Analyzing the Performance of Linux Networking Approaches for Packet Processing : A Comparative Analysis of DPDK, io_uring and the standard Linux network stack
Bibliographic record
Abstract
This thesis investigates the network performance of DPDK, io_uring, and the standard Linux network stack in terms of latency, packet loss, throughput, and packet rate. DPDK is widely adopted in the telecommunication industry and has been for several years. io_uringis a new Linux system call interface, as of this writing, used for asynchronous I/O operations and could potentially offer better network performance than the standard Linux network stack. The analysis involves benchmarking of DPDK, io_uring and Linux network stack both with a continuous flow of data traffic and bursty traffic to see how the applications are able to handle different kinds of data traffic conditions. The Linux kernel network parameters were tuned in order to investigate what kind of performance increase can be achieved for an application using io_uring to gain a better understanding of howio_uring compares to DPDK. From the analysis, it was concluded that DPDK had the best overall performance when considering packet loss, throughput, latency, and packet rate.io_uring performed better than the Linux network stack in terms of throughput, packet loss, and packet rate but was not considerably better in terms of latency. When comparing the latency of bursty traffic for DPDK, io_uring, and the standard Linux network stack, it was shown that the kernel-based alternatives were better able at handling traffic over their maximum performance than DPDK, thus it may offer better scalability. After tuning the Linux kernel network parameters it was concluded that tuning may impact the performance network metrics of an application using io_uring. We conclude that io_uring shows some promising results compared to the standard Linux network stack, but is not yet able to compare with DPDK in network performance. The development of io_uring is ongoing and it may improve further in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".