Abstract: We study how queue configuration affects human servers’ service time by comparing dedicated queues with shared queues using field data from a natural experiment in a supermarket. We hypothesize that queue configuration may affect servers’ service rate through several mechanisms: pooling may affect service rate directly as a result of social loafing effect and competition effect and indirectly via its impact on queue length. To investigate these impacts, we take advantage of the supermarket’s checkout layout and use a data set containing both checkout transaction details and queue information collected from video recordings in the supermarket. After we control for the queue length, we find that servers in dedicated queues are about 10.7% faster than those in shared queues, mainly because of the social loafing effect. We also demonstrate that pooling has an indirect negative effect on service time through its impact on queue length. In addition, the queue configuration’s direct effect and its indirect queue...

Abstract: A long line of research examines how best to schedule work to improve operational performance. This literature typically takes the perspective of a central planner who directs individuals to execute tasks in a prescribed order. In many settings, however, workers have discretion to deviate from the assigned order. This paper considers the operational implications of “discretionary task ordering,” defined as the task sequence resulting from an individual’s ability to select which task to complete next from a work queue. Using data from more than 2.4 million radiological studies read by 91 physicians over a period of two and a half years, we examine the conditions under which discretion is exercised to deviate from the assigned First-In-First-Out scheduling policy, and the performance effects of those choices. Exploiting random assignment of tasks (cases) to doctors’ queues, together with variation in queue characteristics, we find that, on average, deviations lead to slower completion times, providing evidence of the costs of exercising discretion. Doctors tend to deviate more and deviations tend to be less detrimental with experience, yet deviations remain harmful even for high levels of experience. Moreover, doctors tend to deviate to follow two common ordering strategies: shortest expected processing time and batching similar cases. Choosing the shortest tasks first is particularly detrimental for speed. Batching is associated with better performance when it occurs naturally, but not when it results from using discretion, suggesting that the benefit of repetition does not compensate for the cost of exercising discretion in this setting. Our research offers a behavioral perspective on queue management and highlights that discretion may have unintended negative costs.

Abstract: Cloud data centers (CDC) are an integral part of today's internet services. Enterprises and Businesses around the world rely heavily on data centers for their daily computation and IT operations. In fact, every time we search for an information on the internet, or we use an application on our smartphones, we access data centers. In CDC, most compute resources are represented as virtual machines (VMs) which are mapped into physical machines (PMs). Performance is often is a key metric for CDC. This paper presents a stochastic model based on queuing theory to aid in studying and analyzing performance in CDC. CDC platforms are modeled with an open queuing system that can be used to estimate the expected Quality of Service (QoS) guarantees the cloud can offer. We give numerical examples to show how the model estimates the number of required VM instances needed to satisfy a given the QoS parameters. In particular, we plot the response time, drop rate and CPU utilization while varying the incoming request arrival rate, and for different number of VM instances. We cross-validate our analytical model using a DES (Discrete Event Simulator). Our analysis and simulation results show that the proposed model is able to estimate the number of VMs needed to achieve QoS targets when varying the arrival request rate.

Abstract: People often have to queue for a busy service in many places around a city, and knowing the queue time can be helpful for making better activity plans to avoid long queues. Traditional solutions to the queue time monitoring are based on pre-deployed infrastructures, such as cameras and infrared sensors, which are costly and fail to deliver the queue time information to scattered citizens. This paper presents CrowdQTE, a mobile crowdsensing system, which utilizes the sensor-enhanced mobile devices and crowd human intelligence to monitor and provide real-time queue time information for various queuing scenarios. When people are waiting in a line, we utilize the accelerometer sensor data and ambient contexts to automatically detect the queueing behavior and calculate the queue time. When people are not waiting in a line, it estimates the queue time based on the information reported manually by participants. We evaluate the performance of the system with a two-week and 12-person deployment using commercially-available smartphones. The results demonstrate that CrowdQTE is effective in estimating queuing status.

