eBPF-Based Telemetry System

• eBPF-based telemetry system is a dynamic kernel instrumentation framework that leverages LLVM-compiled eBPF bytecode for safe, fine-grained metric extraction.
• It enables precise monitoring by attaching probes to kernel hooks such as tracepoints, kprobes, and system calls to aggregate performance data in real time.
• Widely applied in SRv6 routing, cloud diagnostics, and network convergence, it delivers near-native performance with minimal overhead.

An eBPF-based telemetry system leverages the dynamic, safe, and efficient programmability of the Linux kernel provided by the extended Berkeley Packet Filter (eBPF) virtual machine for fine-grained in-kernel observation, metric extraction, and programmable monitoring. Such systems have become central components for extracting, aggregating, and exporting telemetry data in networking, cloud-native, and service-oriented environments, with concrete validation in routing (notably SRv6), network diagnostics, real-time system monitoring, and dynamic protocol adaptation.

1. Kernel Integration and Execution Model

eBPF-based telemetry is enabled by the in-kernel eBPF runtime, in which users load custom programs compiled via LLVM into safe bytecode and inject them at kernel hooks. The eBPF runtime integrates a static verifier that enforces control-flow integrity, memory and type safety, and execution bounds; following verification, the code is Just-In-Time (JIT) compiled to native instructions (Gbadamosi et al., 16 Sep 2024). eBPF programs can be attached to a variety of dynamic kernel hooks (such as sockets, system calls, tracepoints, cgroups, or networking stack functions), allowing telemetry to be performed at critical points in the data or control path.

A simplified pipeline for telemetry is as follows:

1. Write eBPF program in a high-level language (typically C or a domain-specific language).
2. Compile to eBPF bytecode via LLVM.
3. Load and verify via system calls such as BPF_PROG_LOAD; verifier uses symbolic execution and precise resource analysis.
4. Attach to kernel event/hook; execution triggered at runtime on specified events (e.g., packet arrival, syscall entry, context switch).
5. Use kernel helper functions and in-kernel maps for data collection, state management, and efficient user–kernel data exchange.
6. Data is either acted on immediately (e.g., in-network telemetry, filtering, modification) or exported to user space for further aggregation.

This design yields near-native performance (due to JIT), safety (due to static verification), and dynamic deployability—all critical for modern telemetry systems.

2. Instrumentation Techniques and Metric Collection

eBPF offers unprecedented granularity for instrumentation by allowing the placement of fine-grained probes at the kernel layer. Telemetry programs typically achieve this by:

• Attaching kprobes/tracepoints to performance-critical kernel functions (e.g., vfs_read/write, sock_recvmsg, sched_switch, system call entry/exit, and protocol state transitions).
• Using BPF maps for persistent state and cross-event aggregation (e.g., per-thread, per-connection, per-resource statistics).
• Collecting time-resolved metrics directly via kernel timers or extracting them from kernel state objects (e.g., tcp_sock for connection KPIs).

A concrete illustration is the use of 16 eBPF-based metrics across six kernel subsystems for diagnosing performance degradation in online data-intensive applications (Landau et al., 19 May 2025). These include:

Subsystem	Metric Examples	Granularity
Scheduling	runtime, rq_time, sleep_time	thread
Futex (locks)	futex_wait_time/count	thread→futex
VFS/Pipes	pipe_wait_time/count	thread→pipe
Network	socket_wait_time/count	thread→socket (by 5-tuple)
Epoll	epoll_wait_time/count	thread→epoll resource
Block IO	sector_count	thread/device

This fine-grained observation allows discrimination of bottlenecks such as lock contention (futex metrics), disk bottlenecks (iowait, sector_count), CPU contention (rq_time), or multiplexed IO delays (epoll metrics).

In network telemetry, eBPF is employed to instrument protocol headers, extract timestamps from traffic (e.g., delay measurement for SRv6), perform passive KPIs collection (e.g., bytes sent, RTT per connection), or implement programmable path tracking and multipath discovery (Xhonneux et al., 2018).

3. Programmable Telemetry via eBPF APIs and Extensions

To safely program custom telemetry logic, the kernel exposes domain-specific helper functions. For example, the SRv6 implementation provides helpers such as bpf_lwt_seg6_store_bytes (controlled SRH field write), bpf_lwt_seg6_adjust_srh (dynamically adjusting TLV storage), and bpf_lwt_seg6_action (invoking additional SRv6 actions) (Xhonneux et al., 2018). Telemetry programs receive read-only packet or syscall parameters and must return integer codes indicating action (e.g., BPF_OK, BPF_DROP, or BPF_REDIRECT).

Use cases directly addressed include:

• Passive/Active Delay Measurement: Custom SRH TLVs carrying timestamps for one-way or round-trip latency monitoring; eBPF extracts, processes, and exports statistics at line rate, with negligible performance penalty (3–5% overhead, further reduced with JIT).
• Fast Reroute and Failure Detection: In-situ SRv6 eBPF programs enable programmable failover; detect link failures (based on timers and event telemetry) and immediately reroute packets through alternative segment lists (Xhonneux et al., 2018).
• Connection-Level KPIs: Flowcorder places probes at key TCP state transitions to collect and export KPIs (e.g., $\mathcal{A}\text{RTT}$, $\sum \text{Retransmissions}$) over IPFIX, achieving sub-1% overhead in microbenchmarks (Tilmans et al., 2019).
• Congestion Control Adaptation: Injecting new TCP options and dynamically tuning protocol parameters (timeouts, ACK strategy) on a per-connection basis via eBPF (Tran et al., 2019).

