First-come-first-served Research Articles

Analysis of a Markovian Queue of Single Server Performing in MultiPhase Subject to Disaster, Recovery and Repair

In this paper, we can study about Markovian single-server queue with servers in distinct phases of disaster and repair. The server can stay in full-active Phase or passive Phase randomly and alternately. The server is set to serve in two Phases, such as the full active Phase and passive Phase. When the server operates in full active Phase, the customers arrive and served in First Come: First Serve (FCFS) queue discipline. However, when the server switches from full active to passive Phase, it can only offer to provide service at a rate lower than that of the server in full active Phase. During passive Phase, customers are not allowed to join the system. The server moves to repair Phase immediately after the last customer is served, irrespective of the Phase. The customers will be restricted from joining the queue in repair Phase. After the repair Phase, the server will enter the full active Phase. The server remains in the system for a random period of time. In the event of a disaster, in full active and passive Phases, all customers in system aredropped out, and system moves to repair Phase. The expressions for the steady state probabilities and some key performance metrics are obtained. A numerical illustration is made to study the effects of server shifting from full active phase to passive phase that affects the customers in the system and the occurrence of disaster which has an adverse effect on the customers in that phase.

Reducing Makespan and Enhancing Resource Usage in Cloud Computing With ESJFP Method: A New Dynamic Approach

ABSTRACTA well‐designed web‐based tool or application allows consumers to access on‐demand services on a pay‐per‐use basis via the internet in the cloud computing service paradigm. Cloud computing continues to be a leading technology trend, with a primary focus on optimizing and enhancing end‐user applications. The increasing challenge of meeting the diverse needs of clints for service providers is propelling the development of load scheduling algorithms. Some of the current scheduling algorithms used in cloud computing include First Come First Serve (FCFS), Shortest Job First (SJF) and Round Robin (RR). The response time and makespan—the interval between the start and end times of consecutive tasks on the same machine—are the two most crucial load balancing elements. Response time refers to the duration a server takes to respond to a client's request. Measured in milliseconds, this timer starts when a client sends a request and stops when the server sends its initial response. This study examines many load balancing methods and suggests improvements for cloud computing's Shortest Job First (SJF) methodology. To minimize makespan and maximize resource usage, we provide a solution that is Enhanced Shortest Job First with Priority (ESJFP) load scheduling algorithms. Under the ESJFP method, computers with more processing power are assigned the longest tasks with higher MIPS (million instructions per second) needs, while machines with lesser processing capacity are assigned the shortest jobs with lower MIPS requirements. By giving equal priority to all activities, this technique makes sure that neither high‐MIPS nor low‐MIPS jobs have to wait an extended period of time for resource allocation.

Efficient I/O Performance-Focused Scheduling in High-Performance Computing

High-performance computing (HPC) systems are becoming increasingly important as contemporary exascale applications with demand extensive computational and data processing capability. To optimize these systems, efficient scheduling of HPC applications is important. In particular, because I/O is a shared resource among applications and is becoming more important due to the emergence of big data, it is possible to improve performance by considering the architecture of HPC systems and scheduling jobs based on I/O resource requirements. In this paper, we propose a scheduling scheme that prioritizes HPC applications based on their I/O requirements. To accomplish this, our scheme analyzes the IOPS of scheduled applications by examining their execution history. Then, it schedules the applications at pre-configured intervals based on their expected IOPS to maximize the available IOPS across the entire system. Compared to the existing first-come first-served (FCFS) algorithm, experimental results using real-world HPC log data show that our scheme reduces total execution time by 305 h and decreases costs by USD 53 when scheduling 10,000 jobs utilizing public cloud resources.

Allocation of Treatment Slots in Elective Mental Health Care-Are Waiting Lists the Ethically Most Appropriate Option?

