The Race to Microseconds: Rethinking Observability in RDMA Systems

Modern computing is entering an era in which milliseconds are no longer precise enough to describe performance. Artificial intelligence training, high-performance computing, real-time analytics and distributed databases increasingly depend on infrastructure capable of moving enormous quantities of data with extremely small delays.

Remote Direct Memory Access (RDMA) has become an important technology in this transformation. By allowing systems to transfer data directly between memory spaces while minimizing CPU involvement, kernel processing and unnecessary memory copies, RDMA can deliver exceptionally low latency and high throughput. Technologies such as InfiniBand and RDMA over Converged Ethernet (RoCE) have consequently become important building blocks for demanding computing environments.

Yet the architecture that makes RDMA fast can also make it difficult to observe. Traditional monitoring tools were designed around operating-system networking stacks and conventional packet flows. RDMA bypasses many of those observation points, meaning a system can appear healthy while experiencing degradation inside the host, network fabric, RNIC, PCIe subsystem, storage path or application.

That observability gap is central to the research of Ajay Prasad, based out of California USA,whose published work examines latency and performance monitoring in RDMA-powered ultra-low-latency environments, as well as reliable data access for real-time applications in modern data centers.

Prasad's work approaches RDMA monitoring as more than collecting additional metrics. It focuses on understanding how multiple infrastructure layers interact and identifying where latency originates.

His research examines end-to-end and round-trip latency across hardware, kernel behavior, storage, networking, applications, system-wide resources and security. An important challenge is performing this measurement without allowing the monitoring system itself to alter the environment.

That concern becomes critical at microsecond timescales. Monitoring mechanisms that consume CPU resources, generate additional traffic or introduce synchronization overhead can change the workload they are intended to measure.

Prasad's proposed direction therefore emphasizes lightweight, domain-specific observability through background services, RDMA-aware diagnostic tools and time-series infrastructure capable of recording fine-grained performance information with minimal disruption.

RDMA deliberately reduces many of the observation points found in traditional networking to achieve lower latency. Consequently, healthy network interfaces, switches and application-level metrics do not necessarily indicate that the complete data path is healthy.

An issue can exist between the RNIC, PCIe subsystem, CPU, memory and application without becoming visible through conventional dashboards.

This makes end-to-end and round-trip latency especially important. Individual component metrics can indicate that something is unusual, but end-to-end latency shows what the application actually experiences. RTT can reveal changes in communication behavior over time, while tail latency, jitter and intermittent anomalies can expose problems hidden by averages.

Prasad's approach treats the environment as interconnected layers. At the hardware and kernel layer, monitoring can include interrupt behavior, CPU scheduling, RNIC counters, completion errors and retransmissions.

At the storage layer, I/O latency, NVMe completion times, queue depth and NVMe-over-Fabrics behavior become important, particularly in distributed environments.

At the network layer, RTT, one-way latency where accurately measurable, jitter, packet behavior, congestion indicators, ECN, PFC and queue characteristics can reveal conditions affecting performance.

At the application layer, transaction latency, API response time, processing time and application-specific RDMA operation or verb completion behavior show the impact on workloads.

The system-wide layer connects throughput, utilization, errors and resource efficiency. Security must also be considered because RDMA's direct-memory capabilities introduce security and isolation considerations that can affect performance.

A central observation in Prasad's work is that conventional monitoring can miss bottlenecks inside the host.

An RDMA server contains a critical path involving the RNIC, PCIe, CPU, memory and potentially GPUs or other accelerators. Research such as Hostping has demonstrated that problems in these internal paths can remain invisible to conventional network monitoring.

PCIe latency, memory channels, address translation, RNIC configuration and inter-socket communication can affect application performance even when the external network appears healthy.

Monitoring therefore needs to extend beyond the switch fabric and examine the complete path from infrastructure to application.

Storage is also part of the end-to-end performance path. As organizations adopt NVMe, NVMe-over-Fabrics and distributed storage architectures, storage latency increasingly becomes part of an application's latency budget. A fast network cannot compensate for storage delays, just as storage optimization cannot resolve network congestion.

An effective monitoring architecture should correlate storage events with network and application behavior. This can help determine whether application latency originates in storage, networking or interactions between components.

