Edge computing has a key role in reducing latency and congestion problems and improving application performance for telco and 5G network applications. Maintaining a network architecture with the lowest possible latency is key for meeting the network performance requirements of 5G. Compared to 4G technology, with an average latency of 50 ms, 5G is targeted to reach latency of 1 ms or less. This reduction in latency boosts wireless throughput by a factor of 10.

About low latency

Many of the deployed applications in the Telco space require low latency that can only tolerate zero packet loss. Tuning for zero packet loss helps mitigate the inherent issues that degrade network performance. For more information, see Tuning for Zero Packet Loss in OpenStack.

The Edge computing initiative also comes in to play for reducing latency rates. Think of it as being on the edge of the cloud and closer to the user. This greatly reduces the distance between the user and distant data centers, resulting in reduced application response times and performance latency.

Administrators must be able to manage their many Edge sites and local services in a centralized way so that all of the deployments can run at the lowest possible management cost. They also need an easy way to deploy and configure certain nodes of their cluster for real-time low latency and high-performance purposes. Low latency nodes are useful for applications such as Cloud-native Network Functions (CNF) and Data Plane Development Kit (DPDK).

OKD currently provides mechanisms to tune software on an OKD cluster for real-time running and low latency (around <20 microseconds reaction time). This includes tuning the kernel and OKD set values, installing a kernel, and reconfiguring the machine. But this method requires setting up four different Operators and performing many configurations that, when done manually, is complex and could be prone to mistakes.

OKD uses the Node Tuning Operator to implement automatic tuning to achieve low latency performance for OKD applications. The cluster administrator uses this performance profile configuration that makes it easier to make these changes in a more reliable way. The administrator can specify whether to update the kernel to kernel-rt, reserve CPUs for cluster and operating system housekeeping duties, including pod infra containers, and isolate CPUs for application containers to run the workloads.

OKD also supports workload hints for the Node Tuning Operator that can tune the PerformanceProfile to meet the demands of different industry environments. Workload hints are available for highPowerConsumption (very low latency at the cost of increased power consumption) and realTime (priority given to optimum latency). A combination of true/false settings for these hints can be used to deal with application-specific workload profiles and requirements.

Workload hints simplify the fine-tuning of performance to industry sector settings. Instead of a “one size fits all” approach, workload hints can cater to usage patterns such as placing priority on:

  • Low latency

  • Real-time capability

  • Efficient use of power

Ideally, all of the previously listed items are prioritized. Some of these items come at the expense of others however. The Node Tuning Operator is now aware of the workload expectations and better able to meet the demands of the workload. The cluster admin can now specify into which use case that workload falls. The Node Tuning Operator uses the PerformanceProfile to fine tune the performance settings for the workload.

The environment in which an application is operating influences its behavior. For a typical data center with no strict latency requirements, only minimal default tuning is needed that enables CPU partitioning for some high performance workload pods. For data centers and workloads where latency is a higher priority, measures are still taken to optimize power consumption. The most complicated cases are clusters close to latency-sensitive equipment such as manufacturing machinery and software-defined radios. This last class of deployment is often referred to as Far edge. For Far edge deployments, ultra-low latency is the ultimate priority, and is achieved at the expense of power management.

About Hyper-Threading for low latency and real-time applications

Hyper-Threading is an Intel processor technology that allows a physical CPU processor core to function as two logical cores, executing two independent threads simultaneously. Hyper-Threading allows for better system throughput for certain workload types where parallel processing is beneficial. The default OKD configuration expects Hyper-Threading to be enabled.

For telecommunications applications, it is important to design your application infrastructure to minimize latency as much as possible. Hyper-Threading can slow performance times and negatively affect throughput for compute-intensive workloads that require low latency. Disabling Hyper-Threading ensures predictable performance and can decrease processing times for these workloads.

Hyper-Threading implementation and configuration differs depending on the hardware you are running OKD on. Consult the relevant host hardware tuning information for more details of the Hyper-Threading implementation specific to that hardware. Disabling Hyper-Threading can increase the cost per core of the cluster.