Abstract: The random early detection active queue management (AQM) scheme uses the average queue size to calculate the dropping probability in terms of minimum and maximum thresholds. The effect of heavy load enhances the frequency of crossing the maximum threshold value resulting in frequent dropping of the packets. An adaptive queue management with random dropping algorithm is proposed which incorporates information not just about the average queue size but also the rate of change of the same. Introducing an adaptively changing threshold level that falls in between lower and upper thresholds, our algorithm demonstrates that these additional features significantly improve the system performance in terms of throughput, average queue size, utilization and queuing delay in relation to the existing AQM algorithms.

Abstract: We study the problem of optimal information sharing in the context of a service system. In particular, we consider an unobservable single server queue offering a service at a fixed price to a Poisson arrival of delay-sensitive customers. The service provider can observe the queue, and may share information about the state of the queue with each arriving customer. The customers are Bayesian and strategic, and incorporate any information provided by the service provider into their prior beliefs about the queue length before making the decision whether to join the queue or leave without obtaining service. We pose the following question: which signaling mechanism and what price should the service provider select to maximize her revenue? We formulate this problem as an instance of Bayesian persuasion in dynamic settings. The underlying dynamics make the problem more difficult because, in contrast to static settings, the signaling mechanism adopted by the service provider affects the customers' prior beliefs about the queue (given by the steady state distribution of the queue length in equilibrium). The core contribution of this work is in characterizing the structure of the optimal signaling mechanism. We summarize our main results as follows. (1) Structural characterization: Using a revelation-principle style argument, we find that it suffices to consider signaling mechanisms where the service provider sends a binary signal of "join" or "leave", and under which the equilibrium strategy of a customer is to follow the service provider's recommended action. (2) Optimality of threshold policies: For a given fixed price for service, we use the structural characterization to show that the optimal signaling mechanism can be obtained as a solution to a linear program with a countable number of variables and constraints. Under some mild technical conditions on the waiting costs, we establish that there exists an optimal signaling mechanism with a threshold structure, where service provider sends the "join" signal if the queue length is below a threshold, and "leave" otherwise. (In addition, at the threshold, the service provider randomizes.) For the special case of linear waiting costs, we derive an analytical expression for the optimal threshold i terms of the two branches of the Lambert-W function. (3) Revenue comparison: Finally, we show that with the optimal choice of the fixed price and using the corresponding optimal signaling mechanism, the service provider can achieve the same revenue as with the optimal state-dependent pricing mechanism in a fully-observable queue. This implies that in settings where state-dependent pricing is not feasible, the service provider can effectively use optimal signaling (with the optimal fixed price) to achieve the same revenue.

Abstract: Distributed cloud networking enables the deployment of a wide range of services in the form of interconnected software functions instantiated over general purpose hardware at multiple cloud locations distributed throughout the network. We consider the problem of optimal service delivery over a distributed cloud network, in which nodes are equipped with both communication and computation resources. We address the design of distributed online solutions that drive flow processing and routing decisions, along with the associated allocation of cloud and network resources. For a given set of services, each described by a chain of service functions, we characterize the cloud network capacity region and design a family of dynamic cloud network control (DCNC) algorithms that stabilize the underlying queuing system, while achieving arbitrarily close to minimum cost with a tradeoff in network delay. The proposed DCNC algorithms make local decisions based on the online minimization of linear and quadratic metrics obtained from an upper bound on the Lyapunov drift-plus-penalty of the cloud network queuing system. Minimizing a quadratic vs. a linear metric is shown to improve the cost-delay tradeoff at the expense of increased computational complexity. Our algorithms are further enhanced with a shortest transmission-plus-processing distance bias that improves delay performance without compromising throughput or overall cloud network cost. We provide throughput and cost optimality guarantees, convergence time analysis, and extensive simulations in representative cloud network scenarios.