Waiting lists are a standard approach to managing excess demand in elective health care. While waiting times are an important policy issue, the ethical validity of the first come, first served (FCFS) principle as such is rarely questioned. Presenting a psychiatric day hospital where all eligible patients have roughly equal claims as a case study, we criticize the reflex use of FCFS for allocation of elective psychiatric care, consider conditions under which this may not be the optimal strategy, and discuss alternatives. We conclude that in our example prioritizing more recent referrals (last come, first served [LCFS]) makes more sense, clinically and ethically. Where several referrals arrive (near-)simultaneously under LCFS, we propose that a higher level of scrutiny be applied to detect possible good reasons for prioritizing one of them. We believe that our observations can be applied to other health care settings that share relevant characteristics with our case.

Power Optimized Task Scheduling using Genetic Algorithm (POTS-GA) in Cloud Environment

In a cloud environment, the allocation of tasks has become pivotal on account of rapid growth of user requests. The processing of user requests leads to a significant execution time, and a huge amount of power is also consumed. Consequently, task scheduling for optimizing makes pan and power usage has become critical, particularly in a heterogeneous environment. This research work proposes Power-Optimized Task Scheduling using Genetic Algorithm (POTS-GA) that aims to minimize execution time and power consumption. The proposed strategy employs genetic algorithm to take scheduling decision while taking into consideration the execution time and overall power consumption of resources. The fitness computation considering both objectives and the customized genetic operators ensure to search for a better scheduling solution. The experiments performed on a large number of tasks and virtual machines show that the proposed POTS-GA approach outperforms other task scheduling strategies including Efficient Task Allocation using Genetic Algorithm (ETA-GA), Round Robin algorithm (RRA), and First Come First Serve (FCFS) and Greedy algorithm in terms of makes pan and power consumption.

Nonlinear Analysis of EDF Scheduling Framework for Resource Distribution in Cloud Computing

In modern times, the field of information technology is significantly shifting towards cloud computing. This innovative technology involves the permanent storage of resources on servers, which clients access via the internet. Typically, a cloud comprises a collection of resources known as virtual machines, capable of managing both computational tasks and storage needs. As the demand for cloud services continues to rise, the scheduling of these resources presents a considerable challenge. Scheduling refers to the workflow management necessary to complete tasks within the system. An efficient scheduler adapts its scheduling policy dynamically in response to changing conditions and task requirements. Cloud computing represents a recently developed technology where resources, whether physical or virtual machines, are permanently stored on servers and accessed by users through the internet. The cloud offers various service models, including Platform as a Service (PaaS), Software as a Service (SaaS), and Infrastructure as a Service (IaaS). In this context, IaaS refers to a set of resources, including virtual machines, that provide computational and storage services. Given the increasing adoption of cloud technology, resource scheduling has become a critical task. Hence, optimizing the scheduling of requests to ensure the best utilization of cloud resources is essential. This paper introduces a Nonlinear Analysis of EDF Scheduling Framework for resource allocation that considers deadlines and processing times when assigning resources to specific jobs. The performance metrics, including Average Turnaround Time, Average Waiting Time, and Average Deadline Violation, show a significant improvement when compared to traditional scheduling models such as First-Come, First-Served (FCFS), the Shortest Job First (SJF), and Simple Earliest Deadline First (EDF) models.

Adaptive Dynamic Programming with Reinforcement Learning on Optimization of Flight Departure Scheduling

The intricacies of air traffic departure scheduling, especially when numerous flights are delayed, frequently impede the implementation of automated decision-making for scheduling. To surmount this obstacle, a mathematical model is proposed, and a dynamic simulation framework is designed to tackle the scheduling dilemma. An optimization control strategy is based on adaptive dynamic programming (ADP), focusing on minimizing the cumulative delay time for a cohort of delayed aircraft amidst congestion. This technique harnesses an approximation of the dynamic programming value function, augmented by reinforcement learning to enhance the approximation and alleviate the computational complexity as the number of flights increases. Comparative analyses with alternative approaches, including the branch and bound algorithm for static conditions and the first-come, first-served (FCFS) algorithm for routine scenarios, are conducted. Moreover, perturbation simulations of ADP parameters validate the method’s robustness and efficacy. ADP, when integrated with reinforcement learning, demonstrates time efficiency and reliability, positioning it as a viable solution for decision-making in departure management systems.

