Multi-Docker-Eval: Docker Evaluation & Automation

• Multi-Docker-Eval is a systematic framework that quantifies Docker container overhead and benchmarks automated environment building across edge, HPC, and cloud scenarios.
• It uses controlled testbeds to measure critical metrics like fail-to-pass and commit rates, providing actionable insights into resource utilization and scalability.
• The framework informs improvements in automation agents and container performance, guiding both research in systems engineering and practical deployment strategies.

Multi-Docker-Eval denotes the rigorous, large-scale evaluation of Docker-based environments, focusing on both infrastructure resource modeling (e.g., for edge/cloud/HPC scenarios) and AI-driven software engineering automation. It encapsulates both empirical testbeds (quantifying per-container and system-wide overhead) and standardized benchmarks for assessing the ability of agents or frameworks to autonomously build, configure, and test containerized environments across heterogeneous software stacks. Recent research positions Multi-Docker-Eval as a central diagnostic and stress-test tool for containerization overhead studies, blockchain emulation at scale, and the automation of environment-building within the software engineering pipeline.

1. Definition and Scope of Multi-Docker-Eval

Multi-Docker-Eval refers to both:

1. Systematic measurement of Docker container overhead (CPU, memory, I/O, network) as the number of concurrent containers, services, and communication partners (“clients,” “servers,” “nodes”) scale—including the quantification of daemon-side and application-side resource use in edge, HPC, and cloud settings (Avino et al., 2018, Xu et al., 2017, Arango et al., 2017).
2. A formal benchmark for automatic environment building, exemplified by the “Multi-Docker-Eval” suite introduced for automated software engineering agents (Fu et al., 7 Dec 2025, Zhang et al., 31 Jan 2026). This benchmark evaluates end-to-end success in producing functional, testable Docker-based environments across diverse open-source repositories and language ecosystems.

Multi-Docker-Eval is crucial in both empirical performance engineering (determining efficiency and capacity limits) and machine learning-driven automation (quantifying the reliability of agentic approaches to infrastructure automation).

2. Benchmark Designs and Evaluation Protocols

2.1. Container Performance Testbeds

Testbeds for Multi-Docker-Eval in container overhead studies feature controlled variations in the number of running Docker containers $(N_s)$ and simulated clients $(N_c)$. A distinguished example uses an 8-core Intel i7 host and instrumented process accounting to assess Docker CPU scheduling overhead across two application classes: constant-bitrate 720p FFserver video streaming and interactive Minecraft game servers (Avino et al., 2018).

2.2. Automated Environment-Building Benchmark

The “Multi-Docker-Eval” benchmark (Fu et al., 7 Dec 2025, Zhang et al., 31 Jan 2026) comprises 334 repository-patch pairs across 40 repositories in 9 programming languages. Agents must:

• Generate a Dockerfile ($E_i$)
• Create a test script ($T_i$) that fails on original code $(R_i)$ and passes on patched code $(R_i \oplus P^*_i)$,
• Satisfy strict build ($T_\mathrm{build} \leq 1800$ s) and test ($T_\mathrm{test} \leq 2700$ s) deadlines.

Metrics include:

• Fail-to-Pass Rate (F2P):

$$\mathit{F2P} = \frac{1}{N} \sum_{i=1}^{N} \mathbf{1}[\neg T_i(R_i) \wedge T_i(R_i \oplus P^*_i)] \times 100\%$$

• Commit Rate (CR): The proportion of instances where agents submit a solution attempt.
• Resource usage: Tokens, wall time, CPU time, peak RAM, image size.

This holistic protocol covers both technical correctness and resource realism, functioning as a critical stress-test for software engineering automation.

3. Quantitative Results: Overhead and Automation Performance

3.1. Docker Overhead in Multi-Container Service Deployment

Empirical Multi-Docker-Eval reveals:

• For video streaming: Docker process overhead is negligible, invariant with $N_s$ or $N_c$. $\Delta_\mathrm{CPU} = 0$ for $N_s \leq 8,\,N_c \leq 8$ (Avino et al., 2018).
• For gaming: Overhead (per-container docker-containerd-shim) increases linearly with $N_s$, but remains a small fraction of application CPU (≈30 ticks per server per 300 s; 0.1 ticks/s).
• In deep learning and HPC: Overhead for CPU- and GPU-bound workloads is routinely $<$1% per container, enabling linear scale-out until substrate resource saturation (Xu et al., 2017, Arango et al., 2017).

3.2. Automated Environment Building: Agent Performance

Automatic environment-building agents evaluated on Multi-Docker-Eval achieve:

• Open-source SOTA model (DeepSeek-v3.1): F2P = 37.72%, CR = 52.89% (Fu et al., 7 Dec 2025)
• DockSmith (30B-A3B): F2P = 39.72%, CR = 58.28% (Zhang et al., 31 Jan 2026)
• RepoLaunch (single-agent): F2P = 8.85%, CR = 22.35% (Fu et al., 7 Dec 2025)
• Task success is strongly language-dependent: Go and Python < 45–52%; C/C++ and Rust < 20%
• Primary bottleneck is environment construction (Docker build failures = 36.1% of total failures)

Process efficiency metrics (input/output tokens, wall-time, CPU time, RAM, image size) remain within the same resource class for top models.

