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Background Hospital administrators are often challenged with overcrowding at hospitals. The study hospital receives referred patients; however, they have to wait in long queues even for getting registered. This was a cause of concern for hospital administrators. The study was undertaken to find an amicable solution to the queues at registration using Queuing Theory. Method This observational and interventional study was carried out in a tertiary care ophthalmic hospital. In the first phase, data of service time and arrival rate was collected. The queuing model was built using the coefficient of variation (CoV) of the observed times. Server utilization for new patient registration was found to be 1.21 and was 0.63 for revisit patients. Scenario-based simulation carried out using free software for optimal utilization of both types of servers. Recommendations made to combine the registration process and to increase one server were implemented. In the second phase, after one year, patient registration data were collected and compared for the number of patients registered using SPSS 17. Results Number of patients registered within the registration timings increased whereas the number of patients registered after the registration timings decreased significantly at 95% CI with a p-value of less than 0.001. Queues finished early and more number of patients were registered in the same time. Conclusion Using queuing theory, the bottleneck of the systems can be identified. Scenario and software-based simulations provide solutions to the problem of queues. The study is an application of Queuing Theory with a focus on efficient resource utilization. It can be replicated in an organization with limited resources facing the challenge of queues.

Switches in cloud data centers support multiple service queues per port to provide differentiated network performance among different traffic classes. To isolate service queues, recent solutions leverage the power of Explicit Congestion Notification (ECN). However, this causes a fundamental dependency on ECN-based transport protocols, making it hard to use generic transport protocols. To this end, we design DynaQ, a protocol-independent multi-queue management solution that enables service queue isolation with generic transport protocols. The key idea of DynaQ is to adjust the packet dropping threshold of service queues dynamically. Specifically, DynaQ allows a service queue to occupy free buffer space but prevents the queue from hurting other active queues. Our solution requires only a few additional clock cycles to implement on hardware. To evaluate DynaQ comprehensively, we conduct a series of testbed experiments and large-scale simulations. Our evaluation results show that, compared to alternative schemes, DynaQ is the only solution that achieves work-conserving weighted fair sharing and low latency without protocol dependency.

In the current era of the Internet of Things (IoT), various devices can provide more services by connecting to the Internet. However, the explosive growth of connected devices will cause the cloud core overload and significant network delays. To overcome these problems, the Mobile Edge Computing (MEC) network is proposed to provide most of the computing and storage near the radio access network to reduce the traffic of the core cloud network and provide lower latency for the terminal.Mobile edge computing can work with third parties to develop multiple services, such as mobile big data analysis and context-aware services. However, when there is a large amount of popular data accessed in a short period, the system must generate many replicas, which will not only reduce access efficiency but also cause additional traffic overhead. To improve the above problems, an Adaptive Replica Configuration Mechanism (ARCM) is proposed in this paper to predict the popularity of the file and make a replica to the low-blocking node. This method spreads the subsequent access workload by copying the popular file in advance to improve the overall performance of the system.

Mobile Cloud Computing enables the migration of services to the edge of the Internet. Therefore, high-performance computing clusters are widely deployed to improve computational capabilities of such environments. However, they are prone to failures and need analytical models to predict their behaviour in order to deliver desired quality-of-service and quality-of-experience to mobile users. This paper proposes a 3D analytical model and a problem-solving approach for sustainability evaluation of high-performance computing clusters. The proposed solution uses an iterative approach to obtain performance measurements to overcome the state space explosion problem. The availability modelling and evaluation of master and computing nodes are performed using a multi-repairman approach. The optimum number of repairmen is also obtained to get realistic results and reduce the overall cost. The proposed model is validated using discrete event simulation. The analytical approach is much faster and in good agreement with the simulations. The analysis focuses on mean queue length, throughput, and mean response time outputs. The maximum differences between analytical and simulation results in the considered scenarios of up to a billion states are less than1.149%,3.82%, and3.76%respectively. These differences are well within the5%of confidence interval of the simulation and the proposed model.

Although considerable methods have been developed for the performance evaluation of signalized intersections using sampled vehicle trajectories, most of them aim at estimating a single parameter and cannot describe the entire arrival-departure process of the traffic flow. This significantly constrains the application of these methods for a comprehensive evaluation and efficient optimization of signalized intersections. In this paper, we propose a cumulative flow diagram (CFD) estimation and prediction method using sampled vehicle trajectories. It can be used to calculate multiple performance measures--traffic volume, queue length, average delay, and total delay--based on the estimated CFD for the current signal timing plan. Concurrently, it can be further employed for signal control optimization based on the predicted CFDs for candidate signal timing plans. The core idea of the proposed method is to generate the cumulative arrival curve based on the arrival characteristics of the sampled vehicles, and then fit the queue leaving points to obtain the cumulative departure curve. Thereby, given the current or any candidate signal timing plan, we can estimate or predict the CFDs by updating the sampled vehicle arrivals. The proposed method is evaluated using both simulation and empirical data. The simulation results yield that the average estimation error of the four performance measures is 10.3% under a real-world level penetration rate of 10%. Meanwhile, similar accuracies are achieved for the CFD prediction. The empirical results show that under a penetration rate of 8.6%, the estimation errors of the traffic volume and queue length are 2.7% and 3.3%, respectively.