Penerapan Model Multi Channel Single Phase dalam Optimisasi Layanan Restoran Cepat Saji

A queue is a waiting line from a service facility. Long queues are caused by the number of consumers who come more than the number of service facilities, it is necessary to apply a queuing model to overcome long queues. This research was conducted at Mc Donald's Jimbaran fast food restaurant because the fast food restaurant is located in a tourist and lecture area, causing queues. The current condition at Mcdonald’s Jimbaran there are more than one service facility, the discipline of the first come first service (FCFS) queue is in accordance with the single-phase multi-channel. After analyzed with data from 31 May 2022 to 06 June 2022 for 7 days. Based on the results of the analysis obtained, it can be concluded that at lunch time requires 4 services while at dinner time only requires 2 service.

Sharp Waiting-Time Bounds for Multiserver Jobs

Multiserver jobs, which are jobs that occupy multiple servers simultaneously during service, are prevalent in today’s computing clusters. But, little is known about the delay performance of systems with multiserver jobs. We consider queueing models for multiserver jobs in scaling regimes where the system load becomes heavy and meanwhile, the total number of servers in the system and the number of servers that a job needs become large. Prior work has derived upper bounds on the queueing probability in this scaling regime. However, without proper lower bounds, the existing results cannot be used to differentiate between policies. In this paper, we study the delay performance by establishing sharp bounds on the steady-state mean waiting time of multiserver jobs, where the waiting time of a job is the time spent in queueing rather than in service. We first characterize the exact order of the mean waiting time under the first come, first serve (FCFS) policy. Then, we prove a lower bound on the mean waiting time of all policies, which has an order gap with the mean waiting time under FCFS. We show that the lower bound is achievable by a priority policy that we call smallest need first (SNF). Funding: This research was supported in part by the National Science Foundation [Grant ECCS-2145713]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsy.2023.0006 .

Designing Improvement of Service effectiveness Using M/M/1 and M/M/S Queue Models (Case Study: Customer Arrivals at Jatim Park Tourist Destination During Holidays and Peak Hours)

Queueing is a collection of people lined up with the purpose of waiting to be served, after which they leave the queue. Queuing activities often arise due to ineffective or suboptimal service times, resulting in longer wait times. To maximize and schedule service time optimally, queueing models such as M/M/1 and M/M/S systems are utilized. This study applies these models to analyze the queuing system at Jatim Park tourist destination. The aim of this research is to determine the effectiveness of service at the ticketing counters of Jatim Park during holidays. To initiate the study, data was collected on visitor arrival times and average service times. The data analysis aims to optimize service effectiveness at the Kampung Coklat tourist area by implementing the FCFS (First Come, First Served) queueing method and the M/M/S system. The findings suggest that the optimal number of teller servers needed to serve visitors effectively is 2. The analysis also identifies that the longest queue occurs between 10:00 AM and 11:00 AM, with 3 visitors waiting at its peak. The average number of visitors in the queue during this period is approximately 1.3 or 1 visitor. Based on these results, the study concludes that the service effectiveness at the Kampung Coklat tourist area is sufficiently optimized.

