l'arcrllel extcufzon can enhance the performance of dzsfrzhufed claen,f-server systems, but the enhancement ma,y he less than expected.Evaluatzons of such deszgns must rn,clude the complex eflects of overheads, heterogeneous parallel branch,es, contention by the parallel parts for servers in lower levels, and simultaneous resource possession effects. A "compensated complelnentary delay" approximation is described which explozis layered queuean,g approEzmations for layered re-.sources tuhach occur in client-server architectures, based on synchronazation dela,y estimates and adjusted levels of conieniion.The new approximataon uses the overlap of parallel branches and a new fast calculation of join drlalys. II gives acceptable errors (averaging about two percent), and has an enormously lower computational cost compared to the competang approach based on decompositzon.The new approximation may moderate a concluszon made by Heidelberger and Trivedi, that decomposition gives greatly superior accuracy to the much cheaper complementary delay approach; these delay approximations are only a little less accurate.
Large distributed client-server systems often contain subsystems which are either identical to each other, or very nearly so, and this simplifies the system description for planning purposes. These replicated components and subsystems all have the same workload and performance parameters. It is known how to exploit this symmetry to simplify the solution of some kinds of performance models, using state aggregation in Markov Chains. This work considers the same problem for layered queueing models, using mean value analysis. The mean values are found for each group of replicas just once, and then are inserted appropriately into the solution of the system as a whole. An algorithm has been implemented in the Layered Queueing Network Solver (LQNS), including approximations to deal with interactions among the replicas, and is evaluated for accuracy and for efficiency. The resulting solver is insensitive (in time of solution) to the number of replicas in a group, and can efficiently calculate waiting times and throughputs for systems with tens of thousands of nodes and processes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.