We represent the classical Engset-loss model by the stochastic process counting the number of customers in the system. A fluid limit for this process is established for all the possible values of the various parameters of the system, as the number of servers tends to infinity along with the number of sources. Our results are derived through a semi-martingale decomposition method. A numerical application is provided to illustrate these results. Then, we represent a finite-source retrial queue considering in addition the number of sources in orbit. Finally, we extend the fluid limit results to a retrial queueing system, discussing different cases.

In this paper a new traffic control method called advanced round robin (ARR) is presented. It is shown that ARR can provide both worst case and statistical quality of service guarantees without coupling cell loss and delay requirements. A related connection admission control algorithm with performance study is also presented. A comparative performance analysis of ARR using simulation was performed in different real scenarios. Results are compared to a reference system and to the simple round robin method.

An asymptotic queuing theoretic approach is proposed to analyze the performance of an FCFS (first-come, first-served) heterogeneous multiprocessor computer system with a single bus operating in a randomly changing environment. All stochastic times in the system are considered to be exponentially distributed and independent of the random environment, while the access and service rates of the processors are subject to random fluctuations. It is shown under the assumption of 'fast' arrivals that the busy period length of the bus converges weakly, under appropriate normalization, to an exponentially distributed random variable. As a consequence, main steady-state performance measures such as system throughput, mean delay time, expected waiting time, and mean number of active processors can be approximately determined. The reliability of the proposed method is validated by comparing the new approximations with known exact results.>