First-come-first-served Research Articles

EMA-PRBDS: efficient multi-attribute packet rank based data scheduling in wireless sensor networks for real-time monitoring systems

ABSTRACTIn recent years, Wireless Sensor Networks (WSNs) are widely placed in real-time monitoring systems like environmental, structural, patient monitoring, etc. The major criterion for WSN includes energy efficiency and network lifetime. Scheduling is used as a large number of data packets focus on the same queue at the same time. Only limited data scheduling schemes have been implemented in WSN to enhance the performance. The existing First Come First Serve (FCFS) and Dynamic Multilevel Priority (DMP) have some technical challenges like delay, packet drop and high energy consumption due to starvation and deadlock. In this paper, we proposed a new algorithm to optimise and rank the incoming data packets based on multi-attributes decision-making methodology named as Packet Rank Based Data Scheduling (PRBDS), the algorithm selects packet priority, deadline, and size as the metrics to rank the incoming data packets. A simulation result shows when compared with existing scheduling, PRBDS not only provides less energy consumption, also significantly reduces the packet drop and increases the lifetime. Thus, the proposed algorithm is most suitable for real-time monitoring system since it combines data ranking method with scheduling algorithm to create accurate and reliable results to evaluate the incoming data packets.

A discrete-time single-server Poisson queueing game: Equilibria simulated by an agent-based model

This paper considers a discrete-time single-server queue with a single acceptance period for a Poissonian population of homogeneous customers. Customers are served on a first-come first-served (FCFS) basis, and their service times are independent and identically distributed with a general distribution. We assume that each customer chooses her/his arrival-time slot with the goal of minimizing her/his expected waiting time in competition with other customers. For this queueing game, we derive a symmetric (mixed-strategy) Nash equilibrium; that is, an equilibrium arrival-time distribution of homogeneous customers, where their expected waiting times are identical. We also propose an agent-based model, which simulates the dynamics of customers who try to minimize their waiting times for service. Through numerical experiments, we confirm that this agent-based model achieves, in steady state, an arrival-time distribution similar to the equilibrium arrival-time distribution analytically obtained.

Evolutionary based hybrid GA for solving multi-objective grid scheduling problem

The grid computing aims at bringing computing capacities together in a manner that can be used to find solutions for complicated problems of science. Conventional algorithms like first come first serve (FCFS), shortest job first (SJF) has been used for solving grid scheduling problem (GSP), but the increased complexity and job size led to the poor performance of these algorithms especially in the grid environment due to its dynamic nature. Previously, researchers have used a genetic algorithm (GA) to schedule jobs in the grid environment. In this paper, a multi-objective GSP is solved and optimized using the proposed algorithm. The proposed algorithm enhances the way the genetic algorithm performs and incorporate significant changes in the initialization step of the algorithm. The proposed algorithm uses SJF during its initialization step for producing the initial population solution. The proposed GA has three key features which are discussed in this paper: It executes jobs with minimum job completion time. It performs load balancing and improves resource utilization. Lastly, it supports scalability. The proposed algorithm is tested using a standard workload (given by Czech National Grid Infrastructure named Metacentrum) which can be a benchmark for further research. A performance comparison shows that the proposed algorithm has got better scheduling results than other scheduling algorithms.

An Improved Shuffled Frog-Leaping Algorithm for Solving the Dynamic and Continuous Berth Allocation Problem (DCBAP)

This research deals with the dynamic and continuous berth allocation problem (DCBAP) in which both arrived and incoming ships are considered and a quay is used as a continuous line to accommodate as many ships as possible at one time. The DCBAP is solved by a two-stage procedure. In the first stage a heuristic/metaheuristic is used to generate alternative ship placement sequences while in the second stage a specific heuristic is employed to place ships and resolve overlaps of ships for the development of a feasible solution. Different methods, including FCFS (First Come First Served), SFLA (Shuffled Frog-Leaping Algorithm), and ISFLA (Improved Shuffled Frog-Leaping Algorithm), were employed in the first stage for comparison. The experimental results show that the ISFLA outperforms the others in terms of solution quality, implying that the ISFLA has the potential to deal with the DCBAP in a container terminal.