Airport Ground Optimizer (AGO): A decision support system initiative for air traffic controllers with optimization and decision-aid algorithms

The growing global air traffic has necessitated more efficient ground management in airports, especially during peak hours. Current approaches mainly consist of radar-based systems and radio communications, which provide limited visual assistance. Hence, Decision Support Systems (DSS) are designed to assist human Air Traffic Controllers (ATCos) by providing reliable recommendations based on current data and situations. The Airport Ground Optimizer (AGO) is a DSS designed for ATCos to manage the complexities of ground traffic in airports. It is based on advanced optimization algorithms and intuitive user interfaces, offering an enhanced operational experience. AGO uses mixed-integer programming (AGO-MIP) and stochastic programming (AGO-STC) to detect conflicts based on expected gate release and taxi times, and then optimizes the entire schedule to minimize hold fuels, resolve conflicts, and reduce fuel consumption, emissions, and delay times. Its key strength lies in translating complex mathematical solutions into user-friendly visual representations. AGO's core functionality includes comprehensive visualizations such as position charts, delay graphs, and potential conflict maps, providing a clear, real-time picture of ground operations for informed decision-making. AGO's queue and gate occupancy analysis calculates and visualizes the maximum queue length and concurrent number of aircraft at gates, enhancing airport capacity, reducing aircraft waiting times, and streamlining ground traffic flow. Simulated in a high-traffic Turkish airport layout, AGO-MIP demonstrated significant improvement in operational efficiency compared to traditional First Come First Served (FCFS) approach. AGO outperformed the traditional FCFS approach, reducing hold fuel by 27.9%, cutting delay time by 21.6%, lowering HC, CO, and NOx emissions by 16.4%, 22.5%, and 29.3% respectively, and decreasing the maximum queue length and its duration by 15.3% and 26.6%, as well as decreasing the number of delayed aircraft by 4.8%. The AGO-STC is also tested using pushback release uncertainties in the case airport, providing robust and feasible sequences, clear feedback for all possible scenarios, and detailed outcomes for each scenario. The study concludes by discussing potential developments and current issues of the tool in detail.

Priority-based subcarrier allocation algorithm for maximal network connectivity in 5G networks

With the widespread adoption of wireless technology, there has been a significant surge in the number of devices seeking wireless connectivity over the past decade. To meet the extensive demand for high-data-rate wireless connectivity, the fifth-generation (5G) cellular network plays a pivotal role. 5G cellular network aims to support a large number of applications with ultra-high data rates by maximizing device connectivity while satisfying quality of service (QoS) requirements. In this paper, we present an innovative priority-based subcarrier allocation (PSA) algorithm to address the challenge of maximizing connectivity in 5G new radio (5G NR) networks. Initially, we formulate the connectivity maximization problem as a subcarrier allocation problem by considering three key parameters: bandwidth requirement, waiting time, and energy level of user devices. The objective of the formulated problem is to optimally allocate subcarriers to multiple users in order to maximize connectivity while maintaining QoS requirements. To address the problem, we propose the PSA algorithm that prioritizes bandwidth, waiting time, and energy parameters using the R-method. To accommodate the network scenarios, we develop three variants of the PSA algorithm—PSA-1, PSA-2, and PSA-3. These variants allocate subcarriers based on the priority-based score of user. We carried out a simulation-based study to illustrate the effectiveness of our proposed algorithm in comparison to traditional methods. The simulation results reveal that our proposed algorithms outperform first come first serve (FCFS) and longest remaining time first (LRTF), and achieves comparable or superior results compared to priority and fairness-based resource allocation with 5G new radio numerology (PFRA-0N) in terms of the number of user allocations, average user allocation ratio, user drop ratios and average connectivity rate. Compared to the shortest job first (SJF) technique, our proposed PSA algorithm performance is slightly inferior in terms of the number of user allocations, average allocation ratio and average connectivity rate; however, it shows superior performance in drop ratios. Further, the proposed algorithms show significant improvements in execution time compared to the optimal exhaustive search solution.

