Journal articles on the topic 'Shortest Job First'
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Santika, Monica, and Seng Hansun. "Implementasi Algoritma Shortest Job First dan Round Robin pada Sistem Penjadwalan Pengiriman Barang." Jurnal ULTIMATICS 6, no. 2 (November 1, 2014): 94–99. http://dx.doi.org/10.31937/ti.v6i2.336.
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Delivery of goods will normally be conducted in accordance with the queuing time of booking. Sometimes, it is inefficient and results in a delay on the delivery of goods. Therefore, to make a better scheduling system, the Shortest Job First and Round Robin algorithms been implemented. From the results of experiments, Shortest Job First and Round Robin algorithms successfully applied to the scheduling delivery application. Shortest Job First algorithm is better than Round Robin scheduling in the case of delivery of goods, because the algorithm execution process which takes small time will be moved before the process which takes much time, so it needs smaller time than using Round Robin algorithm.
Index Terms - Round Robin, Scheduling, Shipping, Shortest Job First
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Gromoll, H. Christian, and Martin Keutel. "Invariance of fluid limits for the shortest remaining processing time and shortest job first policies." Queueing Systems 70, no. 2 (November 9, 2011): 145–64. http://dx.doi.org/10.1007/s11134-011-9267-5.
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Putra, Tri Dharma. "Analysis of Preemptive Shortest Job First (SJF) Algorithm in CPU Scheduling." IJARCCE 9, no. 4 (April 30, 2020): 41–45. http://dx.doi.org/10.17148/ijarcce.2020.9408.
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Pon Pushpa, S. Ewins, and Manamalli Devasikamani. "Schedulability Analysis for Rate Monotonic Algorithm-Shortest Job First Using UML-RT." Modelling and Simulation in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/206364.
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System modelling with a unified modelling language (UML) is an active research area for developing real-time system development. UML is widely used modelling language in software engineering community, to specify the requirement, and analyse the target system successfully. UML can be used to provide multiple views of the system under design with the help of a variety of structural and behavioural diagrams at an early stage. UML-RT (unified modelling language-real time) is a language used to build an unambiguous executable specification of a real-time system based on UML concepts. This paper presents a unified modeling approach for a newly proposed rate monotonic scheduling algorithm-shortest job first (RMA-SJF) for partitioned, semipartitioned and global scheduling strategies in multiprocessor architecture using UML-RT for different system loads. As a technical contribution, effective processor utilization of individual processors and success ratio are analyzed for various scheduling principles and compared with EDF and D_EDF to validate our proposal.
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Hashim Yosuf, Raham, Rania A. Mokhtar, Rashid A. Saeed, Hesham Alhumyani, and S. Abdel-Khalek. "Scheduling Algorithm for Grid Computing Using Shortest Job First with Time Quantum." Intelligent Automation & Soft Computing 31, no. 1 (2022): 581–90. http://dx.doi.org/10.32604/iasc.2022.019928.
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Siddesh, G. M., and K. G. Srinisas. "An Adaptive Scheduler Framework for Complex Workflow Jobs on Grid Systems." International Journal of Distributed Systems and Technologies 3, no. 4 (October 2012): 63–79. http://dx.doi.org/10.4018/jdst.2012100106.
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Grid Computing provides sharing of geographically distributed resources among large scale complex applications. Due to dynamic nature of resources in grid, there is a need of highly efficient job scheduling and resource management policies in grid. A novel Grid Resource Scheduler (GRS) is proposed to effectively utilize the available resources in Grid. Proposed GRS contributes, an optimal job scheduling algorithm on Job Rank-Backfilling policy and a resource matching algorithm based on ranking of resources with best fit allocation model. Performance of GRS is measured by considering a web based BLAST algorithm – a bioinformatics application. GRS aims in reducing; Makespan of the job workflow, execution time of varied size jobs, response time of the submitted jobs and overhead of using the system. It also considers improving the utilization factor and throughput of the available heterogeneous resources in grid. The experimental results prove that proposed grid scheduler framework performs better when evaluated against widely used First Come First Serve (FCFS), Shortest Job First (SJF) and Minimum Time to Due Date (MTTD) scheduling strategies.
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Somasundaram, K., and S. Radhakrishnan. "Task Resource Allocation in Grid using Swift Scheduler." International Journal of Computers Communications & Control 4, no. 2 (June 1, 2009): 158. http://dx.doi.org/10.15837/ijccc.2009.2.2423.
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In nature, Grid computing is the combination of parallel and distributed computing where running computationally intensive applications like sequence alignment, weather forecasting, etc are needed a proficient scheduler to solve the problems awfully fast. Most of the Grid tasks are scheduled based on the First come first served (FCFS) or FCFS with advanced reservation, Shortest Job First (SJF) and etc. But these traditional algorithms seize more computational time due to soar waiting time of jobs in job queue. In Grid scheduling algorithm, the resources selection is NPcomplete. To triumph over the above problem, we proposed a new dynamic scheduling algorithm which is the combination of heuristic search algorithm and traditional SJF algorithm called swift scheduler. The proposed algorithm takes care of Job’s memory and CPU requirements along with the priority of jobs and resources. Our experimental results shows that our scheduler reduces the average waiting time in the job queue and reduces the over all computational time.
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Chang, Cheng-Shang, Arie Hordijk, Rhonda Righter, and Gideon Weiss. "The Stochastic Optimality of SEPT in Parallel Machine Scheduling." Probability in the Engineering and Informational Sciences 8, no. 2 (April 1994): 179–88. http://dx.doi.org/10.1017/s0269964800003326.
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We consider preemptive scheduling on parallel machines where processing times of jobs are i.i.d. but jobs may already have received distinct amounts of service. We show that when processing times are increasing in likelihood ratio, SEPT (shortest expected [remaining] processing time first) stochastically minimizes any increasing and Schur-concave function of the job completion times. The same result holds when processing times are exponential with possibly different means.
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Pon Pushpa, S. Ewins, and Manamalli Devasigamani. "Utilization Bound Scheduling Analysis for Nonpreemptive Uniprocessor Architecture Using UML-RT." Modelling and Simulation in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/705929.
