A Memory Pool Allocator for eBPF Applications

eBPF enables high-performance kernel-level execution by eliminating networking stack traversal and context switching. Despite the advantages, eBPF applications face strict memory management constraints due to the eBPF verifier requirements that mandate static memory allocation. This limitation imposes a fundamental tradeoff between application performance and memory efficiency, ultimately restricting the potential of eBPF. We present Kerby, a dynamic memory pool allocator for eBPF that enables eBPF applications to dynamically manage pre-allocated memory by representing variable-length data as collections of fixed-size blocks. This allows applications to increase the amount of kernel-resident data while minimizing internal fragmentation. Our preliminary evaluation with key-value store implementations demonstrates that Kerby achieves significant improvements in both memory utilization and throughput.

Authors

Institutions

Publication Details

Published
2025-08-19
DOI
https://doi.org/10.1145/3748355.3748370
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Memory Pool Allocator for eBPF Applications

Dongsu Han, Gyuyeong Kim
Parallel Computing and Optimization Techniques
article

A Memory Pool Allocator for eBPF Applications

Dongsu Han, Gyuyeong Kim
article en

Abstract

eBPF enables high-performance kernel-level execution by eliminating networking stack traversal and context switching. Despite the advantages, eBPF applications face strict memory management constraints due to the eBPF verifier requirements that mandate static memory allocation. This limitation imposes a fundamental tradeoff between application performance and memory efficiency, ultimately restricting the potential of eBPF. We present Kerby, a dynamic memory pool allocator for eBPF that enables eBPF applications to dynamically manage pre-allocated memory by representing variable-length data as collections of fixed-size blocks. This allows applications to increase the amount of kernel-resident data while minimizing internal fragmentation. Our preliminary evaluation with key-value store implementations demonstrates that Kerby achieves significant improvements in both memory utilization and throughput.

Korea Advanced Institute of Science and Technology (KR), Sungshin Women's University (KR)
Affordable and clean energy
Openalex Percentile: Top 78%
Parallel Computing and Optimization Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Memory Pool Allocator for eBPF Applications — Dongsu Han, Gyuyeong Kim · (2025) | TGRS Research Map | TGRS