Short-term traffic prediction is of great importance to the management of traffic congestion, a pervasive and difficult-to-solve problem in many metropolises all over the world. However, existing studies on traffic prediction contain rough traffic information at the carriageway level that ignore the distinction between different turns in one intersection. With the aim of predicting traffic at road intersections from big trace data on a finer scale, this study proposes a novel method, the fine-grained traffic prediction method (FTPG) with a graph attention network (GAT), which predicts traffic information, including traffic flow speeds, traffic states, and average queue lengths, at the turn level. In the FTPG, a method for estimation of the queue starting point is proposed to improve the accuracy of traffic information detection. Furthermore, the topology is constructed under turn-level conditions, and a GAT-based method, the spatio-temporal residual graph attention network (ST-RGAN), is proposed to improve the prediction accuracy. Experiments are performed using taxi GPS trace data collected in the city of Wuhan and show that the proposed FTPG method can make predictions with fine-grained traffic information for road intersections accurately and robustly.

For wireless caching networks, the scheme design for content delivery is non-trivial in the face of the following tradeoff. On one hand, to optimize overall throughput, users can associate their nearby APs with great channel capacities; however, this may lead to unstable queue backlogs on APs and prolong request delays. On the other hand, to ensure queue stability, some users may have to associate APs with inferior channel states, which would incur throughput loss. Moreover, for such systems, how to conduct predictive scheduling to reduce delays and the fundamental limits of its benefits remain unexplored. In this paper, we formulate the problem of online user-AP association and resource allocation for content delivery with predictive scheduling under a fixed content placement as a stochastic network optimization problem. By exploiting its unique structure, we transform the problem into a series of modular maximization sub-problems with matroid constraints. Then we devise PUARA, a Predictive User-AP Association and Resource Allocation scheme which achieves a provably near-optimal throughput with queue stability. Our theoretical analysis and simulation results show that PUARA can not only perform a tunable control between throughput maximization and queue stability, but also incur a notable delay reduction with predicted information.

This paper considers a WP-MEC system consisting of multiple base stations (BSs) and mobile devices (MDs), where the MDs offload tasks to the BSs for computational resources and the BSs charge the MDs using wireless power transfer (WPT). In practice, each BS and MD are equipped with a task buffer with limited size and a battery with limited capacity. First, we develop a WP-MEC system with task and energy queuing dynamics to study long-term system performance under time-varying fading channels and stochastic task and energy arrivals. Second, we propose a dynamic throughput maximum (DTM) algorithm based on perturbed Lyapunov optimization to maximize the system throughput under task and energy queue stability constraints, by optimizing the allocation of communication, computation, and energy resources. For DTM, we characterize a throughput-backlog trade-off of [O}(1/V), O(V)] to indicate that the system throughput goes up as the queue backlog increases. However, as V goes large, the system throughput can be pushed arbitrarily close to the optimum at the cost of linearly increasing queue backlog. To reduce the cost, we further develop an improved dynamic throughput maximum (IDTM) algorithm, and verify that the IDTM algorithm can achieve a trade-off of ${[O(1/V), O((\log(V))^2)]}$ between the system throughput and the queue backlog.

This paper introduces two novel algorithms for thread migrations, named CIMAR (Core-aware Interchange and Migration Algorithm with performance Record –IMAR–) and NIMAR (Node-aware IMAR), and a new algorithm for the migration of memory pages, LMMA (Latency-based Memory pages Migration Algorithm), in the context of Non-Uniform Memory Access (NUMA) systems. This kind of system has complex memory hierarchies that present a challenging problem in extracting the best possible performance, where thread and memory mapping play a critical role. The presented algorithms gather and process the information provided by hardware counters to make decisions about the migrations to be performed, trying to find the optimal mapping. They have been implemented as a user space tool that looks for improving the system performance, particularly in, but not restricted to, scenarios where multiple programs with different characteristics are running. This approach has the advantage of not requiring any modification on the target programs or the Linux kernel while keeping a low overhead. Two different benchmark suites have been used to validate our algorithms: The NAS parallel benchmark, mainly devoted to computational routines, and the LevelDB database benchmark focused on read–write operations. These benchmarks allow us to illustrate the influence of our proposal in these two important types of codes. Note that those codes are state-of-the-art implementations of the routines, so few improvements could be initially expected. Experiments have been designed and conducted to emulate three different scenarios: a single program running in the system with full resources, an interactive server where multiple programs run concurrently varying the availability of resources, and a queue of tasks where granted resources are limited. The proposed algorithms have been able to produce significant benefits, especially in systems with higher latency penalties for remote accesses. When more than one benchmark is executed simultaneously, performance improvements have been obtained, reducing execution times up to 60%. In this kind of situation, the behaviour of the system is more critical, and the NUMA topology plays a more relevant role. Even in the worst case, when isolated benchmarks are executed using the whole system, that is, just one task at a time, the performance is not degraded This research work has received financial support from the Ministerio de Ciencia e Innovación, Spain within the project PID2019-104834GB-I00. It was also funded by the Consellería de Cultura, Educación e Ordenación Universitaria of Xunta de Galicia (accr. 2019–2022, ED431G 2019/04 and reference competitive group 2019–2021, ED431C 2018/19) SI

Optimal load distribution and speed scaling in a heterogeneous data center that takes account of the tradeoff between the maintenance and operation costs and system performance are crucial aspects of cloud computing. In this paper, we aim to provide a novel multi-server control model with dynamic feedback to acquire dynamic states of the cloud system, and with queue waiting cost-awareness to optimize the queue wait time and load distribution in task assignment and server configuration management. Using the technology of speed scaling, each server in a data center is configured as an M/M/1 queue system with variable service rate, and the service rate is a function of the length of the task queue. We formulate two optimization problems, the optimal load distribution problem and the optimal controlling service rate problem, and provide algorithms to solve these problems, facilitating load distributions and service rate adjustments. We also present numerical simulations to validate our model. The results show our model to be efficient in multi-server dynamic configurations and task assignments according to the feedback information for the tradeoff between the system cost and performance.

