Infrastructure-Aware Benchmarking

• Infrastructure-aware benchmarking is a methodology that decouples resource configuration from performance testing using Infrastructure-as-Code, enhancing reproducibility and modularity.
• It automates workload deployment and measurement across domains like cloud, HPC, and edge, enabling direct comparison and optimization of hardware resources.
• Benchmarks leverage modular design, explicit resource fingerprinting, and continuous integration to deliver statistically robust and scalable performance insights.

An infrastructure-aware benchmarking framework is a system or set of tools explicitly designed to evaluate and compare the performance, reliability, and resource characteristics of computational environments while being cognizant of the underlying hardware, virtualization, deployment configurations, or network topologies. Such frameworks automate the measurement of representative workloads or synthetic tasks in a manner that is reproducible, modular, and extensible. The goal is to produce performance metrics that are directly attributable to infrastructure properties, rather than to unrelated configuration artifacts or unobserved system drift. Key implementations—such as those in public cloud, data center, big data, edge, robotics, and HPC domains—employ design patterns and methodologies to ensure that benchmarking accurately reveals the true capacity and variability of the target environment, thus supporting data-driven selection and optimization.

1. Principles and Rationale

The foundational principle of infrastructure-aware benchmarking is the decoupling of benchmark definitions, workload deployment, and performance measurement from ad hoc manual processes. Instead, infrastructure representation and experimental workflow are described programmatically, often using Infrastructure-as-Code (IaC) paradigms (e.g., declarative resource specifications with Vagrant—Cloud WorkBench (Scheuner et al., 2014); containerized microservices—Plug and Play Bench (Ceesay et al., 2017); modular deployment pipelines—SProBench (Kulkarni et al., 3 Apr 2025), QBIT (2503.07479)).

This approach achieves several core objectives:

• Reproducibility: Infrastructure is described in code, ensuring benchmark environments and provisioning steps are consistent across executions and sites.
• Modularity and extensibility: Benchmark components, resource setup, and provisioning logic are decoupled, allowing component reuse and easy extension to new scenarios or hardware.
• Testability and version control: Code-based definitions enable rigorous regression testing, git-based version tracking, and systematic validation of benchmark logic and resource allocation.

Explicit definition of the environment under test, in parallel with automated deployment and measurement, reduces confounding effects inherent to manual configuration (e.g., VM setup drift, software version mismatches).

2. Architectural Patterns and Core Components

Common design patterns, as seen across multiple frameworks, include:

Framework/Domain	Infrastructure Management	Benchmark Isolation/Execution	Metrics Collection & Analysis
Cloud WorkBench (Scheuner et al., 2014)	IaC (Vagrant, Chef)	Orchestrator server, client library	Results DB, periodic scheduling
SProBench (Kulkarni et al., 3 Apr 2025)	SLURM batch, CLI-based deploy	Modular generator/broker/pipeline	JMX, system monitors, time-series logs
Plug & Play Bench (Ceesay et al., 2017)	Cluster-aware container setup	Containerized HiBench workloads	Performance, cost computation
QBIT (2503.07479)	Kubernetes, YAML experiment	Microservices, container orchestration	Aggregated CSV, force/energy metrics

Central Orchestrator/Controller: Manages resource acquisition (via provider APIs, job schedulers, or Kubernetes), state tracking, scheduling, and artifact collection. For example, CWB's orchestrator maintains benchmark state using a relational database and web interface, while SProBench leverages automation scripts with SLURM for cluster-wide control.

Infrastructure-Definition Layer: Benchmarks encapsulate infrastructure needs (e.g., VM type, node topology, physical resource binding). In Cloud WorkBench, Vagrant’s Ruby DSL and Chef cookbooks define VM images and application stacks. QBIT prescribes YAML files capturing randomized experiment parameters and target container images.

Workload Execution and Monitoring: Benchmarks are executed by agent code or isolated containers. These agents not only run the benchmarks but also push state and results to the central service, ensuring proper lifecycle management (e.g., "WAITING FOR START PREPARING" → "RUNNING" → "FINISHED" in CWB).

Result Collection and Storage: Detailed performance measures are parsed, stored, and post-processed for further analysis. Formats include CSV, time-series databases (e.g., InfluxDB in continuous HPC benchmarking (Alt et al., 3 Mar 2024)), and centralized online repositories to facilitate traceability and cross-experiment comparisons (Mohammadi et al., 2018).

3. Infrastructure Awareness in Benchmarking Methodologies

Infrastructure-aware frameworks differ from generic benchmarking systems in that they make resource characteristics an explicit part of the benchmarking process:

• Explicit Resource Fingerprinting: Perona (Scheinert et al., 2022) uses standardized, fixed-configuration benchmarks (e.g., sysbench, fio) to obtain directly comparable metrics across machines. Autoencoder-based reduction discards uninformative metrics, creating low-dimensional representations that allow accurate comparison and anomaly detection.
• Emulation and Trace Adaptation: DCNetBench (Liu et al., 2023) builds emulated networks that replicate data center configurations, runs real-world workloads to capture representative traces, and replays these traces to benchmark switch chips or topology changes.
• Containerization and Orchestration: QBIT (2503.07479) and Plug and Play Bench (Ceesay et al., 2017) use Docker/Kubernetes to encapsulate workloads, promoting repeatability across platforms and scaling up experiments for statistical significance.
• Continuous Integration with System Profiling: HPC frameworks (Alt et al., 3 Mar 2024) deploy automated benchmarking pipelines triggered by version control activity, using node-specific job scheduling and system performance counter instrumentation (likwid, Nsight Compute) to tightly relate code changes to hardware-specific throughput.

