Defining information system characteristics with account of different applications service types

• Published in: 2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) pp. 1 - 5
• Main Authors: Zamoryonov, M. V., Kopp, V. Ya, Zamoryonova, D. V.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2017
• Subjects: different types of requirements; Distribution functions; downtime; Industrial engineering; Information systems; Maintenance engineering; Manufacturing; Optimization; Random variables; semi-Markov system; the algorithm phase of consolidation
• DOI: 10.1109/ICIEAM.2017.8076397

The article considers a single-channel queuing system with losses and one serving device. To describe the functioning of the system, we use the apparatus of semi-Markov processes with a common phase space of states which allows us to remove the assumption on the exponential nature of the random event streams commonly used in the literature. In the simulation, the phase-locking algorithm is used for transition from a system with a continuous phase space of states to a system with discrete states. In contrast to the well-known publications on this topic, where only the expectation was determined, this article determines the function of distributing the idle time of the service device when accounting for the receipt of various types of claims. This distribution function is defined for the general case assuming an arbitrary number of requirement types. Comparison is made between the mathematical expectation obtained in the work of the expectation function for the distribution of the idle time of the servicing device and the mathematical expectation of the same value obtained by the formula known from the literature if there are two different types of requirements in the system. Comparing the simulation results, it was assumed that the random variables characterizing the given system are distributed according to the generalized Erlang law of the second order and have continuous functions and distribution densities.