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Layered queues are a canonical form of extended queueing network for systems with nested multiple resource possession, in which successive depths of nesting define the layers. The model has been applied to most modern distributed systems, which use different kinds of client-server and master-slave relationships, and scales up well. The layered queueing network (LQN) model is described here in a unified fashion, including its many more extensions to match the semantics of sophisticated practical distributed and parallel systems. These include efficient representation of replicated services, parallel and quorum execution, and dependability analysis under failure and reconfiguration. The full LQN model is defined here and its solver is described. A substantial case study to an air traffic control system shows errors (compared to simulation) of a few percent. The LQN model is compared to other models and solutions, and is shown to cover all their features.

We consider the classical power management problem: There is a system or “device” which has two states—ON and OFF—and one has to develop a control algorithm for changing between these states as to minimize cost (energy or some other hybrid cost) when given a sequence of service requests. We analyze this problem in terms of online competitive analysis to obtain worst-case guarantees. Although an optimal 2-competitive algorithm exists, that algorithm does not result in good performance in many practical situations, especially in case the device is not used frequently. To take the frequency of device usage into account, we construct an algorithm based on the concept of “slackness degree”. Then by relaxing the worst-case competitive ratio of our online algorithm to 2+ε, where ε is an arbitrary small constant, we make the algorithm flexible to slackness. The algorithm thus automatically tunes itself to slackness degree and gives better performance than the optimal 2-competitive algorithm for real world inputs. In addition to worst-case competitive ratio analysis, a queueing model analysis is given and computer simulations are reported, confirming that the performance of the algorithm is high. We show how the approach can be generalized to a situation where the system has a number of intermediate states. Our model can be used to facilitate renewable energy integration into the electrical grid and we highlight that an online competitive approach can yield techniques for grid resiliency.

This paper presents a queuing system model that incorporates multiple priorities, multiple abandonments, and heterogeneous servers. Waiting for service easily leads to impatient behaviors. The impact of two kinds of impatient behaviors, balking and reneging, on queueing system performance is examined. The problem is formulated as continuous-time Markov chains. It also introduces a special state called the non-sojourn state to record the number of customers who abandon the system. The state transition rate matrix is transformed into a block tridiagonal matrix by appropriately setting the state numbers. A novel indicator called interstate transition frequency is proposed, which aids in distinguishing state transitions during the system evaluation process. Based on the interstate transition frequency, a set of indicators is derived to offer additional analytical perspectives for the queuing system. Finally, the proposed model is applied to an automobile repair shop to validate its effectiveness in practical scenarios.

This paper presents the design and implementation of a smart and safe minimarket prototype for deployment in busy smart cities to mitigate the overhead of shopping experience. The prototype allows customers to remotely access and browse the available products at the minimarket using a special smart-phone application. The system can intelligently detect the nearby location of customers and subsequently provide location-dependent services such as allowing orders to be placed using the application, predicting weekly customer expenditures based on artificial-neural-network machine-learning approach, and automatically delivering purchased products using a robotic shopping cart. This proposal is believed to support safe shopping which became a critical issue after COVID-19 pandemic. From a service provider view point, the application allows the provider to remotely manage the minimarket by adding/removing product items, keeping track of shortage in products, and getting revenue information. Empirical results show that the average service time of the minimarket is ≈60 seconds per customer. However, an analytical model based on queueing theory was used to analyze the performance of the system when customers arrive according to a Poisson random process and get served according to a general-service-time distribution (M/G/1). The case of batch customer arrivals (M[H]/G/1) was also analyzed, where batch size is also assumed to be random. Various traffic intensities and the effect of variable service times were studied and cross-validated with simulation results. Worst-case scenario shows that under heavy load of 95%, when customers arrive at the minimarket every 63 seconds on average, the average response time for each customer is ≈10 minutes.