4. Metrics, Scalability, and Experimental Rigor

The credibility of results in infrastructure-aware benchmarking flows from metrics selection, statistical handling, and scalability support:

• Metric Standardization and Preprocessing: Perona ensures cross-host comparability via fixed-units and value orientation, later using learned embeddings for clustering and ranking.
• Composite and Multi-dimensional Metrics: QBIT goes beyond binary success, aggregating force energy, completion time (e.g., $E = \frac{1}{N}\sum_{n=0}^N |F(n)|^2$), force smoothness, and reliability to give a multidimensional view of insertion quality.
• Performance Variability Assessment: CWB quantifies intra- and inter-execution variability as standard deviations (e.g., 10–20% in disk I/O) to isolate inherent infrastructure instability from measurement error.
• Scalability: SProBench demonstrates near-linear scaling of throughput up to 40 million events/sec on large HPC systems, leveraging SLURM for resource allocation and highlight the precise points at which platform bottlenecks arise.

Patterns here emphasize not only high-throughput benchmarking but rigorous, comparable, and reproducible measurement across scales and hardware heterogeneity.

5. Representative Use Cases and Applications

Infrastructure-aware benchmarking frameworks address a wide array of domains:

• Cloud Instance and Storage Evaluation: CWB rapidly compares instance/storage combinations for I/O-bound workloads, automating the entire lifecycle from deployment to metric collection (Scheuner et al., 2014).
• Data Stream Processing on HPC: SProBench natively integrates with Apache Flink, Spark Streaming, and Kafka, emphasizing both throughput (events/sec) and system-level resource utilization to characterize scaling behavior (Kulkarni et al., 3 Apr 2025).
• Big Data Cost and Performance Profiling: Plug and Play Bench combines containerized deployment of benchmarking suites (HiBench) with integration of cost metrics (e.g., Azure billing), enabling informed trade-off analyses (Ceesay et al., 2017).
• Robotic Simulation/Physical Transition: QBIT evaluates insertion algorithms via large-scale simulation (randomizing contact properties) and reliably transitions to real-world robots thanks to standardized, containerized hardware interfaces (2503.07479).
• Edge Multi-Tenancy: Automated multi-tenancy benchmarking frameworks (Georgiou et al., 12 Jun 2025) orchestrate mixed-workload deployments (e.g., ML inference, streaming analytics, database ops), quantifying the interplay of resource contention, energy usage, and workload interference on heterogeneous edge clusters.

This breadth illustrates the increasing demand for comparative, infrastructure-cognizant methodology far beyond simple synthetic micro-benchmarks.

6. Impact, Limitations, and Future Directions

The maturation of infrastructure-aware benchmarking frameworks has yielded demonstrably improved reliability, reproducibility, and extensibility in empirical studies. By codifying infrastructure descriptions, automating deployment and measurement, and providing extensible pipelines for results analysis, these frameworks have enabled:

• Direct cross-vendor and cross-instance comparison (cloud and big data),
• Large-scale, parallel, and reproducible performance micro- and macro-benchmarks (HPC, stream processing),
• Statistically robust experimental design by automating repeated and randomized trials at scale,
• Bridging of the sim-to-real gap (robotics and edge).

However, challenges remain:

• Heterogeneity: Significant engineering effort is still required to support diverse hardware platforms, cloud APIs, or evolving orchestrators.
• Metric Drift: Ensuring long-term validity of standardized metrics is non-trivial when underlying system architectures evolve.
• Extended Quality Attributes: While many frameworks excel in throughput/latency/accuracy evaluation, incorporating additional concerns (e.g., energy efficiency, resilience, cost under dynamic load, fairness) is an ongoing research focus.

Future directions identified include deeper automation (dynamic infrastructure discovery and adaptive configuration), broader benchmarking coverage (addition of new workloads, domains, and public corpora), integration with continuous deployment and testing pipelines, and more granular cost/performance modeling with real-time resource pricing or advanced optimization-guided resource selection (Ceesay et al., 2017, Scheinert et al., 2022, Kulkarni et al., 3 Apr 2025).

7. Summary Table: Cross-Section of Key Frameworks

Framework	Domain	Infra Management / Awareness	Notable Innovations
Cloud WorkBench	Cloud	IaC, Vagrant, Chef	Full IaC lifecycle automation; experiment versioning
SProBench	HPC Stream Proc.	SLURM, modular pipelines	High-throughput, near-linear scale, workflow automation
Plug & Play Bench	Big Data	Containerized, cluster-specific	Cost metrics integration; config abstraction for cloud/on-prem
QBIT	Robotics	Microservices, Kubernetes	Scalable sim-to-real; force/energy metric suite
Perona	Cloud/Big Data	Explicit host fingerprinting	Autoencoder-based compact infra profiles, anomaly detection

In summary, infrastructure-aware benchmarking frameworks enable comprehensive, systematic, and infrastructure-cognizant performance evaluation, supporting better resource selection, robust reproducibility, and actionable insights across a broad spectrum of computational environments.