3.3. Large-Scale Network Emulation

Blockchain network emulation on single hosts demonstrates scalability to $N \approx 3500$ containers (Pennino et al., 2024), with RAM scaling as $\text{MEM}(N) \approx c N$, and CPU cores as $\text{CPU}_{\rm cores}(N) \approx \alpha N / x$, where $x$ (“time inflation”) staggers protocol timers to avoid CPU saturation.

4. Methodologies and Best Practices

4.1. Overhead Measurement

• Instrument /proc/PID/stat (for dockerd, containerd, shim processes) at 1 Hz
• Compute $$\Delta_\mathrm{CPU}(N_s,N_c) = C_\mathrm{active}(N_s,N_c) - C_\mathrm{idle}$$
• Express as Overhead% relative to application CPU: $100 \times \Delta_\mathrm{CPU} / C_\mathrm{app}$
• Sweep $N_s$ and $N_c$; assess scaling and per-service patterns (Avino et al., 2018)
• For I/O: Layered filesystems (AUFS) are costly for random read/write (Arango et al., 2017), but negligible for deep learning-dominated workloads (Xu et al., 2017)

4.2. Automated Benchmark Execution

• Use containerized agents (multi-agent, memory-augmented workflows) for environment building (Fu et al., 7 Dec 2025, Zhang et al., 31 Jan 2026)
• Ensure strict separation of build and test phases with fine-grained error accounting
• Resource isolation (cpuset, memory), base image slimming, and mounting data via volumes prevent image bloat and noisy-neighbor artifacts (Xu et al., 2017)
• In large-scale network scenarios: ARP suppression, static forwarding, and class-based netem/tc for network delay injection are critical to linear scaling (Pennino et al., 2024)

4.3. Comparative Performance and Scalability

Scenario	Overhead (%)	Container Limit	Scaling Constraint
Edge video streaming	0	$N_s=8$, $N_c=8$	Network IO
Deep learning training	<1	$N=5$–$8$/GPU	PCIe, Data IO
HPC compute (CPU/GPU)	3–8 (Docker), ~1 (Singularity)	Up to node RAM	Network, disk IO
Blockchain emulation	Linear RAM, CPU/x	$N\geq 3\,000$	Host RAM, kernel

5. Error Analysis and Bottlenecks

In automated benchmarks:

• Most errors stem from inaccurate system-dependency inference (header packages, compiler flags) (Fu et al., 7 Dec 2025)
• Dockerfile and test script failures dominate critical error classes; eval-script patching and test-analysis errors decline with agentic feedback and memory-sharing, e.g., DockSmith reduces total errors by 42.5% and Dockerfile errors by 46.7% relative to baseline (Zhang et al., 31 Jan 2026).
• Environment construction (not test logic or LLM reasoning length) is the primary bottleneck to scaling SWE automation (Fu et al., 7 Dec 2025)

In container-based emulation:

• Network stack overhead (bridge+NAT) induces up to 17% bandwidth penalty and 43% higher latency (Arango et al., 2017)
• AUFS layered file systems inflict up to 65% penalty on random IO (Arango et al., 2017); host mounting or tmpfs recommended for IO-intensive containers (Xu et al., 2017).

6. Extensions, Practical Guidelines, and Limitations

Best practices and extension guidelines include:

• Prefer multi-agent, feedback-driven (loop-detection, cross-task memory) pipelines for higher F2P in automation (Fu et al., 7 Dec 2025, Zhang et al., 31 Jan 2026).
• Tune host network settings (use --network=host) for high-performance computing/multi-node, and avoid AUFS for IO (Arango et al., 2017).
• For deep learning orchestration: pin GPUs (docker --gpus), isolate CPUs (cpuset), monitor per-container utilization, and co-locate via orchestrators (Xu et al., 2017).
• For blockchain emulation at scale: pre-generate overlays, exploit static forwarding and ARP suppression, utilize time inflation, and tune kernel/network stack for high-N scenarios (Pennino et al., 2024).
• RAM is the primary constraint on large-scale single-host emulation; CPU can be throttled, but kernel and OS limits (BR_MAX_PORTS, file descriptor caps) must be raised (Pennino et al., 2024).

7. Research Significance and Future Prospects

Multi-Docker-Eval provides a diagnostic and composable laboratory for studying resource overhead, automation bottlenecks, and scalability at the intersection of systems, machine learning, and software engineering research. Its methodologies enable reliable quantification of container scaling behavior (for both compute- and network-bound workloads), comparative assessment of isolation technologies, and standardized benchmarking of agentic environment builders operating on diverse, real-world codebases.

Research directions indicated include:

• Enhanced causal analysis of dependency-resolution failures in automation agents
• Further integration with orchestration frameworks (Kubernetes, Docker Swarm) for massive emulations
• Expansion to new domains (e.g., P2P, IoT, distributed AI training) leveraging the existing best practices and quantitative scaling laws established in foundational Multi-Docker-Eval studies (Fu et al., 7 Dec 2025, Zhang et al., 31 Jan 2026, Pennino et al., 2024).

Multi-Docker-Eval has become the de facto standard for both micro-architectural evaluation of container environments and for assessing progress in the long-horizon automation of executable software engineering workflows.