Abstract: The efficiency of spatial computing depends on the ability to achieve maximal parallelism. This necessitates memory interfaces that can correctly handle memory accesses that arrive in arbitrary order while still respecting data dependencies and ensuring appropriate ordering for semantic correctness. However, a typical memory interface for out-of-order processors (i.e., a load-store queue) cannot immediately meet these requirements: a different allocation policy is needed to achieve out-of-order execution in spatial systems that naturally omit the notion of sequential program order, a fundamental piece of information for correct execution. We show a novel and practical way to organize the allocation for an out-of-order load-store queue for spatial computing. The main idea is to dynamically allocate groups of memory accesses (depending on the dynamic behavior of the application), where the access order within the group is statically predetermined (for instance by a high-level synthesis tool). We detail the construction of our load-store queue and demonstrate on a few practical cases its advantages over standard accelerator-memory interfaces.

Abstract: A queuing system resulting from a signalized intersection regulated by pretimed control in a network of urban traffic is considered. Modeling the queue length and the delay of vehicles is crucial to evaluate the performance of intersections equipped with traffic signals. Air quality and rational use of energy also depend on an efficient management of the intersections. These traffic systems have the specificity that the server (green signal) is deactivated (red signal) during a fixed period of time. In the present work, an M/D/1 queue with a server that occasionally takes vacations is analyzed. The mean delays of vehicles and the mean queue length are computed and compared with those obtained by using a detailed simulation model in a case study. We find that, in general, the mean delays of vehicles given by the proposed queuing model provide a good approximation, but the model can provide slightly smaller values than those obtained in the simulation model for large traffic flows. This result is of interes...

Abstract: A standard priority-queuing system is capable of arranging packets with different traffic classes to guarantee a relatively low latency for the high priority traffic. However, in practical cases, severe delay may be caused by starting a large, low-priority frame ahead of a time-critical frame. In this paper, interspersed express traffic is evaluated, which enables preemption of non-time-critical transmission, in particular, the preemptive queuing system allows the cut-through transmission for critical traffic and minimizes the jitter. We analyse the performance of packet preemption through a system level simulation in Riverbed Modeler. The simulation is complemented by numerical analysis which provides the average queuing delay for both types of traffic (preemptable and express). Furthermore, the paper describes an approach to implement the packet preemption solution on an FPGA in VHDL, which illustrates the complexity of hardware implementation.

Abstract: We study the information-theoretic limit of reliable information processing by a server with queue-length dependent quality of service. We define the capacity for such a system as the number of bits reliably processed per unit time, and characterize it in terms of queuing system parameters. We also characterize the distributions of the arrival and service processes that maximize and minimize the capacity of such systems in a discrete-time setting. For arrival processes with at most one arrival per time slot, we observed a minimum around the memoryless distribution. We also studied the case of multiple arrivals per time slot, and observed that burstiness in arrival has adverse effects on the system. The problem is theoretically motivated by an effort to incorporate the notion of reliability in queuing systems, and is applicable in the contexts of crowdsourcing, multimedia communication, and stream computing.

Abstract: The implementation of safety applications in vehicular ad hoc networks (VANETs) depends on the dissemination of safety-related messages. A self-sorting MAC protocol is proposed for high-density scenarios. The protocol allows vehicles to sort with others in a collision-tolerance manner before data transmission. The vehicles establish a logic queue by the self-sorting process, and the queue is able to access the channel once the length reaches the set threshold. Vehicles in the queue will access the channel by time-division multiple access when the queue occupies the channel. A queue will compete for accessing the channel on behalf of all the nodes in the queue, which greatly alleviates the contention for access from all nodes. In contrast with completely random access, the slot a queue selects to access the channel depends on the completion time of the self-sorting process. In this case, the queue accomplishing the self-sorting process first can avoid collisions with other queues, since they are still in the self-sorting process. The performance of the proposed protocol is evaluated compared with other typical MAC protocols in VANET. The analysis and simulation results in highway and city scenarios show that the proposed protocol can significantly reduce packets loss and delay especially in dense scenarios.

Abstract: Tandem queues with finite buffer capacity commonly exist in practical applications. By viewing a tandem queue as an integrated system, an innovative approach has been developed to analyze its perfo...

Abstract: A system in accordance with present embodiments includes a plurality of wearable devices and a virtual queue control system configured to maintain respective virtual queues for respective attractions and in communication with the plurality of virtual queuing attraction stations. The virtual queue control system is configured to receive communications from the plurality of virtual queuing stations and add guests to the respective virtual queues based on the communications.