Heavy-Traffic Optimal Size- and State-Aware Dispatching

We study the problem of dispatching jobs to multiple FCFS (First-Come, First-Served) queues. We consider the case where the dispatcher is size-aware, meaning it learns the size (i.e. service time) of each job as it arrives; and state-aware, meaning it always knows the amount of work (i.e. total remaining service time) at each queue. While size- and state-aware dispatching to FCFS queues has been extensively studied, little is known about optimal dispatching for the objective of minimizing mean delay. In this work, we propose the first size- and state-aware dispatching policy, called CARD (Controlled Asymmetry Reduces Delay), that provably minimizes mean delay in heavy traffic. This work summarizes our full paper.

Strongly Tail-Optimal Scheduling in the Light-Tailed M/G/1

We study the problem of scheduling jobs in a queueing system, specifically an M/G/1 with light-tailed job sizes, to asymptotically optimize the response time tail. This means scheduling to make \mathbfP [T > t], the chance a job's response time exceeds t, decay as quickly as possible in the t → ∞ limit. For some time, the best known policy was First-Come First-Served (FCFS), which has an asymptotically exponential tail: P [T > t] ∼ C e -γ t . FCFS achieves the optimal decay rate ~γ, but its tail constant C is suboptimal. We derive a closed-form expression for the optimal tail constant, and we introduce γ-Boost, a new policy that achieves this optimal tail constant. We also show via simulation that γ-Boost has excellent practical performance. This abstract summarizes our full paper.

Fairness of the first-come, first-served rule on the rental housing market: Insights from a hypothetical survey experiment

Echoing recent policies implemented in Seattle and Portland, we examine perceptions of the fairness of the first-come, first-served (FCFS) rule in the context of discrimination in the rental housing market. To do so, we use an original hypothetical survey experiment in which a rental agent is confronted with the discriminatory preferences of his landlord customers. A sample of 2,835 respondents representative of the US population was asked about which choice was the best, from a moral point of view: to allocate rental units exclusively to whichever group applied first (FCFS rule), to the other group or 50/50 to both groups. In two separate experiments, we manipulated (i) the order of arrival of the discriminated and non-discriminated groups, (ii) the income impact of implementing the FCFS rule for the rental agent, who risks losing landlord customers if they rent to the discriminated group, (iii) peer effects, i.e., what other rental agents do and (iv) social norms shared by all members of the community. Consistent with the literature, we find that the order of tenant arrival affects respondents’ normative preferences, and that, second to the 50/50 rule, the FCFS rule is well-received by respondents. Additionally, income, peer influence and social norms all causally impact the level of support for the FCFS rule among respondents. Finally, respondents who are more likely to experience economic hardship and belong to the dominant group in their neighbourhood are the least likely to support the FCFS rule.

Optimizing Resource Allocation in M/M/1/N Queues with Feedback, Discouraged Arrivals, and Reneging for Enhanced Service Delivery

This article presents a novel computational approach for analyzing M/M/1/N queues with feedback, discouraged arrivals, and reneging, under the first-come, first-served (FCFS) discipline. We calculate explicit transient state probabilities and represent results using symmetric tridiagonal matrix eigenvalues. Through numerical simulations, we validate our method, providing practical insights for optimizing resource allocation. Our study contributes to both theory and application, advancing queueing theory and aiding decision-makers in improving service quality and resource management.

Analysis and Modeling of Queuing System Simulation in Payment Process at Minimarket (Case Study of Minimarket X Yogyakarta)

The purpose of this study is to analyze the queuing system at Minimarket X so as to minimize the number of queues. The method in this writing uses a discrete event simulation method with the help of Anylogic software. Simulation is used as a method to analyze problems that exist in the payment system at the Minimarket, while Anylogic software as an illustration that can facilitate the queuing system so that it is clearer and easier to understand. Observations conducted for 20 days show that the queuing system applied by Minimarket X is Multi Channel – Single Phase and First Come First Served (FCFS) or First In First Out (FIFO) with 4 cashier officers (M/M/4). Each cashier is tasked with serving customers and processing the products purchased by scanning them and packing each grocery. The average number of Minimarket visitors every day is 22 customers per hour with a queue length of 10 to 11 customers per hour, and the average waiting time for customers to be served is around 27 to 30 minutes. The simulation used through anylogic software obtained error results within 67 minutes 43 seconds.