In cellular networks, cellular user (CU) packets often fail to transmit due to the failure to meet the minimum data transmission rate of cellular links, resulting in low network throughput. Based on the introduction of Device-to-Device (D2D) assisted communication in cellular networks, this paper proposes a probabilistic relay forwarding mechanism, which uses D2D communication devices as relay nodes to assist CU packets in completing transmission from cellular devices to base stations (BSs). By studying the behaviors of two types of users in the network, a queueing model is constructed, and the key performance indexes of CUs and D2D users are deduced by building and analyzing a 3-dimensional Markov chain (3DMC). Numerical experiments are performed to analyze the changing trends of these performance indexes, and the rationality of these changing trends are verified by simulation experiments. The results of comparative experiments show that the proposed probabilistic relay forwarding mechanism can efficiently improve the throughput and decrease the blocking rate of the network.

In the context of the Covid-19 pandemic the pressure that is put on the medical systems is increasing exponentially. Healthcare systems resources are in general scarce, and hence they require policies that ensure the optimal usage of beds and utilization costs. The aim of this study is to explore how artificial immune system approaches for a multiqueuing model may aid the hospital managers improve their resources. The proposed system outlines the route of Covid-19 patients in the intensive care unit (ICU), the compartmental model proposes a reasonable composition of the ICU, considering the queuing parameters, while the artificial immune system optimizes the needed resources (beds plus associated costs). The methodology was demonstrated through a simulation based on real data collected from official sources.

The emerging 5G mobile networks are essential enablers for mobile virtual reality (VR) video streaming applications assuring high quality of experience (QoE) at the end-user. In addition, mobile edge computing brings computational resources closer to the user equipment (UE), which allows offloading computationally intensive processing. In this paper, we consider a network architecture for mobile VR video streaming applications consisting of a server that holds the VR video content, a mobile edge virtualization with prefetching (MVP) unit that handles the VR video packets, and a head-mounted display along with a buffer, which together serve as the UE. Several modulation and coding schemes with different rates are provided by the MVP unit to adaptively cope with the varying wireless link conditions to the UE and the state of the UE buffer. The UE buffer caches VR video packets as needed to compensate for the adaptive rates. A performance analysis is conducted in terms of blocking probability, throughput, queueing delay, and average packet error rate. To capture the effect of fading severity, the analytical expressions for these performance metrics are derived for Nakagami-m fading on the wireless link from the MVP unit to the UE. Numerical results show that the proposed system meets the network requirements needed to assure the QoE levels of different mobile VR video streaming applications.

Due to the deficiency of radio spectrum resources caused by the progress in technology, cognitive radio networks (CRNs) have made significant progress. CRNs have two types of users, namely, primary users (PUs) and secondary users (SUs). Considering that PUs have a higher priority and diversified data transmission requirements, this study divides PUs into two levels, namely, PU1s with a higher priority and PU2s with a lower priority. On the other hand, the occurrence of failures is inevitable in CRNs, which affects the data transmission of users. In this paper, combined with an adjustable PU packets transmission rate mechanism, a communication failure and repair mechanism with classified PUs based on the single-channel CRNs is proposed, and different preemption principles are set according to different system states. A queueing model is established and analyzed with a Markov chain, the performance index expressions that need targeted research are listed, numerical experiments are conducted, and the system performance change trends are obtained. The comparison experiment shows that the proposed communication failure and repair mechanism with classified PUs can improve the throughput of PU1 packets and reduce the blocking rate of PU1 packets compared with the conventional communication failure and repair mechanisms with unclassified PUs.

Multimedia content streaming from Internet-based sources emerges as one of the most demanded services by wireless users. In order to alleviate excessive traffic due to multimedia content transmission, many architectures (e.g., small cells, femtocells, etc.) have been proposed to offload such traffic to the nearest (or strongest) access point also called “helper”. However, the deployment of more helpers is not necessarily beneficial due to their potential of increasing interference. In this work, we evaluate a wireless system which can serve both cacheable and non-cacheable traffic. More specifically, we consider a general system in which a wireless user with limited cache storage requests cacheable content from a data center that can be directly accessed through a base station. The user can be assisted by a pair of wireless helpers that exchange non-cacheable content as well. Files not available from the helpers are transmitted by the base station. We analyze the system throughput and the delay experienced by the cached user and show how these performance metrics are affected by the packet arrival rate at the source helper, the availability of caching helpers, the caches’ parameters, and the user’s request rate by means of numerical results.