Prasad's approach also builds on existing monitoring technologies rather than requiring organizations to replace them. Prometheus and Grafana can provide a foundation for time-series collection and visualization. Linux performance tools can expose CPU and system behavior, while RDMA-specific utilities provide information conventional network monitoring cannot.

The opportunity lies in connecting these outputs through a layer capable of understanding their relationships. Instead of examining numerous independent dashboards, administrators should be able to determine why application latency increased.

Time-series monitoring is valuable because latency is time-dependent. Historical data can reveal trends, correlations and recurring patterns. An increase in application RTT, for example, can be compared with RNIC counters, CPU scheduling behavior, network congestion or storage latency.

A lightweight daemon on each system can collect domain-specific metrics and send them to centralized or distributed time-series infrastructure. Collection must remain efficient so that observability does not become a performance problem.

Ultra-low-latency systems should not rely on universal performance thresholds.

A high-frequency trading application, AI training cluster and industrial control system can have very different latency budgets. Metrics such as RTT, jitter, retransmissions, interrupt latency, queue depth and application completion time can provide starting points, but organizations should establish baselines according to their hardware, workload and topology.

Monitoring should also detect deviations from established behavior. An unexpected change can often be more informative than an absolute threshold.

Security belongs within this model as well. RDMA provides powerful direct-access capabilities but introduces security considerations that traditional networking architectures handle differently. Research has demonstrated vulnerabilities involving packet injection and other RDMA-specific attack surfaces.

Performance isolation is another concern in multi-tenant environments. A malicious or poorly behaved workload can potentially affect other workloads through congestion or resource contention. Monitoring should therefore consider both security events and performance degradation associated with isolation or resource contention.

Collecting more metrics does not automatically create better observability.

Indiscriminate collection can become counterproductive when measurements consume resources or affect workload behavior. The objective should instead be meaningful telemetry: Identifying the signals that explain application behavior and collecting them with as little overhead as possible.

Domain knowledge is essential because monitoring systems need to understand RDMA semantics rather than simply treating RDMA traffic like conventional network traffic.

Prasad's research points toward increasingly intelligent telemetry capable of identifying anomalies and potentially predicting performance degradation before it becomes an application-impacting event.

Another direction is tighter integration between hardware telemetry, network telemetry and application observability. Monitoring systems could increasingly connect changes in application latency with underlying infrastructure automatically.

Interoperability will also become important as organizations operate heterogeneous environments. Monitoring solutions should not force organizations into a single technology or vendor ecosystem.

The broader objective is highly efficient observability that approaches zero additional workload impact while providing deeper visibility.

The larger significance of Prasad's research lies in recognizing that RDMA monitoring cannot simply be an adaptation of conventional data-center monitoring.

The architecture has changed, and observability must change with it.

In conventional environments, administrators can often rely on operating-system counters, packet captures, application logs and relatively coarse measurements. In RDMA-powered systems, critical events can occur at timescales where those mechanisms provide an incomplete picture.

The answer is not necessarily to abandon established technologies. Instead, they can be connected with RDMA-specific telemetry and incorporated into a monitoring architecture that understands the complete system.

That means examining the RNIC, PCIe path, CPU and memory behavior, storage, network fabric, application transactions and security posture together. It also means measuring averages alongside jitter and tail latency while ensuring that telemetry itself does not undermine system performance.

Prasad's work points toward a holistic model of observability in which monitoring is lightweight, continuous, layered and capable of correlating signals across the infrastructure.

The future of high-performance computing will increasingly be defined by systems in which every microsecond matters.

RDMA has changed the performance equation by enabling data to move with dramatically less software overhead. The next challenge is ensuring that organizations can understand, maintain and secure these environments without compromising performance.

The most effective monitoring architecture will not simply collect the greatest volume of data. It will identify the signals that matter, capture them at the appropriate granularity, correlate them across system boundaries and provide actionable insight with minimal overhead.

This is the problem Ajay Prasad's work seeks to address.

By bringing together RDMA-specific diagnostics, end-to-end latency analysis, time-series monitoring, lightweight background services and cross-layer correlation, his research offers a practical direction for organizations navigating increasingly complex ultra-low-latency infrastructure.

The ultimate objective is larger than measuring latency: Making previously invisible performance behavior measurable, understandable and actionable, so that the pursuit of microsecond performance does not come at the cost of operational visibility.

This article was written in cooperation with Will Jones