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The key for adopting the utilization-based schedulability test is to derive the utilization bound. Given the computation times, this paper proposes two utilization bound algorithms to derive interrelease times for nonpreemptive periodic tasks, using a new priority scheme, “Rate Monotonic Algorithm-Shortest Job First.” The obtained task set possesses the advantage of Rate Monotonic Algorithm and Shortest Job First priority scheme. Further, the task set is tested for schedulability, by first deriving a general schedulability condition from “problem window” analysis and, a necessary and sufficient schedulability condition for a task to be scheduled, at any release time are also derived. As a technical contribution, success ratio and effective processor utilization are analyzed for our proposed utilization bound algorithms on a uniprocessor architecture modeled using UML-RT.
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Seth, Sonam, and Nipur Singh. "Dynamic heterogeneous shortest job first (DHSJF): a task scheduling approach for heterogeneous cloud computing systems." International Journal of Information Technology 11, no. 4 (April 7, 2018): 653–57. http://dx.doi.org/10.1007/s41870-018-0156-6.
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Argon, Nilay Tanik, Serhan Ziya, and Rhonda Righter. "SCHEDULING IMPATIENT JOBS IN A CLEARING SYSTEM WITH INSIGHTS ON PATIENT TRIAGE IN MASS CASUALTY INCIDENTS." Probability in the Engineering and Informational Sciences 22, no. 3 (May 27, 2008): 301–32. http://dx.doi.org/10.1017/s0269964808000272.
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Motivated by the patient triage problem in emergency response, we consider a single-server clearing system in which jobs might abandon the system if they are not taken into service within their “lifetime.” In this system, jobs are characterized by their lifetime and service time distributions. Our objective is to dynamically determine the optimal or near-optimal order of service for jobs so as to minimize the total number of abandonments. We first show that if the jobs can be ordered in such a way that the job with the shortest lifetime (in the sense of hazard rate ordering) also has the shortest service time (in the sense of likelihood ratio ordering), then the optimal policy gives the highest priority to this “time-critical” job independently of the system state. For the case in which the jobs with shorter lifetimes have longer service times, we observed that the optimal policy generally has a complex structure that might depend on the type and number of jobs available. For this case, we provide partial characterizations of the optimal policy and obtain sufficient conditions under which a state-independent policy is optimal. Furthermore, we develop two state-dependent heuristic policies, and by means of a numerical study, we show that these heuristics perform well, especially when jobs abandon the system at a relatively faster rate when compared to service rates. Based on our analytical and numerical results, we develop several insights on patient triage in the immediate aftermath of a mass casualty event. For example, we conclude that in a worst-case scenario, where medical resources are overwhelmed with a large number of casualties who need immediate attention, it is crucial to implement state-dependent policies such as the heuristic policies proposed in this article.
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Phani Praveen S. and K. Thirupathi Rao. "Client-Awareness Resource Allotment and Job Scheduling in Heterogeneous Cloud by Using Social Group Optimization." International Journal of Natural Computing Research 7, no. 1 (January 2018): 15–31. http://dx.doi.org/10.4018/ijncr.2018010102.
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Often cloud providers and cloud clients illustrate several constraints and thus allocation of resources in a heterogeneous cloud is a difficult job. As the traffic flow is quite subjective and Client necessities and applications size vary regularly, the major challenge and concern is to map the external job requests to available virtual machines. To reduce the gap among regularly altering client requirements and existing resources, Client-Awareness Allocation of Resources and Scheduling of jobs in cloud by using social group optimization (SGOCARAJS) is proposed. This algorithm is mainly split into two phases namely allocation of resources using SGO and shortest job first scheduling. The main aim is to map the jobs to virtual machines of cloud group to attain higher client satisfaction and lowest makespan time. Experiments are conducted on datasets and results are compared with present scheduling techniques. This model proved that this algorithm outrun the available algorithms based on concerned metrics.
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Sosimi, Adeyanju, Folorunso Oladimeji Ogunwolu, and Taoreed Adegbola. "A Makespan Optimization Scheme for NP-Hard Gari Processing Job Scheduling Using Improved Genetic Algorithm." Journal of Industrial Engineering 2014 (April 6, 2014): 1–10. http://dx.doi.org/10.1155/2014/628640.
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An optimization scheme for minimizing makespan of Gari processing jobs using improved initial population Genetic Algorithm (GA) is proposed. GA with initial population improved by using job sequencing and dispatching rules of First Come First Served (FCFS), Shortest Processing Time (SPT), Longest Processing Time (LPT), and Modified Johnson’s Algorithm for m-machines in order to obtain better schedules than is affordable by GA with freely generated initial population and by individual traditional sequencing and dispatching rules was used. The traditional GA crossover and mutation operators as well as a custom-made remedial operator were used together with a hybrid of elitism and roulette wheel algorithms in the selection process based on job completion times. A test problem of 20 jobs with specified job processing and arrival times was simulated through the integral 5-process Gari production routine using the sequencing and dispatching rules, GA with freely generated initial population, and the improved GA. Comparisons based on performance measures such as optimal makespan, mean makespan, execution time, and solution improvement rate established the superiority of the improved initial population GA over the traditional sequencing and dispatching rules and freely generated initial population GA.
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Et. al., Dr Nageswara Rao M,. "MANUFACTURING INDUSTRY JOB SHOP FLOOR DECISIONS THROUGH HYBRID GENETIC ALGORITHM." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (March 21, 2021): 62–76. http://dx.doi.org/10.17762/itii.v9i2.305.
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This article addresses flexible manufacturing system (FMS) Performance is likely to improve with employment of various resources efficiently. Initially simultaneous scheduling problems are solved by means of priority rules like first come first serve (FCFS), shortest processing time (SPT) and longest processing time (LPT) to find out the operational completion time for 120 problems. Later gene rearrangement genetic algorithm (HGA) is implemented for same set of problems with makespan as objective and the results are compared with the results of priority rules. The results are performed well by using HGA. The same HGA is used to find the finest optimal sequence that minimize the operational completion time.
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Kumar, Sarvesh, Gaurav Kumar, Komal Jain, and Aditi Jain. "An approach to reduce turn around timeand waiting timeby the selection of round robin and shortest job first algorithm." International Journal of Engineering & Technology 7, no. 2.8 (March 19, 2018): 667. http://dx.doi.org/10.14419/ijet.v7i2.8.10553.
