Why Low-Latency Is Crucial For Real-Time Applications In Data Centers

Real-time applications, by definition, are applications that should work without any delay. Latency creates delays and, hence, prevents real-time applications from delivering optimal performance. This means that data centers need to do everything possible to minimize latency for real-time applications. Here is a quick guide to what you need to know about this.

Understanding the growth of real-time applications

Real-time applications are applications that must deliver data or services without noticeable delay to users. This means they need immediate processing and response to input within a specific time frame, often measured in milliseconds or microseconds.

Over recent years, there has been a significant increase in the use of real-time applications in all areas of life. Probably the most obvious example of this is the internet of things (IoT). Many IoT applications are real-time applications. Other common examples include real-time communications (e.g. videocalling), video streaming (e.g. online education), and online trading systems.

Moreover, the proliferation of real-time applications has raised user expectations in other areas. For example, at this point, there are still relatively few websites that need to operate in real-time. Increasingly, however, users demand responses in real-time (or very close to it).

Understanding latency

In the context of data centers, latency refers to the delay or lag that occurs between the initiation of a data transfer request and the receipt of a response. This delay is primarily caused by the time it takes for data to travel between different components within the data center infrastructure. Latency is measured in milliseconds (ms) or microseconds (μs) and is a critical metric for assessing the responsiveness and performance of data center applications.

Types of latency

There are three main types of latency that are common in data centers. Here is an overview of them.

Network latency

Network latency, also known as round-trip time (RTT), refers to the time it takes for data packets to travel from the source to the destination and back again. It includes the propagation delay caused by the physical distance between network devices and the processing delay incurred by routers, switches, and other networking equipment. Network latency can be influenced by factors such as bandwidth limitations, network congestion, and packet loss.

Processing latency

Processing latency, also referred to as server or compute latency, is the time it takes for a server or computing device to process incoming data requests and generate a response. This latency can be affected by the processing power of the server, the efficiency of the operating system and software applications, and the workload on the server at any given time. Processing latency can vary depending on the complexity of the task being performed and the resources available to the server.

Storage latency

Storage latency is the delay experienced when accessing data stored on disk or solid-state drives (SSDs) within the data center storage infrastructure. It includes the time required for the storage device to locate and retrieve the requested data, as well as any additional overhead associated with data transmission. Storage latency can be influenced by factors such as disk seek times, rotational delay (in the case of spinning disks), and the performance characteristics of the storage medium (e.g., read/write speeds).

Factors contributing to latency

Here is an overview of the five main factors that contribute to latency with suggestions on how they can be addressed.

Distance

One approach to mitigate distance-related latency is to deploy content delivery networks (CDNs) that cache and serve content from edge servers located closer to end users. Additionally, leveraging technologies like multicasting and anycast routing can optimize data delivery paths and reduce latency for geographically distributed applications.

Network congestion

To address network congestion, organizations can implement Quality of Service (QoS) policies to prioritize critical traffic types, such as real-time applications or VoIP calls, over less time-sensitive traffic. Additionally, optimizing network routing algorithms and upgrading network bandwidth capacity can help alleviate congestion and reduce latency.

Packet processing and queuing delays

To minimize packet processing and queuing delays, organizations can deploy high-performance network devices with large buffer sizes and fast forwarding capabilities. Additionally, implementing traffic engineering techniques, such as packet prioritization and load balancing, can help optimize packet flow and reduce latency.

Hardware and software processing times

To reduce hardware and software processing times, organizations can invest in high-performance servers and networking equipment with optimized hardware accelerators, such as specialized network interface cards (NICs) or encryption offload engines. Additionally, optimizing software algorithms and configurations can help streamline data processing and minimize latency.

Propagation delay and medium limitations

To address propagation delay and medium limitations, organizations can deploy high-quality networking cables with low attenuation and invest in fiber-optic infrastructure, which offers higher bandwidth and longer transmission distances compared to traditional copper cables. Additionally, minimizing the number of signal regeneration points and optimizing cable routing can help reduce propagation delay and improve overall network performance.