Open vs Closed Workload Models: k6, Artillery and JMeter Measured

A TestMu AI/HyperExecute-authored comparison examines how workload models and load-testing tools affect performance measurements, while disclosing that the publisher, author, reviewer, and managed JMeter platform are connected. Using a Node.js server that slows from 50 milliseconds to 1 second for 20 seconds, the tests show that k6’s closed model with five fixed virtual users reduced request volume during the slowdown and reported a 67.07 ms p95 latency, whereas its open arrival-rate model maintained roughly 100 iterations per second, expanded virtual users, recorded dropped iterations, and showed a 1-second p95. At the same arrival rate, k6 and Artillery produced broadly similar latency results, though k6 reported 82 unstarted iterations due to its virtual-user limit while Artillery completed all scheduled arrivals and did not expose an equivalent shortfall metric. A separate JMeter plan executed on HyperExecute managed infrastructure produced 61 successful samples with a 242.74 ms mean and a 990 ms p99, illustrating how a single cold connection can dominate tail latency in small samples. The discussion advises selecting workload models based on real traffic behavior, monitoring arrival shortfalls and generator capacity, sizing distributed thread counts as totals rather than per-server values, splitting test data across generators, and interpreting high percentiles cautiously in short tests.

No tracked trend matches for this post yet.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.