workloaddependence

Workload dependence is the extent to which the behavior, performance, or reliability of a system changes in response to the characteristics of the workload it processes. It describes how metrics such as latency, throughput, power consumption, or failure rates vary with different input mixes, request patterns, or timing profiles.

Contexts include computing systems (processors, memory hierarchies, storage subsystems), databases and data services, networks and distributed systems, and software applications undergoing performance testing. In each domain, a given system may exhibit strong or weak dependence on workload features such as concurrency level, request mix, locality, access patterns, and burstiness. Recognizing workload dependence helps distinguish performance that is intrinsic to a design from performance that depends on external usage.

Characterization approaches include workload profiling, benchmark suites, trace-driven simulations, and sensitivity analysis. Common metrics are average and tail latency, throughput, resource utilization, and error rates. Quantifying dependence may use correlation between workload features and outcomes, regression models, or formal performance models that incorporate workload parameters.

Implications of workload dependence include the need for workload-aware performance guarantees, more robust capacity planning, and designs that minimize contention or exploit workload regularities. It also affects testing strategies, as realistic validation requires representative workload mixes rather than single, synthetic benchmarks.

Examples illustrate the concept: a web server may show high throughput under steady load but poor tail latency under bursty traffic; a database’s cache hit rate may depend strongly on query locality; a GPU kernel may saturate memory bandwidth only for specific data layouts or access patterns. See also workload characterization, performance modeling, stress testing.