Untethering the Queue based on Multi Channel Access (MCA) Technology at Hospital Radiology Section

Background: Information technology is developing rapidly over time, but there are many companies do not make use of it to support their operational activities. One example is the queue system in the Radiology Section of Sanglah Hospital, which operates the conventional queue system.Objective: The problem occurs in this queue system is inefficient waiting time because the patients must come early and wait in line in the hospital waiting rooms. Many patients thereafter decide going home first or doing other activities. Another problem is it triggers double costs of transportation. The purpose of this study is to improve patients comfort while waiting in queue by applying untethering the queue. Untethering the queue is a service that allows people to be in a queue even though they are not in the waiting room.Methods: The system applies the FCFS (First Come First Serve) algorithm to manage patients’ queue and integrates this queue system with Telegram and SMS gateway. It makes patients easier to take queue numbers and receive notification of health service time from the Radiology Section of Sanglah Hospital.Result: As a result of this study is an untethering the queue application which is web-based queue system integrated to Telegram and SMS gateway. The daily queue services in the Radiology Section of Hospital can be improved through this application.Conclusion: Patients can only come to the hospital when the queue numbers obtained nearly the health service time so that it leads to time efficient and comfort while waiting in queue.Keywords:First Come First Serve, Multi Channel Access, SMS, Telegram, Untethering the Queue

A model and algorithm for vessel scheduling through a two-way tidal channel

ABSTRACTEfficient vessel scheduling through a channel can improve the operational efficiency of ports. Additionally, many large vessels with deep drafts may have delays due to the tidal cycles when transiting tidal channels. Therefore, it is of great significance to study the vessel scheduling through a channel in which the depth is affected by the tide. This paper presents a model and an algorithm to solve the vessel scheduling problem through a two-way traffic channel in which the depth is affected by tides. The vessels passing through the tidal channel from the inbound and outbound directions are ordered under the condition that the channel depth considering tide meets the requirements of vessels’ drafts. The optimization objective is to minimize the total waiting time of all vessels to be scheduled. Next, the model and algorithm are verified using actual data from the north channel of the Yangtze River estuary. The results show that the scheduling efficiency is higher than other scheduling modes of first come first served (FCFS), random scheduling (RS), large draft vessel first (LDVF) and manual scheduling. Additionally, the proposed model and algorithm can be modified slightly and used for vessel scheduling through other two-way channels with tide-affected depth.

Minimization of Makespan Using FCFS and Gupta Method Comparison in Machine Spare Part Industry

Companies that produce spare parts for manufacturing industry machines are experiencing rapid development. The company produces production activities based on job orders where the process uses FCFS (First Come First Served) rules. Company scheduling often experiences delays especially on products have the same flow. The study was conducted to find the optimal sequence of scheduling with makespan minimization criteria using the Gupta Method and FCFS Method. The FCFS method used by the company produces makespan of 12.160,21 minutes = 202,67 hours. The recommendation method has a better level of performance compared to the company shown in efficiency index (EI) and relative error (RE). The EI value of the Gupta method is 1.14 which is indicates that the Gupta method has a good performance compared to the FCFS method. While the Gupta method RE value is 13,76%. The conclusion is Gupta method produces the minimum makespan value with a reduction of makespan of 178,15 hours. The Gupta method is a better method than FCFS method, which is shorter makespan, tardiness and flow time.

Allocation of Cloudlets using Solution of Job Assignment Problem

Resource allocation policies play a key role in determining the performance of cloud. Service providers in cloud computing have to provide services to many users simultaneously. So the job of allocating cloudlets to appropriate virtual machines is becoming one of the challenging issues of cloud computing. Many algorithms have been proposed to allocate cloudlets to the virtual machines. Here in our paper, we have represented cloudlet allocation problem as job assignment problem and we have proposed Hungarian algorithm based solution for allocating cloudlets to virtual machines. The main objective is to minimize total execution time of cloudlets. Proposed algorithm is implemented in Cloudsim-3.03 simulator. We have done comparative analysis of the simulation results of proposed algorithm with the existing First Come First Serve (FCFS) scheduling policy and Min-Min scheduling algorithm. Proposed algorithm performs better than the above mentioned algorithms in terms of total execution time and makespan time (finishing time of last cloudlet)