The increasing prevalence of electric vehicles (EVs) has intensified the coupling between power distribution networks (PDNs) and transportation networks (TNs) in both temporal and spatial dimensions. In order to accurately model the coupled network, this paper studies the dynamic network equilibrium to capture the temporally-dynamic interactions between PDNs and TNs. This equilibrium encapsulates the driver’s choices of route, departure time, and charging location, and the electricity price. In the TN, a dynamic traffic model with point queues is proposed to describe the spatial and temporal evolution of traffic flows that is congruent with established user equilibrium choices. In particular, the queues formed at charging stations are, for the first time, modeled by a point queue. In the PDN, the electricity prices are accurately determined from a second-order conic program with a guarantee of strong duality. After theoretically proving the existence of an equilibrium solution, an improved fixed-point algorithm based on extrapolation is also proposed to solve the equilibrium problem efficiently. Numerical results show that the dynamic network equilibrium can be efficiently solved to capture the temporally variant nature of traffic flows, queues, and electricity prices.

Data collection in big data is an effective way to aggregate information that the collector is interested in. However, there is no assurance for the data that the users provide. Since collector does not have the ability to check the authenticity of every piece of information, the trustworthiness of users participated in the collection become important. In this paper, we design an efficient approach to calculate users' trustworthiness in data collection for big data context. We divide the trustworthiness into familiarity trustworthiness and similarity trustworthiness, and study the influences of user actions on trustworthiness. To prevent malicious users from raising their trustworthiness and providing false information that may mislead final results, we also design a security queue to record users' historical trust information, so that we can detect malicious users with high accuracy. Simulation results show that our model can sensitively resist the malicious actions of users.

Coded caching is a technique that promises significant reductions in network traffic by exploiting the multicast opportunities among multiple cache-enabled users. Most works in this area investigate the fundamental performance limits of coded caching from an information-theoretic perspective. In this paper, from a practical perspective, we focus on the design and analysis of the coded caching scheme in large-scale stochastic networks with dynamic traffic. Aiming at the challenges of the large number of users, dynamic packet requests, and distance-dependent interference, the packet caching scheme and packet delivery scheme are jointly designed to ensure the availability of the coded caching gain. To characterize the coded caching performance, the queue dynamic of the packet requests and channel state are first analyzed. Then by combining the successive group decoding theory and stochastic geometry approach, the network interference is characterized and the successful transmission probability of the multicast coded signal is further derived. Moreover, the coded caching scheme is optimized to minimize the average delay of the packet request, which is proved to ensure service fairness and reduce the complexities of network performance analysis. The analytical and numerical results provide useful insights for the provisioning and planning of the coded caching network.

The Internet of Healthcare Things (IoHT) consists of a wide variety of resource-restricted, heterogeneous, IoT-enabled, wearable/non-wearable medical equipment (things) that connect over the internet to transform traditional healthcare into a smart, connected, proactive, patient-centric healthcare system. The pivotal functions of the 6LoWPAN protocol stack enable comprehensive integration of such networks from wearable wireless sensor networks (W-WSN) to IoHT, as TCP/IP does not suffice the requirements of IoHT networks. As a result, the congestion in the IoHT network increases with a growing number of devices, resulting in loss of critical medical information due to buffer loss and channel loss, which is unacceptable. In this paper, we explored different applications of patient-centric IoHT architectures to draw a realistic resource-limited topological layout of IoHT for congestion estimation. After critically reviewing existing congestion schemes for 6LoWPANs, we proposed an effective buffer-loss estimation model based on the Queuing Theory to determine the number of packets lost at the node’s buffer. The buffer is modeled as an M/M/1/K Markov Chain Queue. The M/M/1/K Queue equilibrium equation is used to establish a relationship between the probabilities of the buffer being empty or completely filled. We derived the expressions for total buffer-loss probability and expected mean packet delay for the resource-constraint IoHT network. Furthermore, to validate the buffer-loss estimation, an analytical model is used to compare buffer-loss probabilities, the number of packets dropped at leaf/intermediate nodes and the number of packets successfully received at the local sink node. The results show a close correlation between both the models on varying values of the number of leaf nodes, buffer size, offered packet load and available channel capacity. Thus, in resource-restrictive IoHT, the proposed model performs better than two related works.

Mobile Edge Computing (MEC) has proved its great potential for supporting the delay-stringent services in wireless networks. We notice that offloading performance of the MEC system is not only determined by the PHY-layer transmission rate, but also closely related to the performance of channel access and computation at the server. To this end, in this correspondence, we investigate a multiuser MEC system, where each user offloads packet to a single AP equipped with an MEC server by preempting the channel according to carrier sense multiple access with collision avoidance (CSMA/CA) protocol. We derive the effective offloading rate and delay by considering transmission outage caused by the poor channel condition, the MAC-layer channel competition, and the packet overflow due to the limited service capability of the server, where the service process is modelled as an M/M/1/B queue. The theoretical analysis is verified by extensive simulations, which has shown that the competition intensity indicated by the number of users and the packet length, as well as the processing capability of the MECserver including the buffer size and the computation speed, has profound impact on the offloading performance.

