English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Performance analysis based on modelling consists of two major steps: modelconstruction and model analysis. Formal modelling techniques significantly aidmodel construction but can exacerbate model analysis. In particular, here weconsider the analysis of large-scale systems which consist of one or moreentities replicated many times to form large populations. The replication ofentities in such models can cause their state spaces to grow exponentially tothe extent that their exact stochastic analysis becomes computationallyexpensive or even infeasible.  In this paper, we propose a new approximate aggregation algorithm for a classof large-scale PEPA models. For a given model, the method quickly checks if itsatisfies a syntactic condition, indicating that the model may be solvedapproximately with high accuracy. If so, an aggregated CTMC is generateddirectly from the model description. This CTMC can be used for efficientderivation of an approximate marginal probability distribution over some of themodel's populations. In the context of a large-scale client-server system, wedemonstrate the usefulness of our method.

An Aggregation Technique For Large-Scale PEPA Models With Non-Uniform Populations