Research on the Problem of Sorting Parallel Runway Aircraft in Terminal Area Based on Genetic Algorithm

Serious air traffic congestion often occurs in busy airports and terminal areas, which seriously interferes with the normal operation of flights, resulting in a large number of flight delays, causing huge economic losses to airlines, and also the affecting the safe operation of flights in the terminal area. This study takes the two-runway airport as the research object, establishes the parallel runway arrival flight sequencing model and designs the double-encoded genetic algorithm, uses a pair of chromosomes to determine the runway allocation and landing sequence number of the arriving flight, and arranges the flight landing sequence and the runway. The simulation experiments were carried out on 10 flights that were about to land at a certain time in the airport, the experimental results show that the algorithm achieves flight sorting and runway allocation optimization, compared with the current FCFS (First Come First Served) algorithm, it can effectively reduce flight delay time and improve the utilization efficiency of the airport terminal area.

Monitoring the Value of Water Quality and Condition Parameters Using the Open Sensor Aquarium

One technology that can be applied in hydroponic farming systems is sensing. Sensors used in this research are pH, electrical conductivity, water level, and temperature sensor. The First Come First Served (FCFS) algorithm is used to help scheduling data plotting. In order to be serviced by the processor, data entities in the queue must wait until all previous processes have been completed. The final test results show that the average success of the system succeeded in activating the actuator to operate the feeding was 92.5% and operating the heater to warm the water temperature was 83.33% for the 720-hour testing period. All sensing data also successfully plotted in graphical form for each of the results of sensing the pH value, water temperature, and electrical conductivity in water.

Transshipment scheduling at a single station with release date and inventory constraints

ABSTRACTWe study a loading and unloading scheduling problem in a single station transshipment terminal, in which inventory and release date constraints are subjected and the objective is to minimize the makespan. We propose an integer linear programming model; as well as a heuristic algorithm and two exact solution approaches: a dynamic programming algorithm and a brand-and-bound algorithm. Finally, performances of the developed algorithms are compared and analyzed using randomly generated test instances. Regarding the computational results, it is revealed that the simple developed heuristic algorithm is very fast and efficient. Also, the branch-and-bound algorithm outperforms the dynamic programming algorithm.Abbreviations: Imperialist competitive algorithm (ICA); Variable neighborhood search (VNS); Genetic algorithm (GA): Adaptive tabu search (ATS); First-come–first served(FCFS); Branch-and-bound (B&B)

Comparative Analysis of FCFS, SJN & RR Job Scheduling Algorithms

One of the primary roles of the operating system is job scheduling. Oftentimes, what makes the difference between the performance of one operating system over the other could be the underlying implementation of its job scheduling algorithm. This paper therefore examines, under identical conditions and parameters, the comparative performances of First Come First Serve (FCFS), Shortest Job Next (SJN) and Round Robin (RR) scheduling algorithms. Simulation results presented in this paper serve to stimulate further research into the subject area.

Profit analysis of a warm standby non-identical unit system with single server performing in normal/abnormal environment

A system/unit is called working in normal environment if it is operating within prescribed conditions set by the manufacturer, otherwise the system is called working in abnormal environment. For example; a hydraulic machine having capacity uplifting the weight of 500 tons exceeds its capacity is termed as working in abnormal environment conditions. In this paper, profit analysis of a warm standby non-identical unit system with single server subject normal/abnormal environment conditions has been discussed. There is a single server who is allowed to do job in normal weather condition only and while performing its job it adopts FCFS (first come first serve) policy. The system model consists of two non-identical units—one is operative and the other kept as warm standby. The main unit may fail directly from normal mode, while failure of warm standby unit takes place owing to remaining unused for a longer period of time. The time taken for repair activity by the server follows negative exponential distribution. The expressions of various reliability measures are analyzed in steady state using semi-Markov process and regenerative point technique. Also, take the arbitrary values for the parameters (i.e., λ, μ, ϕ and θ) to delineate the behavior of some important performance measures to check the efficacy of the system model under such situations shown in the graphs.