Many stochastic systems face a time-dependent demand. Especially in stochastic service systems, for example, in call centers, customers may leave the queue if their waiting time exceeds their personal patience. As discussed in the extant literature, it can be useful to use general distributions to model such customer patience. This paper analyzes the time-dependent performance of a multiserver queue with a nonhomogeneous Poisson arrival process with a time-dependent arrival rate, exponentially distributed processing times, and generally distributed time to abandon. Fast and accurate performance approximations are essential for decision support in such queueing systems, but the extant literature lacks appropriate methods for the setting we consider. To approximate time-dependent performance measures for small- and medium-sized systems, we develop a new stationary backlog-carryover (SBC) approach that allows for the analysis of underloaded and overloaded systems. Abandonments are considered in two steps of the algorithm: (i) in the approximation of the utilization as a reduced arrival stream and (ii) in the approximation of waiting-based performance measures with a stationary model for general abandonments. To improve the approximation quality, we discuss an adjustment to the interval lengths. We present a limit result that indicates convergence of the method for stationary parameters. The numerical study compares the approximation quality of different adjustments to the interval length. The new SBC approach is effective for instances with small numbers of time-dependent servers and gamma-distributed abandonment times with different coefficients of variation and for an empirical distribution of the abandonment times from real-world data obtained from a call center. A discrete-event simulation benchmark confirms that the SBC algorithm approximates the performance of the queueing system with abandonments very well for different parameter configurations. Summary of Contribution: The paper presents a fast and accurate numerical method to approximate the performance measures of a time‐dependent queueing system with generally distributed abandonments. The presented stationary backlog carryover approach with abandonment combines algorithmic ideas with stationary queueing models for generally distributed abandonment times. The reliability of the method is analyzed for transient systems and numerically studied with real‐world data.

IEEE transactions on mobile computing : TMC 21(1), 291-305 (2022). doi:10.1109/TMC.2020.3005737, Published by IEEE, New York, NY

In emergency medical services (EMS), response time is a key factor that shows the performance of the system. This factor is composed of two parts including activation time and travel time. The activation time of an emergency call is the time a call center spends on taking an incoming call for EMS and decides whether an ambulance should be dispatched or the emergency can be resolved by giving some medical advices. Having a good number of call center operators including call takers and dispatchers can help to reduce this time and thus the response time. However, this alone may not be enough to have a reasonable cycle time. In fact, having idle ambulances located in right places when emergency calls occur is an influential factor to reduce the travel time and thus have an optimal response time and save people's lives. Although many researchers have studied improving either the activation time or the travel time, to the best of our knowledge, there exists no study focusing on both. Motivated by this gap, in this study, a framework is proposed which uses queuing theory and location analysis to address both activation and travel times. In this regard, first, queuing theory is applied to evaluate the performance of an EMS call center and reduce the average activation time. Then, three mathematical models including mobile stations, MEXCLP and MECRP are applied to locate ambulances such that the average travel time is minimized. To shed light on the merits of using queuing theory and location models, computational results based on the real-world data from Tehran EMS Center are provided. The proposed methodology can reduce the average response time by approximately four minutes and increase the average coverage by 35%. This work is an example of the tremendous positive impact that the application of queuing theory and mathematical modeling can have. The proposed framework can significantly decrease the cycle time and increase the ambulance coverage level. The findings of this study help the policy makers of EMS centers with evaluating the performance of their systems and determining the optimal number of operators and locations of ambulances in a way that enhances the quality of emergency services.

This paper studies virtual machine (VM) scheduling in a queueing cloud computing system with stochastical arrivals of heterogeneous jobs by considering jobs' delay requirements. The delay-optimal VM scheduling in such a cloud computing system is formulated as a multi-resource multi-class problem minimize the average job completion time, which is often NP-hard. To solve such a problem, we first propose a queueing model that buffers the same type of VM jobs in one virtual queue. The queueing model then divides the VM scheduling into two parallel low-complexity algorithms, i.e., intra-queue buffering and inter-queue scheduling. A min-min best fit (MM-BF) policy is used to schedule the jobs in different queues to minimize the remaining system resources, while a shortest-job-first (SJF) policy is used to buffer the job requests in each queue based on their job lengths in an ascending order. To avoid job starvation for the long-duration jobs in SJF-MMBF, we further propose a queue-length-based MaxWeight (QMW) policy based on Lyapunov drift to minimize the queue lengths of VM jobs, which is called SJF-QMW. Simulation results show that, SJF-MMBF and SJF-QMW achieve low delay performance in terms of average job completion time and high throughput performance in terms of job hosting ratio.

The concept of cloud computing was created to better preserve user privacy and data storage security. However, the resources allocated for processing this data must be optimally allocated. The problem of optimal resource management in the loud computing environment is described in many scientific publications. To solve the problems of optimality of the distribution of resources of systems, you can use the construction and analysis of QS. We conduct an analysis of two-buffer queuing system with cross-type service and additional penalties, based on the literature reviewed in the article. This allows us to assess how suitable the model presented in the article is for application to cloud computing. For a given system different options for selecting applications from queues are possible, queue numbers, therefore, the intensities of transitions between the states of the system will change. For this, the system has a choice policy that allows the system to decide how to behave depending on its state. There are four components of such selection management models, which is a stationary policy for selecting a queue number to service a ticket on a vacated virtual machine each time immediately before service ends. A simulation model was built for numerical analysis. The results obtained indicate that requests are practically not delayed in the queue of the presented QS, and therefore the policy for a given model can be considered optimal. Although Poisson flow is the simplest for simulation, it is quite acceptable for performance evaluation. In the future, it is planned to conduct several more experiments for different values of the intensity of requests and various types of incoming flows.