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In this research,a study on operating system tells about its working, how it helps as interface between user software and system hardware .To implement this, different scheduling is used to provide multiple processing in a hardware. There are different levels of scheduler applied in different levels of process from ready queue to termination. This paper focuses on the average amount of waiting time and amount of turnaround time of processes. The proposed algorithm purely defines less waiting time and turnaround time as compared to the round robin scheduling and shortest job first scheduling algorithm.
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Jafari Navimipour, Nima, Amir Masoud Rahmani, Ahmad Habibizad Navin, and Mehdi Hosseinzadeh. "Job scheduling in the Expert Cloud based on genetic algorithms." Kybernetes 43, no. 8 (August 26, 2014): 1262–75. http://dx.doi.org/10.1108/k-02-2013-0018.
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Purpose – Expert Cloud as a new class of Cloud computing systems enables its users to request the skill, knowledge and expertise of people by employing internet infrastructures and Cloud computing concepts without any information of their location. Job scheduling is one of the most important issue in Expert Cloud and impacts on its efficiency and customer satisfaction. The purpose of this paper is to propose an applicable method based on genetic algorithm for job scheduling in Expert Cloud. Design/methodology/approach – Because of the nature of the scheduling issue as a NP-Hard problem and the success of genetic algorithm in optimization and NP-Hard problems, the authors used a genetic algorithm to schedule the jobs on human resources in Expert Cloud. In this method, chromosome or candidate solutions are represented by a vector; fitness function is calculated based on response time; one point crossover and swap mutation are also used. Findings – The results indicate that the proposed method can schedule the received jobs in appropriate time with high accuracy in comparison to common methods (First Come First Served, Shortest Process Next and Highest Response Ratio Next). Also the proposed method has better performance in term of total execution time, service+wait time, failure rate and Human Resource utilization rate in comparison to common methods. Originality/value – In this paper the job scheduling issue in Expert Cloud is pointed out and the approach to resolve the problem is applied into a practical example.
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Hassan, M. E., and A. Yousif. "Cloud Job Scheduling with Ions Motion Optimization Algorithm." Engineering, Technology & Applied Science Research 10, no. 2 (April 4, 2020): 5459–65. http://dx.doi.org/10.48084/etasr.3408.
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Cloud computing technology success comes from its manner of delivering information technology services, how they are designed, propagated, maintained and scaled. Job Scheduling on cloud computing is a crucial research area and is known to be an NP-complete problem. Scheduling refers to assigning user requests to underlying resources effectively. This paper proposes a new Job Scheduling mechanism for cloud computing environment. The proposed mechanism is based on the Ions Motion Optimization (IMO) algorithm. IMO has two phases, liquid, and crystal. These two phases balance the algorithm behavior between convergence and local optima avoidance. To evaluate the proposed mechanism, a simulation with different scenarios using the CloudSim simulator is conducted. The performance of the proposed algorithm is compared with two metaheuristic algorithms known as Cat Swarm Optimization (CSO) and Glowworm Swarm Optimization (GSO). Furthermore, the proposed IMO mechanism is compared with First Come First Served and random solution. The experimental results demonstrated that the proposed mechanism outperformed both CSO and GSO and produced the shortest execution time in all experimental scenarios.
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Raheja, Supriya, Reena Dhadich, and Smita Rajpal. "Designing of 2-Stage CPU Scheduler Using Vague Logic." Advances in Fuzzy Systems 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/841976.
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In operating system the CPU scheduler is designed in such a way that all the resources are fully utilized. With static priority scheduling the scheduler ensures that CPU time will be assigned according to the highest priority but ignores other factors; hence it affects the performance. To improve the performance, we propose a new 2-stage vague logic based scheduler. In first stage, scheduler handles the uncertainty of tasks using the proposed vague inference system (VIS). In second stage, scheduler uses a vague oriented priority scheduling (VOPS) algorithm for selection of next process. The goal of this work is to handle the uncertainty as well as to optimize both the average and the amount of variation with respect to performance matrices average waiting time, average turnaround time, and average normalized turnaround time. A simulation using MATLAB is also conducted to evaluate the performance. Simulation results show that the proposed scheduler using VOPS algorithm is better than the scheduler with traditional priority scheduling algorithm. Results are based on the dual concept of fuzzy theory and its generalization, vague theory. Additionally, this work comprises the evaluation of VOPS and shortest job first algorithm. The outcome of proposed VOPS algorithm is much closer to the result obtained by traditional shortest job first.
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Vasantam, Thirupathaiah, and Ravi R. Mazumdar. "Sensitivity of mean-field fluctuations in Erlang loss models with randomized routing." Journal of Applied Probability 58, no. 2 (June 2021): 428–48. http://dx.doi.org/10.1017/jpr.2020.99.
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AbstractIn this paper we study a large system of N servers, each with capacity to process at most C simultaneous jobs; an incoming job is routed to a server if it has the lowest occupancy amongst d (out of N) randomly selected servers. A job that is routed to a server with no vacancy is assumed to be blocked and lost. Such randomized policies are referred to JSQ(d) (Join the Shortest Queue out of d) policies. Under the assumption that jobs arrive according to a Poisson process with rate $N\lambda^{(N)}$ where $\lambda^{(N)}=\sigma-\frac{\beta}{\sqrt{N}\,}$ , $\sigma\in\mathbb{R}_+$ and $\beta\in\mathbb{R}$ , we establish functional central limit theorems for the fluctuation process in both the transient and stationary regimes when service time distributions are exponential. In particular, we show that the limit is an Ornstein–Uhlenbeck process whose mean and variance depend on the mean field of the considered model. Using this, we obtain approximations to the blocking probabilities for large N, where we can precisely estimate the accuracy of first-order approximations.
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Faccio, Maurizio, Mojtaba Nedaei, and Francesco Pilati. "A Comparative Analysis of Job Scheduling for Optimum Performance of Parallel Machines by Considering the Energy Consumption." European Journal of Engineering Research and Science 3, no. 9 (September 18, 2018): 6. http://dx.doi.org/10.24018/ejers.2018.3.9.841.