Special Issue on Recent Trends and Future of Fog and Edge Computing, Services and Enabling Technologies

Special Issue on Recent Trends and Future of Fog and Edge Computing, Services and Enabling Technologies

Static Load Balancing Technique for Geographically Partitioned Public Cloud

Large number of users are shifting to the cloud system for their different kind of needs. Hence the number of applications on public cloud are increasing day by day. Handling public cloud is becoming unmanageable in comparison to other counterparts. Though fog technology has reduced the load on centralized cloud resources to a remarkable extent, still load handled at cloud end is significantly high. Geographic partitioning of public cloud can resolve these issues by adding manageability and efficiency in this situation. Dividing public cloud in smaller partitions opens ways to manage resources and requests in a better way. But, partitioned clouds introduce different ends for submission and operations of tasks and virtual machines. We have tried to handle all these complexities in this paper. Proposed work is focused upon load balancing in the partitioned public cloud by combining centralized and decentralized approaches, assuming the presence of fog layer. A load balancer entity is used for decentralized load balancing at partitions and a controller entity is used for centralized level to balance the overall load at various partitions. In the proposed approach, it is assumed that jobs are segregated first. All the jobs which can be handled locally by fog resources are not forwarded to the cloud layer directly. Those are processed locally by decentralized fog resources. Selection of an appropriate Virtual Machine (VM) for filtered set of job, which are forwarded to cloud environment, is done in three steps. Initially, selecting the partition with a maximum available capacity of resources. Then finding the appropriate node with maximum available resources, within a selected partition. And finally, the VM with minimum execution time for a task is chosen. Results are compared with the results produced in this work with First Come First Serve (FCFS) and Shortest Job First (SJF) algorithms, implemented in same setup i.e. partitioned cloud. This paper compares the Waiting Time, Finish Time and Actual Run Time of tasks using these algorithms. After initial experimentation, it is found that in most of the cases, while comparing above parameters, the proposed approach is producing better results than FCFS algorithm. But results produced by SJF algorithm produce better results. To reduce the number of unhandled jobs, a new load state is introduced which checks load beyond conventional load states. Major objective of this approach is to reduce the need of runtime virtual machine migration and to reduce the wastage of resources, which may be occurring due to predefined values of threshold. The implementation is done using CloudSim.

Placement Strategy for Intercommunicating Tasks of an Elastic Request in Fog-Cloud Environment

Cloud computing hosts large number of modern day applications using the virtualization concept. However, end-to-end network latencies are detrimental to the performance of IoT (Internet of Things) applications like video surveillance and health monitoring. Although edge/fog computing alleviates the latency concerns to some extent, it is still not suitable for applications having intercommunicating tasks. Further, these applications can be elastic in nature and demand more tasks during their life-time. To address this gap, in this paper a network aware co-allocation strategy for the tasks of an individual applications is proposed. After modelling the problem using bin packing approach with additional constraints, the authors propose a novel heuristic IcAPER,(Inter-communication Aware Placement for Elastic Requests) algorithm. The proposed algorithm uses the network neighborhood machine for placement, once current resource is fully utilized by the application. Using CloudsimPlus simulator the performance IcAPER algorithm is compared with First Come First Serve (FCFS), Random and First Fit Decreasing (FFD) algorithms for the parameters (a) resource utilization (b) resource fragmentation and (c) Number of requests having intercommunicating tasks placed on to same PM. Extensive simulation results shows IcAPER maps 34% more tasks on to the same PM and also increase the resource utilization by 13% while decreasing the resource fragmentation by 37.8% when compared to other algorithms in our consideration.

An Improved Task Scheduling Mechanism Using Multi-Criteria Decision Making in Cloud Computing

In this work, the authors propose Multi-criteria Decision-making to schedule deadline based tasks in cloud computing. The existing backfilling task scheduling algorithm could not handle similar tasks for scheduling. In backfilling algorithm, tasks are backfilled to provide ideal resources to schedule other deadline sensitive tasks. However, the task to be backfilled is selected on first come, first serve (FCFS) basis from scheduling queue. The scheduling performances require to be improved when, there are similar tasks. In this proposed work, the authors propose to implement MCDM technique, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to improve the performance of the backfilling algorithm through scheduling deadline sensitive tasks in cloud computing. It resolves the conflicts among the similar tasks that is used as a decision support system. The work is simulated with synthetic data sets based on slack values of the tasks. The performance results affirm the task completion and reduction in task rejection compared to the existing backfilling algorithm.