Traffic accident management as an approach to improve public security and reduce economic losses has received public attention for a long time, among which traffic accidents post-impact prediction (TAPIP) is one of the most important procedures. However, existing systems and methodologies for TAPIP are insufficient for addressing the problem. The drawbacks include ignoring the recovery process after clearance and failing to make comprehensive prediction in both time and space domain. To this end, we build a 3-stage TAPIP model on highways, using the technology of spiking neural networks (SNNs) and convolutional neural networks (CNNs). By dividing the accident lifetime into two phases, i.e., clean-up phase and recovery phase, the model extracts characteristics in each phase and achieves prediction of spatial-temporal post-impact variables (e.g., clean-up time, recovery time, and accumulative queue length). The framework takes advantage of SNNs to efficiently capture accident spatial-temporal features and CNNs to precisely represent the traffic environment. Integrated with an adaptation and updating mechanism, the whole system works autonomously in an online manner that continues to self-improve during usage. By testing with a new dataset CASTA pertaining to California statewide traffic accidents on highways collected in four years, we prove that the proposed model achieves higher prediction accuracy than other methods (e.g., KNN, shockwave theory, and ANNs). This work is the introduction of SNNs in the traffic accident prediction domain and also a complete description of post-impact in the whole accident lifetime.

This paper aims to understand the impact of stochastic arrivals on the peak age of information (PAoI) of non-orthogonal multiple access (NOMA) based Internet of Things (IoT) networks. Particularly, the concurrent transmissions of devices under NOMA provoke interactions of queues, which renders an entangled stochastic process to the analysis of PAoI. To this end, we first present an algorithmic solution to calculate the queue service rate, and then we derive analytical expression for the distribution of PAoI, which can be utilized to evaluate the statistics and the violation probability of PAoI. Based on the analysis, which is validated by simulations, we show that in terms of network average PAoI, NOMA considerably outperforms orthogonal multiple access (OMA) in the regime of more frequent update arrivals. We also find that the discrepancy on PAoI violation probability among different NOMA devices heavily hinges on the update arrival rate, demonstrating the critical role of the update dynamic in PAoI performance. The derived results allows us to extract valuable insights from the problem.

Cryptocurrency networks such as Bitcoin have emerged as a distributed alternative to traditional centralized financial transaction networks. However, there are major challenges in scaling up the throughput of such networks. Lightning network and Spider network are alternates that build bidirectional payment channels on top of cryptocurrency networks using smart contracts, to enable fast transactions that bypass the Blockchain. In this paper, we study the problem of routing transactions in such a payment processing network. We first propose a Stochastic model to study such a system, as opposed to a fluid model that is studied in the literature. Each link in such a model is a two-sided queue, and unlike classical queues, such queues are not stable unless there is an external control. We propose a notion of stability for the payment processing network consisting of such two-sided queues using the notion of on-chain rebalancing. We then characterize the capacity region and propose a throughput optimal algorithm that stabilizes the system under any load within the capacity region. The stochastic model enables us to study closed loop policies, which typically have better queuing/delay pe

Nowadays, after the technological development in societies, cloud computing has become one of the most important technologies. It provides users with software, hardware, and platform as remote services over the Internet. The increasing number of cloud users has caused a critical problem in how the clients receive cloud services when the cloud is in a state of instability, as it cannot provide required services and, thus, a delay occurs. Therefore, an algorithm was proposed to provide high efficiency and stability to work, because all existing tasks must operate without delay. The proposed system is an enhancement shortest job first algorithm (ESJF) using a time slice, which works by taking a task in the shortest time first and then the longest first from the queue. Through the experimental results in decreasing the waiting and completion time of the task, as well as taking into account reducing tasks starvation, the result of the proposed ESJF algorithm was compared with the traditional shortest job first (SJF) algorithm. These algorithms were applied when all tasks arrived at the same time, and it proved that the ESJF algorithm works more efficiently compared to SJF.

Cloud computing provides computing resources on demand of users without their direct management. In this cloud paradigm, scheduling the tasks and allocating the resources become major aspect for cloud infrastructure as a service (IaaS). There are more existing algorithms and techniques suggested for task allocation problem. Still there is challenging research on efficient scheduling. To address this issue, many researches are in progress and all of them having their own drawbacks. In this paper, we are proposing a queue-based scheduling strategy with load balancing called as IQSLB and an extended IQSLB also proposed for dealing with critical situations. The proposed strategy calculates the placement value of the tasks in queue with the current status of the virtual machine (VM) in cluster and reshuffles the task accordingly. The extended IQSLB deals with handling deadlock situation where VM cannot adopt task for execution and task will be reshuffled with another task in another queue. The proposed strategy is compared with few existing systems, and the performance evaluation proves that IQSLB schedules tasks more efficiently than other systems. Our proposed IQSLB takes 75 s to allocate 1000 tasks by using 55 virtual machines which is much lesser than existing techniques.