Strongly Tail-Optimal Scheduling in the Light-Tailed M/G/1

We study the problem of scheduling jobs in a queueing system, specifically an M/G/1 with light-tailed job sizes, to asymptotically optimize the response time tail. This means scheduling to make \mathbfP [T > t], the chance a job's response time exceeds t, decay as quickly as possible in the t \to \infty limit. For some time, the best known policy was First-Come First-Served (FCFS), which has an asymptotically exponential tail: \mathbfP [T > t] \sim C e^-γ t . FCFS achieves the optimal *decay rate* γ, but its *tail constant* C is suboptimal. Only recently have policies that improve upon FCFS's tail constant been discovered. But it is unknown what the optimal tail constant is, let alone what policy might achieve it. In this paper, we derive a closed-form expression for the optimal tail constant C, and we introduce *γ-Boost*, a new policy that achieves this optimal tail constant. Roughly speaking, γ-Boost operates similarly to FCFS, but it pretends that small jobs arrive earlier than their true arrival times. This significantly reduces the response time of small jobs without unduly delaying large jobs, improving upon FCFS's tail constant by up to 50% with only moderate job size variability, with even larger improvements for higher variability. While these results are for systems with full job size information, we also introduce and analyze a version of γ-Boost that works in settings with partial job size information, showing it too achieves significant gains over FCFS. Finally, we show via simulation that γ-Boost has excellent practical performance.

An Optimization Model for Flight Rescheduling from an Airport’s Centralized Perspective for Better Management of Demand and Capacity Utilization

Over-capacity flight scheduling by commercial airlines due to the surging demand in recent years creates congestion and significant delays at major airports. This attitude towards maximizing throughput calls for tactical flight rescheduling to comply with airports’ capacity limitations and distribute the peak hour demand over the course of a day. Such displacements of flights may cause significant problems and costs for airlines and some cancellations or missed connections for passengers. This paper presents an optimization model for flight rescheduling at a schedule-coordinated airport to minimize congestion and flight delays at peak hours. The optimization model is used to make better scheduling intervention decisions considering airport resource constraints and safety of operation. A simulation algorithm is also developed to replicate arrival and departure processes in such an airport. The simulation adheres to a first come first served (FCFS) discipline and enforces runway capacity constraints and minimum turnaround times. We compare the delays caused by an ad hoc FCFS operation with those of the optimization model. Computational results from a case study demonstrate that a reduction of 52.6% and 61% in total delay times for arrival and departure flights, respectively, can be achieved. The optimization model also facilitates the implementation of a collaborative decision-making system for better coordination of airport traffic flow management with commercial airlines.

Age of Information in NOMA-IoT Networks: A Temporal Queuing Model Perspective

The Internet of Things (IoT) with non-orthogonal multiple access (NOMA) has been anticipated to offer diverse real-time applications, wherein the crux is to guarantee the age of information (AoI) for dynamic traffic. However, the traffic temporal variation provokes the interdependence between queue status and interference, in which context the AoI performance remains to be further explored. In this paper, an analytical framework is established to characterize the AoI performance in NOMA-IoT networks with random Bernoulli and deterministic periodic arrivals. Particularly, a numerical algorithm is devised to obtain the queue service rate, and tractable expressions for AoI violation probability and average AoI under both the first-come first-served (FCFS) and the preemptive last-come first-served (LCFS-PR) service disciplines are derived. Simulations are conducted to validate the proposed analysis. The results unveil that LCFS-PR conduces to better AoI performance than FCFS, and yet the gain diverges for each device with different traffic arrival configurations. In addition, the result shows that with sporadic traffic arrival, the periodic pattern outperforms the Bernoulli pattern, whereas this advantage gradually diminishes with more frequent packet arrival.