, priority-driven highway for GPU server communications.

Let’s explore how you can achieve this through SONiC via ONES 3.0 Fabric Manager orchestration tool.

Lossless And Prioritized Data Flow

Any packet entering the fabric with any DSCP/DOT1P marking can be mapped to any queue of the interface and enabling PFC on this queue makes it lossless. With PFC in place, when congestion is detected in the queue, a pause frame is sent back to the sender, signaling it to temporarily halt sending traffic of that priority. This mechanism effectively prevents packet drops, ensuring lossless transmission for traffic of particular priority.

Beyond PFC, there’s another layer of congestion management—Explicit Congestion Notification (ECN). With ECN, we can define buffer thresholds, exceeding which Congestion Notification (ECN-CNP) packets are sent to the sender, prompting it to reduce the transmission rate and proactively avoid congestion.

At this stage, we’ve ensured that our priority traffic is lossless. Moving into the egress phase, we can further enhance performance by prioritizing this traffic over others, even under congestion. SONiC provides scheduling algorithms like Deficit Weighted Round Robin (DWRR), Weighted Round Robin (WRR), and Strict Priority Scheduling (STRICT). By binding priority queues to these schedulers, the system can ensure that higher-priority traffic is transmitted preferentially, either in a weighted manner (for WRR/DWRR) or with absolute priority (for STRICT).

In summary, through PFC, ECN, and advanced scheduling techniques, SONiC ensures that high-priority traffic from GPU servers is not only lossless but also prioritized during both congestion and egress phases.

Simplifying Complex QoS Configurations with ONES Orchestration

Configuring SONiC’s complex QoS features may sound daunting, but with ONES 3.0’s seamless orchestration, it’s a breeze. ONES allows you to set up essential QoS configurations like DSCP to traffic-class mapping, PFC, ECN thresholds, and even scheduler types—all with a few lines in a YAML template. Here’s a snapshot of the YAML template showcasing how ONES orchestrates SONiC QoS (QoS is the section in YAML below)

ONES UI AI Fabric Orchestration YAML Template
Fig 1 – ONES UI AI Fabric Orchestration YAML Template

The Fabric Manager automates the creation and assignment of QoS profiles, saving administrators from manually configuring multiple aspects. Here’s how it works:

Mapping Traffic Classes and Queues

Orchestration begins by mapping traffic into appropriate classes and queues. ONES 3.0 Orchestration allows you to specify mapping values from DSCP (Layer 3) and dot1p (Layer 2) to traffic classes, traffic classes to queues, and traffic classes to priority groups (PGs). Upon specifying these mapping values, profiles would be created with standard namings using these mapping values like DOT1P_TC_PROFILE, TC_QUEUE_PROFILE, TC_PG_PROFILE, DSCP_TC_PROFILE and are binded to the interfaces that are part of the orchestration. This configuration ensures that each type of traffic is routed to its appropriate queue and handled correctly.

For example, we can specify mapping values in the YAML as above in image and FM will create the corresponding profiles and bind it to the interface as below:

Priority Flow Control (PFC) and Explicit Congestion Notification (ECN)

The next critical part of QoS orchestration involves Priority Flow Control (PFC), where ONES YAML allows users to define the queues that should be PFC-enabled. Moreover a PFC Watchdog can be configured to ensure that the PFC is well functioning with restoration, detection times and action to be taken in case of malfunctioning .

ECN configuration parameters can be provided in the YAML template using which ONES Fabric Manager creates a profile WRED_PROFILE and attaches it to all the queues that are PFC enabled for all the interfaces that are part of orchestration.

Here’s an example of how this would be configured on the interface for the YAML input in the above image.

This approach ensures that your network proactively manages congestion and minimizes packet drops for high-priority traffic.

Advanced Scheduling for Optimized Egress

Finally, Scheduling plays a vital role in controlling how packets are forwarded from queues. Orchestration allows administrators to choose between scheduling mechanisms such as Deficit Weighted Round Robin (DWRR), Weighted Round Robin (WRR), or STRICT priority scheduling, depending on their needs.

In the case of DWRR or WRR, weights can be assigned to each queue, influencing how often a queue is serviced relative to others. Upon specifying these parameters in the YAML, ONES-FM creates the scheduler policies (SCHEDULER.<weight>) each for a unique weight assigned to the queues and attach these created policies to the queues according to their weightage for all the interfaces that are part of the orchestration.

For instance in the below given image YAML input, there are two unique weights 60 and 40 that are assigned to queue 3 and 4 respectively. So, two scheduler policies SCHEDULER.40, SCHEDULER.60 are created and binded to the interface queues 3 and 4 respectively.

Now, here comes a question , what if all the queues are congested. How does the congestion notification packets even traverse through the network to reach the sender to stop or slow down the traffic coming in ?

ONES-FM provides an option to designate a specific queue for ECN_CNP (Explicit Congestion Notification packets) traffic, using STRICT scheduling, ensuring that even when the network is heavily congested there is always a room left for the congestion notification packets, preventing further blockages. cnp_queue under the ECN section in the above image represents that and is orchestrated as below by ONES-FM:

Flexible, Day-2 Support for QoS Management

One of the standout features of ONES-FM 3.0 is its support for Day-2 operations. As your network evolves and traffic patterns change, you can modify the QoS configurations through either the YAML template or the NetOps API. This flexibility ensures your network is always tuned to deliver the performance required by your AI workloads.

Future-Proof Your AI Infrastructure with ONES 3.0

With its intuitive YAML-based approach and support for dynamic Day-2 adjustments, ONES Fabric Manager eliminates much of the complexity associated with configuring and managing networks. ONES makes one confident that network infrastructure is both reliable and future-proof. In essence, ONES Fabric Manager enables seamless orchestration for AI fabrics, ensuring your network is always ready to meet the growing demands of AI-driven data centers.

AI Fabric Orchestration: Supercharging AI Networks with SONiC NOS

Explore the latest in AI network management with our ONES 3.0 series As the demand for high-performance parallel processing surges in the AI era, GPU clusters have become the heart of data-intensive workloads. But it’s not just about the GPUs themselves—intercommunication between GPU servers is the backbone of their overall performance. Enters the network switch […]