The quality assurance of two-stage population-based cancer screening program is determined by arrival rate (attending screening), positive rate (determined by the criteria of screening test), the compliance and the waiting time (WT) for confirmatory diagnosis in those screened as positive. These parameters were correlated between the process of screening procedures and the effectiveness of screening program. To capture such an inter-dependence of these parameters and quantify the effectiveness of program, we proposed a Queue hurdle Coxian phase-type (QH-CPH) model to estimate the arrival rate of screenees with the Poisson Queue process and the compliance rate of confirmatory diagnosis with the hurdle model, and also to identify the hidden states of WT that is affected by the capacity of health care and relevant covariates (such as demographic features and geographic areas) with the Coxian phase-type (CPH) process. We applied the proposed QH-CPH model to Taiwanese nationwide colorectal cancer screening program data for estimating the arrival rate and the probability of not complying with colonoscopy and classifying the compliers into two hidden states, short-waiting phase and long-waiting phase for colonoscopy. Significant covariates responsible for three processes were also identified by using the proportional hazards regression forms. A simulation study was further performed to assess the joint effect of these parameters on WT through a series of scenarios. The proposed QH-CPH model can provide an insight into the optimal and the practical design on population-based cancer screening for health policy-makers given the limited health care resources and capacity.

In slotted-ALOHA (S-ALOHA) system, users should contend for the channel to transmit a packet in their queue every single time, irrespective of how many packets they have in their queue. The maximum throughput of S-ALOHA system is known as $e^{-1}\approx 0.3679$ (packets/slot) for a large number of users. This work proposes a novel S-ALOHA system, where the user with a successful (re)transmission of the packet at the head of queue is allowed to keep on transmitting his packet with some reservation probability if his queue is not empty. We call this system S-ALOHA system with successive transmission (ST) and show that the throughput of the proposed system becomes one packet per slot as the reservation probability is raised. We analyze the stability region of S-ALOHA systems with ST for two heterogeneous users and also investigate the stability condition of the system with $N$ symmetric users. Finally, we develop a Bayesian-learning backoff algorithm, with which users can control their (re)transmission with real-time estimation on the backlogged users. It is shown that the system with the proposed backoff algorithm can achieve the theoretical limit of the throughput.

In large-scale service systems, it is a common practice to organize customers with similar service requirements into a single queue served by a group of servers. This pooled queue structure is deemed highly efficient because the servers’ idleness will be minimized. In “To Pool or Not to Pool: Queueing Design for Large-Scale Service Systems,” Cao, He, Huang, and Liu demonstrate that the dedicated queue structure, under which each server has her own queue, could be more advantageous for improving the system’s service level. Moreover, the servers’ additional idleness induced by the dedicated queue structure will be negligible when the system scale is large. By solving a staffing problem, this study also intends to help service system designers answer the following question: To achieve a specified service-level objective in a more efficient manner, should the servers have a common queue or separate queues?

We study an M/M/1/K queue where customers can reschedule their appointments. Rescheduled customers show up with higher probabilities, incurring lower no-show costs, but rescheduling also frees up slots that may not be filled later, leading to wasted service capacity and lower throughput. The system manager aims to minimize the long-run average cost by controlling rescheduling policies. We derive conditions under which rescheduling should be allowed for different scenarios depending on whether customers can reschedule only once or multiple times.

This paper examined the impact of a network attack on a congested transmission session. The research is motivated by the fact that the previous research community has neglected to evaluate security issues related to network congestion environments, and has instead concentrated on resolving congestion issues only. At any point in time, attackers can take advantage of the congestion problem, exploit the attack surface, and inject attack vectors. In order to circumvent this issue, a machine learning algorithm is trained to correlate attack vectors from the attack surface in a network congestion signals environment with the value of decisions over time in order to maximise expected attack vectors from the attack surface. Experimental scenario that dwell on transmission rate overwhelming transmission session, resulting in a standing queue was used. The experiment produced a dataset in which a TCP transmission through bursting transmission were capture. The data was acquired using a variety of experimental scenarios. Nave Bayes, and K-Nearest Neighbours prediction analyses demonstrate strong prediction performance. As a result, this study re-establishes the association between attack surface and vectors with network attack prediction.

The viral transmission in public places and transportations can be minimized by following the world health organization (WHO) guideline. However, the uncertainty in a dynamic system complicates the social engagement to the physical distancing regulation. This study aims to overcome this obstacle in MRT stations and train by developing an adaptive queue line system. The system was developed using low-cost hardware and open-source software to guide passengers using visual information. The system works by capturing seat images and identify the presence of humans using a cloud machine learning service. The physical representation of MRT was translated to data representation using the internet of things (IoT). The data then streamed using an asynchronous API with a representative endpoint. The endpoint is then accessed by a display computer in the destination station platform to provide visual information. The visual information was ergonomically designed with visual display principles, including the minimum content load, layout, color combination, and dimension of contents. The design of the system was evaluated by Markov simulation of virus transmission in train and usability testing of the visual design. The implementation of the system has balanced the queue line capacity in station and crowd spots distribution in MRT. The system was effective due to the visual cortex manipulation by visual information. Consequently, the aerosol and falling droplets' viral transmission radius can be reduced. Accordingly, the chance for airborne transmission can be lowered. Therefore, the adaptive queue line system is a non-pharmaceutical intervention of viral transmission diseases in public transportation

We consider a make-to-stock environment with a single production unit that corresponds to a single machine or a line. Production and hence inventory are controlled by the two-critical-number policy. Production times are independent and identically distributed general random variables and demands are generated according to a stationary Poisson process. We model this production-inventory system as an M/G/1 make-to-stock queue. The main contribution of the study is to extend the control of make-to-stock literature by considering general production times, lost sales and fixed production costs at the same time. We characterize the long-run behaviour of the system and also propose a simple but very effective approximation to calculate the control parameters of the two-critical-number policy. An extensive numerical study exhibits the effects of the production time distribution and the system parameters on the policy control levels and average system cost.