Abstract: Tandem queues have been used to model congestion in a wide variety of systems such as communication networks, manufacturing systems, supply chains and traffic flows. This paper considers the optimal control of tandem queues in order to mitigate surface congestion at large airports. The taxi-out process is modeled by two queues in tandem: the first one represents aircraft in a congested ramp or apron area, and the second one reflects aircraft waiting in the departure runway queue. The evolution of the mean queue lengths are described using ordinary differential equations. The resulting model is used to determine the optimal gate release rate for departure flights, in order to control the lengths of queues on the airport surface. Simulations of the optimal control policy show a reduction in queue lengths, resulting in lower taxi-out times.

Abstract: In this paper, a practical real-time leader's path following control system is proposed. The control system which is composed of pose estimation of preceding vehicle, leader's path queue management and autonomous controller is a general framework for manned and unmanned vehicle convoys. Then the algorithms for waypoints management, vision-based vehicle tracking, LIDAR-based vehicle tracking, EKF-based data fusion, adaptive inter-distance control and model-based trajectory following are described in detail. Accordingly, the proposed approach is developed and implemented on newly developed UGVs and numerous field tests are carried out to evaluate the system performance in both on-road and off-road scenarios. Experiments show that the system performs well and is robust to environmental disturbance.

Abstract: The aim of this study was to simulate pedestrian movements in the queuing process in subway stations using microscopic simulation models. The queuing process was divided into two stages – walking and selecting a queue, and entering a queue and waiting in that queue. To represent the boundary of these two stages, a novel queuing line concept was developed. This queuing line dynamically changes with factors such as pedestrian preference, queue shape and queue length. Furthermore, two corresponding microscopic simulation models were developed to handle those two stages on the basis of the social force model and the queuing rule, respectively. Typically, the queuing rule is determined by the desired force, the impact of the nearest pedestrian ahead in the same direction and a self-stopping mechanism. Two examples – a ticket office and a security check in a subway station – were used to verify the validity of the proposed models.

Abstract: The stability of a cyclic polling system, with a single server and two infinite-buffer queues, is considered. Customers arrive at the two queues according to independent batch Markovian arrival processes. The first queue is served according to the gated service discipline, and the second queue is served according to a state-dependent time-limited service discipline with the preemptive repeat-different property. The state dependence is that, during each cycle, the predetermined limited time of the server's visit to the second queue depends on the queue length of the first queue at the instant when the server last departed from the first queue. The mean of the predetermined limited time for the second queue either decreases or remains the same as the queue length of the first queue increases. Due to the two service disciplines, the customers in the first queue have higher service priority than the ones in the second queue, and the service fairness of the customers with different service priority levels is also considered. In addition, the switchover times for the server traveling between the two queues are considered, and their means are both positive as well as finite. First, based on two embedded Markov chains at the cycle beginning instants, the sufficient and necessary condition for the stability of the cyclic polling system is obtained. Then, the calculation methods for the variables related to the stability condition are given. Finally, the influence of some parameters on the stability condition of the cyclic polling system is analyzed. The results are useful for engineers not only checking whether the given cyclic polling system is stable, but also adjusting some parameters to make the system satisfy some requirements under the condition that the system is stable.

Abstract: The invention provides an online vehicle washing queuing system. According to the online vehicle washing queuing system, reservation queuing requests are sent by mobile terminals to a server, a real-time queuing number list is sent to the mobile terminals by the server according to the reservation queuing requests, and the real-time queuing number list is generated according to the reservation queuing requests of the mobile terminals received by the server and the information of vehicles arriving a store. Queuing for vehicle washing is reserved by vehicle washing-needing users through the mobile terminals to the server, the real-time queuing number list is formed by the server according to the reservation queuing requests, the list is sent to the mobile terminals, so service conditions of each store can be queried by the vehicle washing-needing users, online queuing is carried out, queuing sequence of the washing-needing users at one store can be queried, and the vehicle washing condition of the present store is mastered; the quantity of washing-needing vehicles and the time can be known by the vehicle washing-needing users and washing service providing stores, so the vehicle washing-needing users are made to arrive the store in time, queuing for long time for vehicle washing in the store can be avoided, the time of two parties can be saved, and road occupation caused by vehicle washing field queuing can be avoided.

