In design and optimization of intensive care unit (ICU) networks, one common practice is to prioritize the treatment for patients of higher emergency levels, while ensuring fairness to other patients by guaranteeing a certain Quality of Service (QoS) level. One common approach to realize such priority arrangement is bed reservation policy, which designates a certain number of last occupied beds in each hospital to be exclusively used by certain patient classes. In this paper, we propose an approach that can significantly improve the computational efficiency in obtaining the optimal reservation thresholds for each patient class, given their respective requirements, in a non-hierarchical ICU model (where the external emergency patients can possibly be allocated to any ICU hospital), which has been shown to be computationally challenging in performance evaluation and optimization. Specifically, we apply the Information Exchange Surrogate Approximation (IESA) to analytically approximate the key QoS metrics under given reservation thresholds, and the integer Particle Swarm Optimization (PSO) algorithm to search for the optimal threshold based on the approximation results by IESA. We demonstrate numerically, with real data from ICUs in Hong Kong, that IESA can obtain reasonably accurate results for QoS metrics, and thus lead to accurate optimal reservation thresholds. In addition, our proposed approach combining IESA and PSO can significantly reduce the computation time by more than four orders of magnitude compared to the state-of-the-art evaluation and optimization methods in existing research for similar problems, especially for ICU networks with practical sizes.

We propose a versatile cross-layer framework to analyze performance metrics for mobile traffic in fifth-generation (5G) millimeter wave (mmWave) networks. Our proposed framework is based on stochastic geometry, teletraffic models, and the classical Erlang Fixed Point Approximation method, with the objective of evaluating blocking probability, mean service time of user requests, and utilization rate of base stations by taking into account practical concerns in mmWave networks including blockages encountered by mmWaves in the physical layer, capacity constraints in the network layer, and the stochastic nature of mobile traffic. We demonstrate by numerical results that our analytical method is accurate and computationally-efficient.

Non-hierarchical overflow loss systems (NH-OLSs) with mutual overflow have applications in many telecommunications and service systems, e.g., cellular networks, video-on-demand, emergency healthcare, or cloud services. In this paper, we make fundamental contributions in teletraffic modeling for blocking probability evaluation in NH-OLSs. These are based on the development of the well-known decomposition methodology, which decomposes an NH-OLS into independent subsystems and applies a suitable node model to each subsystem. In particular, we provide new both theoretical and numerical results involving both new and existing approximation methods. We show that these results cover a broad range of NH-OLSs, including those with heterogeneous arrival processes, server group size, and/or routing, with both Poisson and non-Poisson arrivals of fresh requests. Our theoretical results include the first scalable asymptotic exactness results for NH-OLSs. Our new approximations include the first computationally efficient and fairly accurate approximation for NH-OLSs with both mutual overflow and non-Poisson input with asymptotic exactness properties.

Energy efficiency and Quality of Service (QoS) are two important considerations for the design and planning of cellular networks. One effective approach to handle the tradeoff between power consumption and QoS is to switch some of the Base Stations (BSs) to sleep mode when traffic load is low. In this paper, we model each BS as a processor-sharing queue with vacations, and investigate the performance of three BS sleeping schemes, namely the isolated scheme in which each BS switches mode based on its own load, the cooperative scheme in which traffic is allowed to overflow from sleeping BSs to neighboring active BSs, and a combination of both schemes. We propose a robust, scalable and computationally efficient analytical method to evaluate QoS metrics (including mean delay and blocking probability) and power consumption for each scheme and validate their accuracy by simulations. We also demonstrate the power consumption and QoS trade-off by extensive and statistically reliable experiments, and compare the performance of the three schemes under different network conditions.

Overflow loss systems have wide applications in telecommunications and multimedia systems. In this paper, we consider an overflow loss system consisting of a set of finite-buffer processor-sharing (PS) queues, and develop effective methods for evaluation of its blocking probability. For such a problem, an existing approximation of the blocking probability is based on decomposition of the system into independent PS queues. We provide a new approximation which instead performs decomposition on a surrogate model of the original system, and demonstrate via extensive numerical results that our new approximation is more accurate and robust than the existing approach. We also examine the sensitivity of the blocking probability to the service time distribution, and demonstrate that an exponential distribution is a good approximation for a wide range of service time distributions.