A MANET (Mobile Ad-hoc Network) is a group of mobile network nodes dynamically forming a network without any pre-existing infrastructure. Multi-path routing protocols in MANETs try to discover and use multiple routes between source and destination nodes. Multipath routing is typically used to reduce average delay, increase transmission reliability, provide load balancing among multiple routes, and improve security and overall QoS (Quality of Service). In this paper, the Cluster-Based Routing Protocol (CBRP), which is a single path MANET protocol is enhanced to use multiple paths. The traffic will be distributed among multiple paths to reduce network traffic congestion and decrease delay. An analytical model is used for multipath and single path CBRP routing protocols in MANETs to estimate the end-to-end delay and queue length. The analytical results show that the average delay and average queue length in multipath CBRP are less than the average delay and queue length in single path CBRP.

Low-latency and energy-efficient wireless communication holds the potential of enabling the industrial internet of things (IIoT), automatic driving, and telesurgery. Cross-layer scheduling, which is aware of both the channel and queue states, has attracted considerable attention recently because it is capable of reducing the average delay substantially while meeting a given average power constraint. As a result, there has been considerable work, in which joint channel and buffer aware scheduling is formulated as a constrained Markov decision process (CMDP). In general, the optimal solution to a CMDP problem is characterized by the stationary probability of actions, yielding probabilistic cross-layer scheduling with possibly high complexity in practice. In this paper, we are interested in the low-latency and energy-efficient stationary cross-layer scheduling policy for wireless channels with continuous channel states, e.g. Rayleigh fading. It is interestingly shown that a deterministic cross-layer scheduling policy can achieve the optimal tradeoff between the average delay and power. In other words, the signaling complexity of cross-layer scheduling can be significantly reduced without causing sub-optimality. Simulation results also demonstrate that our work provides a low-complexity solution for low-latency and energy-efficient wireless communications.

We consider a queueing system with multiple stations serving a single class of customers. Each station has many servers, and its own dedicated queue. The system manager must decide to which station each new customer is routed. We propose and analyze a minimum-expected-delay faster-server-first with ξ -choice ( MED-FSF ( ξ ) ) routing policy, under which an integer ξ , with P { ξ ≥ 2 } > 0 , is randomly generated for each new customer. Then, a subset of ξ stations will be randomly collected, with state information being retrieved. Among these collected stations, the system manager assigns the new customer to: (i) the one with minimum expected delay if all these ξ stations are fully occupied; or (ii) the one with fastest available server. We prove that in the Halfin–Whitt regime this policy is asymptotically equivalent to the MED-FSF policy, which only works when full information of the system state is available. Moreover, we compare the MED-FSF ( ξ ) policy with the random routing policy, which uses no information and routes each incoming customer to a station in a random manner. Using simulation experiments, we validate the theoretical results, and find that our proposed policy significantly outperforms the random routing policy. This suggests that a slight increase in information at each routing epoch can substantially improve system performance.

La atención al cliente como parte fundamental de la gestión empresarial, ha generado cambios sustanciales con el avance tecnológico, lo que ha obligado a las empresas a desarrollar nuevas estrategias para mantenerse en el contexto competitivo de la industria, la misma que se ve evidenciada con la atención ágil en la fila de un banco, supermercado, oficinas de gobierno, agencias de viaje. El tiempo de espera genera pérdidas económicas para los usuarios y en las empresas genera pérdida de cuota de mercado sumado a la mala reputación por la baja calidad de atención. El objetivo de este trabajo generar un aporte con un análisis de la teoría de colas y tiempo de espera para la industria de las agencias de viaje, que en el contexto de la pandemia se han visto obligadas a utilizar la tecnología basado en la metodología de un modelo de colas de canales múltiples, sujetos a diversos supuestos, orientados con trabajos previos de diferentes autores. Los resultados principales evidencian que se debe trabajar en el tiempo promedio de atención, apoyado con el uso de la tecnología, y de esta manera limitar a ciertas actividades del proceso la atención telefónica, reduciendo de manera significativa los tiempos de espera en la fila y de esta manera propender a una mejora continua en la atención al cliente.

The 6G space-air-ground integrated network can achieve global full-area three-dimensional coverage and ultrawide-area broadband access capabilities anytime and anywhere. Through the integration of satellite networks and terrestrial networks, it can provide a better user experience and has become the core development direction of 6G networks. Inspired by the potential of service differentiation ability of 6G IEEE 802.11ax protocols, say 6G Wi-Fi 6, a Data-driven Fair-Hierarchical Scheduling (DFHS) is proposed in this paper to schedule packets from diversified applications in dense IoT networks, which allows data streams to fairly share the spectrum and at the same time satisfy deadline requirements. Our DFHS is divided into the outer-layer and inner-layer schedulers. First, diversified packets of IoT services are classified into categories according to delay requirement and transmission frequency. After that, the outerlayer scheduler assign classified packets to different Access Categories with differentiated channel competition capabilities. Next, the inner-layer scheduler optimally schedules the AC queues according to access response ratio of packets. A Particle Swarm Optimization algorithm is further introduced to solve the optimal scheduling problem of packets. Numerical results demonstrate that DFHS outperforms classic FCFS, the latest EARS and Tians schemes in terms of Service Missing Ratio, queueing delay and fair index.