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In this study, a comparative analysis of scheduling of machines and jobs was conducted by proposing a method, which aims to optimize the performance of the job-shop environment. A wide range of objective functions including make-span, maximum tardiness, total flow time, total tardiness, total weighted flow time, and total weighted tardiness, and energy consumption. The decision variables of a manufacturing company including the job’s weight, stating and completion time, due dates, releasing date, and processing time were considered as inputs of the optimization model. Then, subject to the defined technical constraints of the system, a comparative analysis on the basis of eight scheduling methods was conducted to assess the performance indicators of the job-shop system. Results of the analysis revealed variations of different objective functions based on the proposed methods. The optimum make-span varied from minimum of 210 days estimated by the Earliest Due Date (EDD) method, up to 222 days estimated by the Critical Ratio (CR) method. Further recommendations were also made for optimum values of the involved decision variables in the considered job-shop environment. Results revealed that the maximum tardiness was best optimized using the EDD method, whilst the Shortest Processing Time (SPT) led to the best optimum value for the total flow time. Analysis of the energy evaluation of the machines showed that the optimum overall energy consumption has been observed with the value of 7,934 kWh for the first operating machine under the effect of the Minimum Slack method, whilst the optimum energy consumption for the second machine was observed with the value of 7,968 kWh using the First Come First Served (FCFS). Last stage of study recommended the optimum planning schedules and resource allocations of the jobs in the machines considering each scheduling method during the operation of the designed job-shop.
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Huang, Chueng-Chiu, and Gideon Weiss. "Preemptive Scheduling of Stochastic Jobs with a Two-Stage Processing Time Distribution on M + 1 Parallel Machines." Probability in the Engineering and Informational Sciences 6, no. 2 (April 1992): 171–91. http://dx.doi.org/10.1017/s0269964800002436.
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We analyze the optimal preemptive sequencing of n jobs on M + 1 parallel identical machines to minimize expected total flowtime. The running times of the jobs are independent samples from the distribution Pr(X = H) = p, Pr(X = H + T) = 1 − p, where H, T are random variables of general distribution. Preemption of a job is allowed when H is completed. This problem does not have a simple optimal solution. We show that the scheme of shortest expected remaining processing time first (SERPT) is close to optimal in two senses. The expected flowtime under SERPT and under the optimal policy differ by no more than a constant, independent of the number of jobs, and the expected number of optimal decisions that are not according to SERPT is bounded by a constant, independent of the number of jobs.
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Kalyana Sundaram, N., and Dr S.P.Rajagopalan. "Experimental Evaluation of Energy Saving Task Scheduling (ESTS) in Cloud Computing." International Journal of Engineering & Technology 7, no. 3.34 (September 1, 2018): 107. http://dx.doi.org/10.14419/ijet.v7i3.34.18783.
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Cloud Computing provides services, on-demand access, infrastructure, storage of data and application. It possesses the reliability, availability and the scalability. One of the issues in cloud computing is Energy Saving. In this paper, the proposed work is Energy Saving Task Scheduling (ESTS) methodology. The aim of this methodology is to show the performance comparison of all the task scheduling types. Task scheduling or Job scheduling is referred to as policies that control the work order to be performed by a computer system. Types of Task Scheduling are Shortest Job First (SJF), First Come First Serve (FCFS), Round Robin (RR) and Priority Scheduling. In each type of schedule, the processes used by the parameters were calculated. Finally, the performance comparison is made in scheduling algorithms and shows better results. This method is implemented in net beans toolkit.
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Setiawan, Ari, Luthfan Qashmal, Rachmawati Wangsaputra, Yatna Yuwana Martawirya, and Abdul Hakim Halim. "An Object-Oriented Modelling of Production Scheduling for Flexible Manufacturing System." Applied Mechanics and Materials 842 (June 2016): 345–54. http://dx.doi.org/10.4028/www.scientific.net/amm.842.345.
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This paper presents an object-oriented modelling approach to production scheduling for FMS. The purpose of this study is to prepare a simulation tool to try a method of production scheduling that allocates jobs to the machines and cutting tools in an FMS. This model is developed by using Pharo as the language software for object-oriented programming through UML system design. This model consists of three types of classes. The first type is the Equipment-class, which related to the physical equipment in the FMS, for example machining-centers, cutting tools, pallet stocker, stacker crane. The second type is the Product-class that related to the information how to process the product. This class has sub-class such as process plan, machining operation and required cutting tools. The third class is the Production Planning and Controlling (PPC) class, which related to create the schedule to equipment. The PPC-class has sub-classes such as the daily order or job, job sequence, job allocation on machine and scheduling methods. A numerical example is provided in this paper to show that the object-oriented model for FMS and production scheduling is verified. The FMS consist of four identical machining centers and eight jobs. Each job has a process plan that requires some specific cutting tool types. The method for production scheduling in this model is Shortest Processing Time (SPT). The production scheduling performance is measured in this model.
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Raheja, Supriya. "An Intuitionistic Fuzzy Based Novel Approach to CPU Scheduler." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 4 (May 7, 2020): 316–28. http://dx.doi.org/10.2174/1573405614666180903120708.
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Background: The extension of CPU schedulers with fuzzy has been ascertained better because of its unique capability of handling imprecise information. Though, other generalized forms of fuzzy can be used which can further extend the performance of the scheduler. Objectives: This paper introduces a novel approach to design an intuitionistic fuzzy inference system for CPU scheduler. Methods: The proposed inference system is implemented with a priority scheduler. The proposed scheduler has the ability to dynamically handle the impreciseness of both priority and estimated execution time. It also makes the system adaptive based on the continuous feedback. The proposed scheduler is also capable enough to schedule the tasks according to dynamically generated priority. To demonstrate the performance of proposed scheduler, a simulation environment has been implemented and the performance of proposed scheduler is compared with the other three baseline schedulers (conventional priority scheduler, fuzzy based priority scheduler and vague based priority scheduler). Results: Proposed scheduler is also compared with the shortest job first CPU scheduler as it is known to be an optimized solution for the schedulers. Conclusion: Simulation results prove the effectiveness and efficiency of intuitionistic fuzzy based priority scheduler. Moreover, it provides optimised results as its results are comparable to the results of shortest job first.
