Cloud infrastructure significantly impacts benchmarking results by introducing variability and environmental factors that differ from traditional on-premises setups. In cloud environments, resources like CPU, memory, and storage are shared, dynamically allocated, and subject to fluctuating performance based on demand. For example, a virtual machine (VM) in the cloud might share physical hardware with other tenants, leading to “noisy neighbor” effects where another user’s workload consumes resources and slows down your tests. Even minor changes in network latency or disk I/O caused by shared infrastructure can skew results, making it harder to isolate the performance of the system being tested.
Replicating benchmarking tests consistently is another challenge in the cloud. Providers often update hardware or software configurations without notice, and instances of the same type (e.g., AWS EC2’s “m5.large”) might run on different underlying hardware generations. A test run today could yield different results tomorrow, even with identical settings. For example, a storage benchmark using cloud disks might show inconsistent throughput if the provider’s backend storage layer is under heavier load during one test. To address this, developers often run benchmarks multiple times and average results, or use provider-specific tools (like AWS CloudWatch) to monitor resource usage during tests and filter out anomalies.
Finally, cloud benchmarking requires careful planning to match the test environment to real-world use cases. For instance, testing a database’s performance in a single-region cloud VM might not reflect how it behaves in a multi-region deployment with cross-zone latency. Developers must also account for ephemeral resources like burstable CPU credits (e.g., AWS T3 instances) or preemptible VMs (e.g., Google Cloud Spot VMs), which can throttle performance mid-test. Tools like Kubernetes orchestration or infrastructure-as-code (e.g., Terraform) help standardize environments, but ultimately, cloud benchmarks should include clear documentation of the environment’s configuration, resource limits, and any observed variability to make results actionable.