In order to better explore the ocean, autonomous underwater vehicles (AUVs) have been widely applied to facilitate the information collection. However, considering the extremely large-scale deployment of sensor nodes in the Internet of Underwater Things (IoUT), a homogeneous AUV-enabled information collection system cannot support timely and reliable information collection considering the time-varying underwater environment as well as AUV’s energy and mobility constraints. In this article, we propose a multi-AUV-assisted heterogeneous underwater information collection scheme for the sake of optimizing the peak Age of Information (AoI). Moreover, the limited service M/G/1 vacation queueing model is utilized to model the process of information exchange, where the optimal upper limit of the number of AUVs served in the queueing system as well the steady-state distribution of the queue length are derived. A low-complexity adaptive algorithm for adjusting the upper limit of the queuing length is also proposed. Finally, simulation results validate the effectiveness of our proposed scheme and algorithm, which outperform traditional methods in terms of the peak AoI.

The randomized drop when seen (rDWS) scheme is an online, feedback-based network coding technique proven to be an efficient, lightweight variant of the deterministic drop when seen (DWS) scheme. We have proposed a non-generation-based, generalized version of rDWS with stochastic packet arrival at the sender queue (SQ) in this letter. Next, a queueing analysis is performed to find the expected SQ length with respect to the load factor. Analysis shows that asymptotic SQ length in rDWS approaches the same of DWS as the innovativeness factor approaches unity, which establishes the effectiveness of rDWS in maintaining shorter expected SQ length.

Electric vehicle (EV) fast-charging stations (CSs) with innovative operation management strategies can help to meet the growing EV charging needs. Despite the short charging time associated with th...

Wireless sensor network (WSN) is a core component of multiple smart city applications. Utilizing the same WSN for multiple applications helps reduce cost. However, satisfying quality of service requirements of these independent applications is very challenging. For instance, uncoordinated path selection for data dissemination may result in the formation of queues in the WSN violating end-to-end delay requirements of several applications. To this end, we propose a software defined network based approach to ensure satisfaction of individual delay constraints while ensuring minimal increase in the average queue length of the WSN. The approach utilizes a logically centralized controller to generate a comprehensive view of the whole network in a scalable manner. We develop several graph theoretic algorithms to reduce the number of nodes and edges in the communication paths and to identify the most suitable communication paths for each application so that end-to-end delays are minimized. The evaluations demonstrate that our approach performs up to 34% better than existing works and up to 14% worst in comparison to the optimal solution for different topologies, network sizes, and end-to-end delay requirements. Moreover, performance of the proposed graph theoretic algorithms is also measured w.r.t. time.

This paper considers the equilibrium balking behavior of customers in a single-server Markovian queue with variable vacation and vacation interruption, where the server can switch across four states: vacation, working vacation, idle period, and busy period. Once the queue becomes empty, the server commences a working vacation and slows down its service rate. However, this period may be interrupted anytime by the vacation interruption. Upon the completion of a working vacation, the server takes a vacation in a probability-based manner and stops service if the system is empty. The system stays idle after a vacation until a new customer arrives. The comparisons between the equilibrium balking strategy of customers and the optimal expected social benefit per time unit for each type of queue are elucidated and the inconsistency between the individual optimization and the social optimization is revealed. Moreover, the sensitivity of the expected social benefit and the equilibrium threshold with respect to the several parameters as well as diverse precision levels is illustrated through numerical examples in a competitive cloud environment.

The fifth-generation (5G) new radio standard defines various functions for forwarding treatments under differentiated quality of services requirements. In particular, a gNB base station helps combat overbuffering due to massive packet flows from associated mobile devices. Several active queue management (AQM) schemes have been proposed previously, with controlled delay (CoDel) proving to be an efficient method that can properly handle excessive buffering, that is called bufferbloat. This article proposes a novel and enhanced AQM scheme based on CoDel for gNB that guarantees low queuing delays, minimizes packet drop rate, and furthermore stabilizes the built-in queue. According to data-intensive performance evaluation results, the proposed scheme verifies that the queuing delay for served packets are decreased and supports ultrareliable low latency communication applications. Especially, the proposed algorithm achieves the packet drop rates and queuing delays tradeoff as $[\mathcal {O}(1/V), {O}(V)]$ where $V$ is a tradeoff parameter; providing efficient control decisions to improve performance in terms of time-average packet drop rate minimization subject to queue stability on demand in 5G mobile system.

The efficient use of resources in wireless communications has always been a major issue. In the Internet of Things (IoT), the energy resource becomes more critical. The transmission policy with the aid of a coordinator is not a viable solution in an IoT network, since a node should report its state to the coordinator for scheduling and it causes serious signaling overhead. Machine learning algorithms can provide the optimal distributed transmission mechanism with little overhead. A node can learn by itself by utilizing the machine learning algorithm and make the optimal transmission decision on its own. In this paper, we propose a novel learning Medium Access Control (MAC) protocol with learning nodes. Nodes learn the optimal transmission policy, i.e., minimizing the data and energy queue levels, using the Q-learning algorithm. The performance evaluation shows that the proposed scheme enhances the queue states and throughput.