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Ruekkasaem, Lakkana, Isaree Srikun, and Pasura Aungkulanon. "Comparative Study of Well Known Sequencing Methodologies and Differential Evolution: A Case Study of Thailand Garment Industries." Applied Mechanics and Materials 457-458 (October 2013): 1427–31. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.1427.
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The objective of this research is to study the problem and efficiency improvement of the clean room garment factory. The methods of production schedule were performed by heuristic technique of well-known scheduling methodologies including Shortest Processing Time, , Earliest Due Date, First Come First Serve method and Differential Evolution Method (DE). All methods can help to reduce the costs of delay shipment and time loss in various case studies. They were applied to the production order in 2 months after flooding problems in Thailand .The demand has a lot of quantities over production capacity. The results showed that the rules of Earliest Due Date method (EDD) is a powerful methodology for increasing production efficiency and minimizing make span. Whereas, the First Come First Serve (FCFS) method is the best method for finding a minimum value of total late job. Finally, Differential Evolution Method (DE) is the best method for finding a minimize value of average tardiness job.
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Indrianti, Nur, and Vina Islamia Vervly Suandevin. "Determination of Order Delivery Time in Event Organizer Industry Using a Non-Delay Scheduling Approach." International Journal of Service Management and Sustainability 5, no. 1 (June 28, 2020): 1. http://dx.doi.org/10.24191/ijsms.v5i1.9868.
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This study discusses the determination of order delivery time in the event organizer (EO) industry. With regard to the characteristics of the EO production process that is identical to the job shop production process in the manufacturing industry, a non-delay scheduling approach is applied. The non-delay schedule is compiled using the non-delay algorithm with the criteria for makespan minimization. Job assignment is done using the shortest processing time (SPT), longest processing time (LPT), and first comes first served (FCFS) priority rules. We consider the situation where all orders arrive simultaneously (offline) and at different time (online). As a case study, the modified non-delay algorithm is examined to solve the problem of an EO in Indonesia. The results of the study show that the nondelay algorithm using SPT rule provides the best schedule performance which results in the shortest makespan and the lowest resource idle time. In addition to determining the delivery time, the resulting non-delay schedule can be used to control the execution of each order. The method of determining order delivery time in this study can be applied to other service industries. Further study can be developed for situations where order arrival and processing time are probabilistic. Furthermore, it is also necessary to consider the balanced distribution of the workload among operators. Determination of Order Delivery Time in Event
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M, Preethi, and Kayalvizhi Jayavel. "IOT based visualization of weightage based static task scheduling algorithm in datacenter." International Journal of Engineering & Technology 7, no. 2.8 (March 19, 2018): 439. http://dx.doi.org/10.14419/ijet.v7i2.8.10478.
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Cloud computing has raised majorly to provide everything as a service and also for scaling the resources and utilizing the resources in an effective way. This paper aims to propose a scheduling algorithm which allocates static tasks to the resources effectively without making any tasks starve for the resources for long time. In SJF algorithm, the shortest tasks will be executed initially, and the largest tasks will keep on starving for the resources to be allocated. The proposed algorithm handles such a situation effectively by adding the jobs under different weightage queues and then scheduling them in an SJF order. This gives priority to even largest job. In this paper a framework is proposed, which fetches data from Amazon SDB storage and the processing of data based on proposed algorithm occurs in a cloudsim and finally the results are visualized through an IOT mobile device. The comparison is also made for First Come First Serve (FCFS), which is a default scheduling algorithm and the proposed algorithm.
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Naderia, Bahman, and Sheida Goharib. "The cycle shop scheduling: mathematical model, properties and solution algorithms." RAIRO - Operations Research 53, no. 1 (January 2019): 111–28. http://dx.doi.org/10.1051/ro/2017086.
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Conventionally, in scheduling problems it is assumed that each job visits each machine once. This paper studies a novel shop scheduling called cycle shop problems where jobs might return to each machine more than once. The problem is first formulated by two mixed integer linear programming models. The characteristics of the problem are analyzed, and it is realized that the problem suffers from a shortcoming called redundancy, i.e., several sequences represents the same schedule. In this regard, some properties are introduced by which the redundant sequences can be recognized before scheduling. Three constructive heuristics are developed. They are based on the shortest processing time first, insertion neighborhood search and non-delay schedules. Then, a metaheuristic based on scatter search is proposed. The algorithms are equipped with the redundancy prevention properties that greatly reduce the computational time of the algorithms. Two sets of experiments are conducted. The proposed model and algorithms are evaluated. The results show the high performance of model and algorithms.
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Aungkulanon, Pasura. "Comparison of Bee Algorithm and Scheduling Methodologies: A Case Study of Manufacturing in Thailand." Applied Mechanics and Materials 835 (May 2016): 864–68. http://dx.doi.org/10.4028/www.scientific.net/amm.835.864.
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The aim of this research is to study the problem and efficiency improvement of the instrument factory in Thailand. The methods of production schedule are performed by four heuristic techniques which are Shortest Processing Time (SPT), Earliest Due Date (EDD), First Come First Serve (FCFS) and Bee algorithm (BA). All four methods aim to reduce the cost of delay shipment and time loss in manufacturing process. We apply these methods in the production process in an instrument factory. The results show that BA method is powerful in term of minimize make span and average completion time. Whereas, SPT method is the best method for finding minimum value of total late job.
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SANTOSO, J., G. D. VAN ALBADA, P. M. A. SLOOT, and B. A. A. NAZIEF. "SIMULATION OF HIERARCHICAL RESOURCE MANAGEMENT FOR META-COMPUTING SYSTEMS." International Journal of Foundations of Computer Science 12, no. 05 (October 2001): 629–43. http://dx.doi.org/10.1142/s0129054101000692.
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Optimal scheduling in meta-computing environments still is an open research question. Various resource management (RM) architectures have been proposed in the literature (e.g. [2][13][12]). In the present paper we explore, through simulation, various multi-level scheduling strategies for compound computing environments comprising several clusters of workstations. We study global and local RM and their interaction. The local RM comprises both the cluster management and operating system schedulers. Each level refines the scheduling decisions of the layer above it, taking into account the latest resource information. Our experiments explore conventional strategies like First Come, First Served (FCFS) and Shortest Job First (SJF) at the global RM level. At all levels, the schedulers strive to maintain a good load balance. The unit of load balancing at the global level is the job consisting of one or more parallel tasks; at the local level it is the task. The results of our simulations indicate that, especially at high system loads, the use of a global RM can result in a significant performance gain.
