Contact us
Join Community
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

Unveiling the Backbone: Exploring ONES Network Latency Measurement Backend

Introduction

In today’s datacenter landscape, network latency holds significant importance due to its impact on the overall performance, efficiency, and reliability of datacenter operations. Here are key aspects that highlight the significance of network latency in data centers. Low network latency is crucial for ensuring optimal performance of applications hosted in data centers. Users expect fast response times when accessing services and applications, and latency directly influences the perceived responsiveness of these systems. Organizations face various challenges in measuring and optimizing network latency, as this task involves complex considerations related to infrastructure, applications, and user experience. Some common challenges include the complexity of the network infrastructure, dynamic workloads and continuous monitoring feeding to the analysis.
This blog introduces you to the backend of ONES network latency measurement component , the core engine responsible for collecting data related to network latency. This component plays a crucial role in providing insights into the performance of a network, helping organizations monitor and optimize their infrastructure. It supports various network protocols, including ICMP (Internet Control Message Protocol), and TCP (Transmission Control Protocol), depending on the need.

Let’s explore the key aspects and functionalities of the backend.

Core Features

The NWSLA measurement component provides an agent that runs in the SONiC switches as well as servers. This agent exposes an API to the ONES collector eco-system and allows for triggering the latency measurements. Latency to a Destination IP can be measured using either ICMP or TCP. The calculation involves the following parameters
  • Protocol - ICMP vs TCP
  • Destination IP
  • Port (for TCP)
The above diagram explains the same. ONES Collectors controls & facilitates the probes, allowing latency measurements to be performed by the whole ecosystem of endpoints. One of the important features of this agent is its ability to allow the calculations to be calculated periodically. For instance an operator wishes to calculate the latency between point A to point B every 5 minutes. This aids the operator in the following cases
  • Periodic latency calculations help operators monitor the health of the network. An increase in latency can indicate potential bottlenecks or issues that need optimization.
  • By measuring latency regularly, operators can verify that the network meets the latency requirements specified in SLAs, maintaining high-quality services for customers and stakeholders.
  • Regular latency measurements provide a baseline for normal network behavior. Deviations from this baseline can signal potential faults, enabling operators to troubleshoot and resolve issues promptly.
  • Latency calculations help operators understand how network performance scales with increasing loads. This information is crucial for capacity planning and ensuring the network can handle growing demands.
  • Periodic latency measurements are essential for monitoring the performance of real-time applications, such as video conferencing or voice-over IP (VoIP), where low latency is critical for a smooth user experience.

To cater to such requirements, ONES allows the operator to schedule the calculation of the latency periodically over specified time intervals. This allows the operators to understand the performance of the networks proactively.

NanoSecond Level Precision

One of the unique features of the ONES infrastructure that calculates this latency is its ability to calculate the latency in terms of nanoseconds. Calculating the latency in terms of nanoseconds offers some unique advantages, in scenarios where extremely precise and rapid measurements are essential perfect for the datacenter networks. It allows
  • Ultra-Low Latency Requirements: Nanosecond-level measurements cater to applications with ultra-low latency requirements. These applications demand the fastest possible response times for optimal performance.
  • Real-Time Communication: Applications like telecommunication services and real-time communication tools benefit from nanosecond-level measurements. This precision ensures that communication is nearly instantaneous, enhancing the quality of real-time interactions
  • Computing: In edge computing environments, where processing occurs closer to the data source, nanosecond-level latency measurements are crucial. This precision helps evaluate the effectiveness of edge deployments in providing rapid responses.
  • High Throughput Networks: In networks with exceptionally high throughput, nanosecond-level precision is advantageous for accurately assessing the efficiency and performance of the network, especially under extreme loads.
In summary, calculating latency in nanoseconds offers advantages in situations where speed, precision, and real-time responsiveness are paramount.

Decoding Nanosecond Latency Calculation

Precision in nanosecond latency calculation is a sophisticated endeavor. ONES adopts an innovative approach by modeling the network analogous to an optical channel, ensuring a high degree of precision in latency calculation. The methodology involves sending bursts of packets and deriving latency measurements seamlessly, deviating from the conventional approach of correlating request and response times. This eliminates the need to calculate latency per request before initiating subsequent probes. The flexibility of ONES allows for the configuration of parameters to align with specific network requirements.

Scalability & Robustness

The ONES ecosystem excels in delivering exceptional scalability and robustness. The philosophy of ONES is seamlessly reflected in the design of its latency measurement, ensuring scalability and robustness are prioritized. This commitment is affirmed through various validations, including measuring latency under load, the seamless addition of new nodes for calculations, the capacity to handle and sustain a significant number of probes by the agent, built-in fault tolerance features, optimized resource utilization, and consistent operational and longevity behavior.

Use-Cases: Ping-pong Mesh

One of the simple use-case scenarios will be to trigger the measurement across the endpoints of the network. This initiates the latency test from the end points attached to the network ensuring that the packets used to measure latency traverse the network to reach the other endpoint. This will be calculated proactively at a set defined interval allowing to check on the latency periodically. Identifying latency bottlenecks helps optimize resource allocation and maintain high-quality services
Under such use-cases, the measurement of latency plays a vital role in optimizing overall network performance. Low-latency communication directly enhances user experience, aids in capacity planning, facilitates proactive issue resolution, and furnishes valuable data for making informed decisions about network infrastructure.These scenarios can be expanded, including the exploration of latency between the most distant leaf nodes, and so on.

ONES Network SLA - Future Looking

Subsequent releases of ONES provide robust support with advanced features built upon this foundational base. Initially, integration with the ONES UI will be seamless, offering comprehensive cloud integration support. Additionally, support for path tracing and availability metrics will be extended across the system.

In conclusion, the backbone of a network latency measurement tool functions as the core engine responsible for gathering, processing, and evaluating data to gauge the vitality and efficiency of a network. It stands as a pivotal element for organizations aiming to sustain ideal network latency, guaranteeing a smooth and responsive user experience.

Have comments or feedback? Please feel free to get in touch with us
For experiencing SONiC, Please try ONES Center  https://aviznetworks.com/one-center
For detailed case study of SONiC, please refer here

Share the Post:

Related Posts

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

  • Neekshitha Dyasani
Read more

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,

  • Anbarasan Ramalingam
Read more

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

  • Pramod Taramatta & Dorababu Arigi
Read more

Unveiling the Backbone: Exploring ONES Network Latency Measurement Backend

Explore the nuances of ONES latency measurement, serving as the bedrock of accuracy and effectiveness in network latency calculations. The backend, with its thorough data collection, processing, and analysis, guarantees a holistic grasp of the network's well-being and performance. As this blog navigates the intricacies of ONES's latency measurement, uncover the synergistic partnership of ONES and SONiC in the realm of open networking.