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In queueing theory, a discipline within the mathematical theory of probability, mean value analysis (MVA) is a recursive technique for computing expected queue lengths, waiting time at queueing nodes and throughput in equilibrium for a closed separable system of queues. The first approximate techniques were published independently by Schweitzer〔 and Bard, followed later by an exact version by Lavenberg and Reiser published in 1980. It is based on the arrival theorem, which states that when one customer in an ''M''-customer closed system arrives at a service facility he/she observes the rest of the system to be in the equilibrium state for a system with ''M'' − 1 customers. ==Problem setup== Consider a closed queueing network of ''K'' M/M/1 queues, with ''M'' customers circulating in the system. To compute the mean queue length and waiting time at each of the nodes and throughput of the system we use an iterative algorithm starting with a network with 0 customers. Write ''μ''''i'' for the service rate at node ''i'' and ''P'' for the customer routing matrix where element ''p''''ij'' denotes the probability that a customer finishing service at node ''i'' moves to node ''j'' for service. To use the algorithm we first compute the visit ratio row vector v, a vector such that v = v P. Now write ''L''''i''(''n'') for the mean number of customer at queue ''i'' when there are a total of ''n'' customers in the system (this includes the job currently being served at queue ''i'') and ''W''''j''(''n'') for the mean time spent by a customer in queue ''i'' when there are a total of ''n'' customers in the system. Denote the throughput of a system with ''m'' customers by ''λ''''m''. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Mean value analysis」の詳細全文を読む スポンサード リンク
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