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Vis, P., R. Bekker, and R. D. van der Mei. "Heavy-traffic limits for polling models with exhaustive service and non-FCFS service order policies." Advances in Applied Probability 47, no. 04 (December 2015): 989–1014. http://dx.doi.org/10.1017/s0001867800048989.
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We study cyclic polling models with exhaustive service at each queue under a variety of non-FCFS (first-come-first-served) local service orders, namely last-come-first-served with and without preemption, random-order-of-service, processor sharing, the multi-class priority scheduling with and without preemption, shortest-job-first, and the shortest remaining processing time policy. For each of these policies, we first express the waiting-time distributions in terms of intervisit-time distributions. Next, we use these expressions to derive the asymptotic waiting-time distributions under heavy-traffic assumptions, i.e. when the system tends to saturate. The results show that in all cases the asymptotic waiting-time distribution at queueiis fully characterized and of the form Γ Θi, with Γ and Θiindependent, and where Γ is gamma distributed with known parameters (and the same for all scheduling policies). We derive the distribution of the random variable Θiwhich explicitly expresses the impact of the local service order on the asymptotic waiting-time distribution. The results provide new fundamental insight into the impact of the local scheduling policy on the performance of a general class of polling models. The asymptotic results suggest simple closed-form approximations for the complete waiting-time distributions for stable systems with arbitrary load values.
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Vis, P., R. Bekker, and R. D. van der Mei. "Heavy-traffic limits for polling models with exhaustive service and non-FCFS service order policies." Advances in Applied Probability 47, no. 4 (December 2015): 989–1014. http://dx.doi.org/10.1239/aap/1449859797.
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We study cyclic polling models with exhaustive service at each queue under a variety of non-FCFS (first-come-first-served) local service orders, namely last-come-first-served with and without preemption, random-order-of-service, processor sharing, the multi-class priority scheduling with and without preemption, shortest-job-first, and the shortest remaining processing time policy. For each of these policies, we first express the waiting-time distributions in terms of intervisit-time distributions. Next, we use these expressions to derive the asymptotic waiting-time distributions under heavy-traffic assumptions, i.e. when the system tends to saturate. The results show that in all cases the asymptotic waiting-time distribution at queue i is fully characterized and of the form Γ Θi, with Γ and Θi independent, and where Γ is gamma distributed with known parameters (and the same for all scheduling policies). We derive the distribution of the random variable Θi which explicitly expresses the impact of the local service order on the asymptotic waiting-time distribution. The results provide new fundamental insight into the impact of the local scheduling policy on the performance of a general class of polling models. The asymptotic results suggest simple closed-form approximations for the complete waiting-time distributions for stable systems with arbitrary load values.
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Bachmat, Eitan, and Josu Doncel. "Size-Based Routing Policies: Non-Asymptotic Analysis and Design of Decentralized Systems." Sensors 21, no. 8 (April 12, 2021): 2701. http://dx.doi.org/10.3390/s21082701.
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Size-based routing policies are known to perform well when the variance of the distribution of the job size is very high. We consider two size-based policies in this paper: Task Assignment with Guessing Size (TAGS) and Size Interval Task Assignment (SITA). The latter assumes that the size of jobs is known, whereas the former does not. Recently, it has been shown by our previous work that when the ratio of the largest to shortest job tends to infinity and the system load is fixed and low, the average waiting time of SITA is, at most, two times less than that of TAGS. In this article, we first analyze the ratio between the mean waiting time of TAGS and the mean waiting time of SITA in a non-asymptotic regime, and we show that for two servers, and when the job size distribution is Bounded Pareto with parameter α=1, this ratio is unbounded from above. We then consider a system with an arbitrary number of servers and we compare the mean waiting time of TAGS with that of Size Interval Task Assignment with Equal load (SITA-E), which is a SITA policy where the load of all the servers are equal. We show that in the light traffic regime, the performance ratio under consideration is unbounded from above when (i) the job size distribution is Bounded Pareto with parameter α=1 and an arbitrary number of servers as well as (ii) for Bounded Pareto distributed job sizes with α∈(0,2)\{1} and the number of servers tends to infinity. Finally, we use the result of our previous work to show how to design decentralized systems with quality of service constraints.
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Liu, Xin, and Lei Ying. "Steady-state analysis of load-balancing algorithms in the sub-Halfin–Whitt regime." Journal of Applied Probability 57, no. 2 (June 2020): 578–96. http://dx.doi.org/10.1017/jpr.2020.13.
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AbstractWe study a class of load-balancing algorithms for many-server systems (N servers). Each server has a buffer of size $b-1$ with $b=O(\sqrt{\log N})$, i.e. a server can have at most one job in service and $b-1$ jobs queued. We focus on the steady-state performance of load-balancing algorithms in the heavy traffic regime such that the load of the system is $\lambda = 1 - \gamma N^{-\alpha}$ for $0<\alpha<0.5$ and $\gamma > 0,$ which we call the sub-Halfin–Whitt regime ($\alpha=0.5$ is the so-called Halfin–Whitt regime). We establish a sufficient condition under which the probability that an incoming job is routed to an idle server is 1 asymptotically (as $N \to \infty$) at steady state. The class of load-balancing algorithms that satisfy the condition includes join-the-shortest-queue, idle-one-first, join-the-idle-queue, and power-of-d-choices with $d\geq \frac{r}{\gamma}N^\alpha\log N$ (r a positive integer). The proof of the main result is based on the framework of Stein’s method. A key contribution is to use a simple generator approximation based on state space collapse.
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Wibowo, Achmad Teguh. "Super Smart Optimized Round Robin." Systemic: Information System and Informatics Journal 2, no. 2 (December 1, 2016): 19–21. http://dx.doi.org/10.29080/systemic.v2i2.110.
