Data assessments run regular path diagnostics on multiple paths at once, with the goal of predicting your network’s ability to deliver data-intensive applications. Data assessments return familiar metrics (loss, latency, jitter, etc.), but also a ‘readiness’ value. Readiness is a succinct representation of how well a network path is expected to handle data-intensive applications and is based on a predictive model developed by AppNeta. Data assessments are best used to evaluate your networks ability to handle activities like ftp transfers, backups, and recovery. These types of activities primarily demand bandwidth for long, steady TCP transfers under relatively constant network conditions, i.e., packet loss, routes, and bandwidth are not varying appreciably. Data assessments won’t be as relevant to voice, video, best-effort, or transactional data.

Readiness

Conceptually readiness is like MOS for data, but with key differences: MOS is based on current performance and is typically related to the symptomatic evaluation of the network path—that is, what the loss, latency and jitter impose upon the quality of the service. Readiness on the other hand leverages the diagnostic capability of APM to discover the origins of loss, latency, and jitter, and then characterize the range of performance possible in the presence of those causes.

The basis for readiness is throughput, which is the critical measure of how well a data-intensive application is performing. Modeling is used to estimate throughput at layer 4, based on layer 3 behaviors and set of parameters that represent a typical TCP configuration. This estimate considers total capacity and utilization, and the effect of loss, latency, and jitter on TCP congestion control. Assuming no performance bottlenecks at higher layers, it also reflects the throughput that is available to the application at layer 7.

If you want to influence your readiness score increase throughput. Keep in mind however that readiness neither throughput exactly nor is it a linear function of throughput.