Abstract: The objects of study of this paper are communication channels in which the dominant type of noise are symbol shifts, the main motivating examples being timing and bit-shift channels. Two channel models are introduced and their zero-error capacities and zero-error-detection capacities determined by explicit constructions of optimal codes. Model A can be informally described as follows: 1) The information is stored in an $n $ -cell register, where each cell is either empty or contains a particle of one of $P $ possible types and 2) due to the imperfections of the device each of the particles may be shifted several cells away from its original position over time. Model B is an abstraction of a single-server queue: 1) The transmitter sends packets from a $P $ -ary alphabet through a queuing system with an infinite buffer and a first-in-first-out service procedure and 2) each packet is being processed by the server for a random number of time slots. More general models including additional types of noise that the particles/packets can experience are also studied, as are the continuous-time versions of these problems.

Abstract: In many real-life queueing systems, a customer may balk upon arrival at a queueing system, but other customers become aware of it only at the time the balking customer was to start service. Naturally, the balking is an outcome of the queue length, and the decision is based on a threshold. Yet the inspected queue length contains customers who balked. In this work, we consider a Markovian queue with infinite capacity and with customers that are homogeneous with respect to their cost reward functions. We show that that no threshold strategy can be a Nash equilibrium strategy. Furthermore, we show that for any threshold strategy adopted by all, the individual's best response is a double threshold strategy. That is, join if and only if one of the following is true: (i) the inspected queue length is smaller than one threshold, or (ii) the inspected queue length is larger than a second threshold. Our model is under the assumption that the response time of the server when he finds out that a customer balked is negligible. We also discuss the validity of the result when the response time is not negligible.

Abstract: In this paper, we study the performance of service systems with priority upgrades. We model the service system as a single-server two-class priority queue, with queue 1 as the normal queue and queue 2 as the priority queue. The queueing model of interest has various applications in healthcare services, perishable inventory and project management. We comprehensively examine the system’s stationary distribution, computational algorithm design and sensitivity analysis. We observe that when queue 2 is large, the conditional distribution of queue 1 approximates a Poisson distribution. The tail probability of queue 2 decays geometrically, while the tail probability of queue 1 decays much faster than queue 2’s. This helps us design an algorithm that computed the stationary distribution. Finally, by using the algorithm, we perform a sensitivity analysis on various system parameters, i.e., the arrival rates, service rates and the upgrade rate. The numerical study provides helpful insights into designing such service systems.

Abstract: This paper presents the design and evaluation process of a self-learning system for local ramp metering control. This system is developed on the basis of reinforcement learning (RL) and can deal with the problem of on-ramp queue management through a continuous learning process. A general framework of the system design including the definition of RL elements and an algorithm that can accomplish the learning process is proposed. Simulation tests are carried out to evaluate the performance of the new system. In terms of the total time spent by road users, the new system can achieve a 30% reduction from the situation of no control, a result which is competitive with the widely accepted algorithm ALINEA. Meanwhile, simulation results show that the new system can keep on-ramp queues strictly under a series of pre-specified constraints, which proves its capability of managing on-ramp queues.

Abstract: We consider joint capacity---inventory management for multi-server make-to-stock queues operating under a base stock policy. The number of servers corresponds to the capacity decision, and the base stock level is the inventory decision. Our goal is to minimize a combination of capacity, inventory, and backordering costs. We develop a square-root rule for the joint decision and justify the rule analytically in a many-server queue asymptotic framework. We demonstrate the accuracy of the square-root rule, first via our derivation and numerical assessment of a novel corrected diffusion approximation and then more directly by conducting extensive numerical experiments. Finally, we provide operational insights into the trade-offs involved in such joint management problems, through various analysis based on the square-root rule as well as a comparison with analogous results for single-server make-to-stock queues.