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Aspek penting dalam sistem operasi adalah multiprogramming. Multiprogramming adalah proses atau metode yang digunakan untuk mengekssekusi beberapa proses secara bersamaan dalam memori. Tujuan utamanya adalah untuk meminimalkan Average Waiting Time, Average Turnaround Time, dan memaksimalkan penggunaan CPU. Ada berbagai algoritma yang digunakan dalam multiprogramming seperti First Come First Serve (FCFS), Shortest Job First (SJF), Priority Scheduling (PS) dan Round Robin(RR). Diantara semua itu yang paling sering digunakan adalah Round Robin. Round Robin merupakan algoritma penjadwalan yang optimal dengn sistem timeshared. Dalam RR, waktu kuantum bersifat statis dan algoritma ini bergantung pada besarnya kuantum yang dipilih/digunakan. Kuantum inilah yang berpengaruh pada Average Waiting Time dan Average Turnaround Time nantinya. Tujuan dari makalah ini adalah mengusulkan algoritma yang lebih baik daripada Round Robin sederhana dan Smart Optimized Round Robin sebelumnya.
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Yusriski, Rinto, Ragil Pardiyono, Sinta Rahmawati, and Lia Nur Atika. "MERANCANG MODEL PENJADWALAN KONSTRUKSI MULTI BENDING DENGAN MEMPERTIMBANGKAN POLA ALIRAN PROSES JOB SHOP UNTUK MEMINIMUMKAN MAKESPAN." Volume 22 No. 2 Desember 2020 22, no. 2 (November 25, 2020): 115–22. http://dx.doi.org/10.23969/infomatek.v22i2.3353.
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PT XYZ merupakan salah satu perusahaan manufaktur yang bergerak di bidang mebel seperti meja, rak dan kursi. Karakteristik pekerjaan yang diproses perusahaan adalah terdapat sejumlah pekerjaan yang harus dikerjakan pada mesin-mesin dengan routing yang berbeda-beda (memiliki aliran proses job shop) dan setiap pekerjaan terdiri atas satu atau lebih unit permintaan. Hasil studi awal menunjukkan bahwa terdapat masalah pada Departemen produksi bagian konstruksi multi bending yaitu tidak tercapainya target produksi harian (tercapai sebesar 77%). Berdasarkan hasil pengecekan kapasitas menunjukkan bahwa kapasitas tersedia sebesar 22.080 menit, sementara waktu yang diperlukan adalah 5.472 menit. Dengan demikian, target produksi seharusnya dapat selesai pada waktu yang telah ditetapkan oleh perusahaan. Studi awal menyimpulkan bahwa metoda penjadwalan perusahaan perlu diperbaiki. Perusahaan mengadopsi teknik penjadwalan dengan aturan prioritas First-Come-First-Serve (FCFS), yaitu prioritas pengerjaan job diurutkan berdasarkan saat kedatangan job ke lantai produksi. Analisa awal menunjukkan bahwa keterlambatan ini disebabkan oleh pengiriman pekerjaan dari suatu mesin ke mesin yang lain yang dilakukan setelah seluruh komponen pekerjaan tersebut diselesaikan. Hal ini mengakibatkan makespan yang panjang sehingga beberapa pekerjaan selesai melampaui saat tenggat (due date). Pada sistem nyata diketahui bahwa stasiun kerja memiliki jumlah mesin lebih dari satu sehingga memungkinkan job untuk dijadwalkan pada mesin-mesin tersebut secera paralel. Selain itu, komponen-komponen dari job yang telah selesai bisa saja dikirimkan pada mesin selanjutnya (operation overlapping) sesuai routing tanpa harus menunggu seluruh komponen selesai. Hal ini membutuhkan penentuan besar ukuran lot yang harus ditransfer antar mesin. Penelitian ini mengusulkan metode penjadwalan dengan menggunakan prioritas Shortest Processing Time (SPT) yang digkombinasikan dengan teknik lot splitting. Asumsi yang digunakan adalah seluruh job sudah tersedia dari saat t=0. Hasil dari penjadwalan usulan ini mempunyai hasil yang lebih baik dalam mengurangi makespan sebesar 20,7% dan meningkatkan utilitas mesin sebesar 8%.
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Zahri, Amiluddin, and Muhammad Kumroni Makmuri. "OPTIMALISASI PRODUKSI DENGAN MENGANALISIS PEKERJAAN MENGGUNAKAN METODE MOST (Studi Kasus PT. X di Palembang)." Jurnal Tekno 16, no. 1 (April 1, 2019): 1–16. http://dx.doi.org/10.33557/jtekno.v16i1.356.
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A job is completed efficiently would say when lasted the shortest completion time. Measuring success of a production system in the industry are usually expressed in terms of productivity or the size of the input and output generated. Measurement standard at this time are made directly by the method stopwatch and indirectly with MOST methods (Maynard Operation Sequence Technique). Results of measurements using a standard time stop watch is 179,62 Second, with MOST measurements before the analysis is 123,73, most after the analisis is 75,25 second. Raw output using the old work is 200 Unit newspaper first hour , and use the most is the raw output of 478 Unit newspaper first hour. Seeing the results of the study suggested that the company may consider to perform the application of research results have been obtained
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Roohnavazfar, Mina, Daniele Manerba, Lohic Fotio Tiotsop, Seyed Hamid Reza Pasandideh, and Roberto Tadei. "Stochastic single machine scheduling problem as a multi-stage dynamic random decision process." Computational Management Science 18, no. 3 (February 3, 2021): 267–97. http://dx.doi.org/10.1007/s10287-020-00386-1.
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AbstractIn this work, we study a stochastic single machine scheduling problem in which the features of learning effect on processing times, sequence-dependent setup times, and machine configuration selection are considered simultaneously. More precisely, the machine works under a set of configurations and requires stochastic sequence-dependent setup times to switch from one configuration to another. Also, the stochastic processing time of a job is a function of its position and the machine configuration. The objective is to find the sequence of jobs and choose a configuration to process each job to minimize the makespan. We first show that the proposed problem can be formulated through two-stage and multi-stage Stochastic Programming models, which are challenging from the computational point of view. Then, by looking at the problem as a multi-stage dynamic random decision process, a new deterministic approximation-based formulation is developed. The method first derives a mixed-integer non-linear model based on the concept of accessibility to all possible and available alternatives at each stage of the decision-making process. Then, to efficiently solve the problem, a new accessibility measure is defined to convert the model into the search of a shortest path throughout the stages. Extensive computational experiments are carried out on various sets of instances. We discuss and compare the results found by the resolution of plain stochastic models with those obtained by the deterministic approximation approach. Our approximation shows excellent performances both in terms of solution accuracy and computational time.
