Resumen
Within a supply chain organisation, where millions of messages are processed, reliability and performance of message throughput are important. Problems can occur with the ingestion of messages; if they arrive more quickly than they can be processed, they can cause queue congestion. This paper models data interchange (EDI) messages. We sought to understand how best DevOps should model these messages for performance testing and how best to apply smart EDI content awareness that enhance the realms of Ambient Intelligence (Aml) with a Business-to business (B2B) supply chain organisation. We considered key performance indicators (KPI) for over- or under-utilisation of these queueing systems. We modelled message service and inter-arrival times, partitioned data along various axes to facilitate statistical modelling and used continuous parametric and non-parametric techniques. Our results include the best fit for parametric and non-parametric techniques. We noted that a one-size-fits-all model is inappropriate for this heavy-tailed enterprise dataset. Our results showed that parametric distribution models were suitable for modelling the distribution?s tail, whilst non-parametric kernel density estimation models were better suited for modelling the head of a distribution. Depending on how we partitioned our data along the axes, our data suffer from quantisation noise.