Metaheuristic Algorithms for Independent Task Scheduling in Symmetric and Asymmetric Cloud Computing Environment

Cloud Computing (CC) is a recent technology in the Information and Communication Technology (ICT) field. It provides an on-demand access to the shared pool of resources via virtualization. Large enterprises move toward CC due to its flexibility and scalability driven from its elastic pay-per-use model. To provide ensured efficient performance to users, tasks should be efficiently mapped to available resources. Therefore, Task Scheduling (TS) is significant issue in the CC technology. TS is a NP-complete optimization problem, so a deep investigation of different metaheuristic and heuristic TS algorithms is presented here. Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) as metaheuristic algorithms are implemented and their performance have been compared to heuristic techniques (First Come First Serve (FCFS) and Shortest Job First (SJF)) on symmetric and asymmetric environment. The cloud service providers and users have different performance requirements. Six performance metrics including makespan, flow time, response time, resource utilization, throughput time and degree of imbalance have been measured. For asymmetric environment, real environment, metaheuristic TS algorithms surpassed the heuristic methods.

Discrete-Event Systems Modeling and the Model Predictive Allocation Algorithm for Integrated Berth and Quay Crane Allocation

In this paper, we study the problem of integrated berth and quay crane allocation (I-BCAP) in general seaport container terminals and propose the model predictive allocation (MPA) algorithm and preconditioning methods for solving the I-BCAP. First, we propose a dynamical modeling framework based on discrete-event systems (DESs), which describes the operation of a berthing process with multiple discrete berthing positions and multiple quay cranes. Second, based on the discrete-event model, we propose the MPA algorithm for solving the I-BCAP using the model predictive control (MPC) principle with a rolling event horizon. The validation and performance evaluation of the proposed modeling framework and allocation method are done using: 1) extensive Monte Carlo simulations with realistically generated datasets; 2) real dataset from a container terminal in Tanjung Priuk port, located in Jakarta, Indonesia; and 3) real life field experiment at the aforementioned container terminal. The numerical simulation results show that our proposed MPA algorithm can improve the efficiency of the process where the total handling and waiting cost is reduced by approximately 6%–9% in comparison with the commonly adapted method of first-come first-served (FCFS) (for the berthing process) combined with the density-based quay cranes allocation (DBQA) strategy. Moreover, the proposed method outperforms the state-of-the-art hybrid particle swarm optimization (HPSO)-based and genetic algorithm (GA)-based method proposed in the recent literature. The real life field experiment shows an improvement of about 6% in comparison with the existing allocation method used in the terminal.

A new approach for performance assessment of parallel and non-bottleneck machines in a dynamic job shop environment

PurposeThe current study aims to propose a new analytical approach by considering energy consumption (EC), maximum tardiness and completion time as the primary objective functions to assess the performance of parallel, non-bottleneck and multitasking machines operating in dynamic job shops.Design/methodology/approachAn analytical and iterative method is presented to optimize a novel dynamic job shop under technical constraints. The machine’s performance is analyzed by considering the setup energy. An optimization model from initial processing until scheduling and planning is proposed, and data sets consisting of design parameters are fed into the model.FindingsSignificant variations of EC and tardiness are observed. The minimum EC was calculated to be 141.5 hp.s when the defined decision variables were constantly increasing. Analysis of the optimum completion time has shown that among all studied methods, first come first served (FCFS), earliest due date (EDD) and shortest processing time (SPT) have resulted in the least completion time with a value of 20 s.Originality/valueConsiderable amount of energy can be dissipated when parallel, non-bottleneck and multitasking machines operate in lower-power modes. Additionally, in a dynamic job shop, adjusting the trend and arrangement of decision variables plays a crucial role in enhancing the system’s reliability. Such issues have never caught the attention of scientists for addressing the aforementioned problems. Therefore, with these underlying goals, this paper presents a new approach for evaluating and optimizing the system’s performance, considering different objective functions and technical constraints.