These mechanisms allow operators to program telemetry logic that is dynamically loaded, updated at runtime, and strictly constrained to kernel-safe operations.

4. Architecture, Performance, and System Overhead

Evaluations show eBPF-based telemetry can deliver high throughput, low overhead, and robust safety:

• Minimal throughput degradation versus in-kernel baselines: typical eBPF actions (e.g., minimal SRv6 End function) incur ~3% overhead, more complex programs (e.g., End.T with SRH action insertion) only ~5% (Xhonneux et al., 2018).
• JIT compilation provides a further 1.8× speedup over interpreted eBPF execution.
• Traffic monitoring functions (e.g., STAMP delay monitoring in SD-WANs) deliver nearly line-rate performance (up to ~3 million packets/s processed); routers maintain >98% of baseline forwarding even under heavy measurement traffic (Scarpitta et al., 2022).
• Telemetry programs operate with low CPU utilization, e.g., ~1.21% CPU overhead at 100 Hz sampling for host-GPU correlation (Darzi et al., 19 Oct 2025).
• Careful placement of probes (outside the packet fast path when possible) ensures negligible latency increases for applications (<0.02%), with only a few performance profiles exported per connection.

A core design challenge is balancing flexibility and safety. Direct memory writes, packet modifications, or resource manipulation are restricted to safe subsets of kernel state. All user programs are statically verified, and dynamic race conditions are minimized via atomic primitives and in-kernel concurrency controls.

5. Use Cases and Real-World Impact

eBPF-based telemetry frameworks are applied in diverse domains:

Domain	Applications / Capabilities
Routing (SRv6/NFV/SDN)	Programmable delay monitoring, fast reroute, service chaining, hybrid WAN aggregation (Xhonneux et al., 2018, Xhonneux et al., 2018, Scarpitta et al., 2022)
Protocol/Transport	Per-connection KPIs, adaptive protocol extensions, multipath telemetry via eBPF injection (Tilmans et al., 2019, Tran et al., 2019)
Cloud/HPC Infrastructure	Host-side telemetry for GPU-tail latency diagnostics; root cause analysis (NIC, PCIe, CPU) (Darzi et al., 19 Oct 2025)
Telemetry and Diagnostics	Fine-grained lock, disk, CPU, and multiplexing IO contention diagnosis in data-intensive services (Landau et al., 19 May 2025)
Distributed Tracing	In-kernel, non-intrusive context propagation and request tracking, language-neutral (Yang et al., 2023)

The flexibility of eBPF in supporting both in-band packet manipulation and out-of-band KPI extraction has enabled its integration into production-grade Linux routers (e.g., kernel 4.18+), end-host performance monitors, multi-tenant cloud orchestration (via federated agents), and distributed tracing in microservices.

6. Advantages, Limitations, and Future Directions

Advantages:

• Dynamic Programmability: Telemetry logic can be changed on-the-fly—no kernel rebuilds or module reloads; supports rapid prototyping and operations.
• Safety: Statically verified with strong guarantees on control flow, memory access, and kernel integrity.
• Low Overhead: Achieves high performance with negligible impact on forwarding/data path throughput.
• Integration with Existing Toolchains: eBPF programs can interact with user space via BPF maps, perf events, and ringbuffers; supports IPFIX, gRPC, and custom APIs for exporting metrics.

Limitations:

• Expressiveness Constraints: Static verifier restricts program complexity (e.g., unbounded loops, complex indirect access), which can limit expressiveness of deeply stateful telemetry.
• Deployment Scope: Requires Linux kernel support (≥ 4.14–4.18 for most features). Porting to other platforms is non-trivial; some advanced features are not universally available.
• Interpreting Fine-Grained Metrics: Rich kernel-level metrics require domain knowledge to interpret, especially in multi-service or highly concurrent architectures.
• Security Considerations: While safety is strong, privilege requirements for eBPF loading can restrict adoption in certain multi-tenant scenarios; mitigations (e.g., SafeBPF) are emerging to address this (Lim et al., 11 Sep 2024).

Future Directions:

• Further Ecosystem Integration: New helper functions, richer data exchange primitives (e.g., BPF ringbuffers), and higher-level languages and toolchains are lowering the barrier for developing safe, efficient telemetry programs.
• Advanced Telemetry and Control: With ongoing work on hardware assistance (e.g., memory tagging) and verified DSLs (e.g., BeePL (Priya et al., 14 Jul 2025)), telemetry extensions could be designed with stronger formal correctness and safety guarantees.
• Cross-Platform Deployment: Integration with platform-independent frameworks (e.g., Wasm-bpf (Zheng et al., 9 Aug 2024)) could enable seamless distribution of telemetry programs across heterogeneous cloud and edge deployments.
• Automated Analysis and Adaptive Response: Combining eBPF-based telemetry with AI/ML pipelines (as shown in federated anomaly detection agents (Zehra et al., 11 Oct 2025)) will drive increasingly autonomous, privacy-preserving observability infrastructures.

eBPF-based telemetry systems have thus emerged as powerful, low-overhead primitives for kernel and network observability, offering the flexibility and programmability required for modern cloud, SDN/NFV, and application-dense environments while remaining grounded in the strict safety and efficiency demands of kernel programming.