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Putra, Edson Y., Green A. Sandag, and Enrico S. Djimesha. "Aplikasi Simulator Algoritma Penjadwalan Central Processing Unit Menggunakan Actionscript 3.0." CSRID (Computer Science Research and Its Development Journal) 10, no. 3 (March 3, 2021): 139. http://dx.doi.org/10.22303/csrid.10.3.2018.139-145.
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<p>Teknologi pada zaman ini telah berkembang dengan pesat, khususnya teknologi Central Processing Unit. Karena itu, manusia dituntut untuk mempelajari cara kerja Central Processing Unit. Central Processing Unit itu sendiri memiliki banyak fungsi, salah satunya adalah melakukan pemrosesan, dimana terdapat antrian proses yang akan dieksekusi. Sayangnya, metode pembelajaran mengenai antrian Central Processing Unit masih kurang efektif karena hanya menggunakan Gantt Chart sebagai gambaran untuk cara kerjanya. Ini menyebabkan peneliti bermaksud untuk melakukan perancangan aplikasi simulator berbasis desktop untuk membantu para pelajar agar lebih mudah memahami dengan memberikan gambaran secara animasi mengenai cara kerja antrian proses yang terdapat pada Central Processing Unit, khususnya antrian dengan algoritma First-Come-First-Served dan nonpreemptive Shortest-Job-First. Pada perancangan aplikasi ini, peneliti menggunakan perangkat lunak Adobe Flash Professional CC 2015 dengan bahasa pemrograman ActionScript 3.0.<em> </em><em></em></p>
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Faccio, Maurizio, Mojtaba Nedaei, and Francesco Pilati. "A new approach for performance assessment of parallel and non-bottleneck machines in a dynamic job shop environment." International Journal of Energy Sector Management 13, no. 4 (November 4, 2019): 787–803. http://dx.doi.org/10.1108/ijesm-11-2018-0005.
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Purpose The 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/approach An 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. Findings Significant 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/value Considerable 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.
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Lei, Bin, Fangxin Hu, Zhaoyuan Jiang, and Haibo Mu. "Optimization of Storage Location Assignment in Tier-To-Tier Shuttle-Based Storage and Retrieval Systems Based on Mixed Storage." Mathematical Problems in Engineering 2020 (May 20, 2020): 1–17. http://dx.doi.org/10.1155/2020/2404515.
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To improve the efficiency of tier-to-tier shuttle-based storage and retrieval system (SBS/RS), the optimization problem of the location allocation based on the mixed storage of goods is proposed. Considering the effect of warehouse operation scheduling and batch outbound allocation on the location allocation, an optimization model with the shortest outbound time of all outbound orders in a certain historical period is established. The optimization model consists of two stages: location allocation and job scheduling. A two-layer genetic algorithm is designed to solve the model. The first layer is used to solve the location allocation, and the coding method is group coding; the second layer is used for job scheduling, and the coding mode is real number coding. When the population is initialized during the location allocation phase, the BFD algorithm is used to improve the convergence velocity of the algorithm. Taking the actual data of a tier-to-tier SBS/RS of an aviation food company as an example, the established model and design algorithm were verified, and the different batch intervals of each cargo space for storing different types of goods and outbound were analyzed. The optimization effects of the algorithm are compared, and the effects of considering the job scheduling and not considering the job scheduling on the location allocation are compared. The results show that based on the cargo allocation strategy of cargo mixed storage, the outbound efficiency can be improved by about 20%. Considering job scheduling, the efficiency of warehousing is improved by about 5% compared with the optimization of warehouse allocation without job scheduling, where the efficiency of delivery is increased by about 6%.
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Yin, Peng-Yeng, Hsin-Min Chen, Yi-Lung Cheng, Ying-Chieh Wei, Ya-Lin Huang, and Rong-Fuh Day. "Minimizing the Makespan in Flowshop Scheduling for Sustainable Rubber Circular Manufacturing." Sustainability 13, no. 5 (February 28, 2021): 2576. http://dx.doi.org/10.3390/su13052576.
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It is estimated that 1 billion waste tires are generated every year across the globe, yet only 10% are being processed, and much rubber waste is yielded during manufacturing. These waste tires and rubber scraps are poisonous to the environment when processed via incineration and landfill. Rubber circular manufacturing is an effective solution that reduces not only rubber waste but also raw material costs. In this paper we propose a two-line flowshop model for the circular rubber manufacturing problem (CRMP), where the job sequence of two production lines is appropriately aligned to obtain the shortest makespan while guaranteeing that sufficient rubber waste yielded in the first line is ready to be reused for circular production in the second line. A genetic algorithm (GA) is developed, and the design of its genetic operations is customized to the CRMP context to achieve efficient and effective evolution. The experimental results with both real and synthetic datasets show that the GA significantly surpasses two heuristics in the literature by delivering the minimum makespan, which is 3.4 to 11.2% shorter than those obtained by the two competing methods.
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Fuchigami, Helio Yochihiro, and João Vitor Moccellin. "Estudo da influência da programação do primeiro estágio em flow shop híbridos com tempos de setup explícitos." Exacta 11, no. 2 (December 12, 2013): 149–60. http://dx.doi.org/10.5585/exactaep.v11n2.4466.
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Neste artigo são apresentados quatro métodos heurísticos construtivos (denominados LPT, TOTAL, SCT e LPST) para programação da produção em sistemas flow shop híbridos com tempos de setup independentes da sequência de execução das tarefas. O foco deste trabalho é o estudo da influência da programação do primeiro estágio neste ambiente de produção. O critério de desempenho utilizado é a minimização da duração total da programação (makespan). O método LPT baseia-se na conhecida regra Longest Processing Time e sequencia as tarefas pela ordem crescente da soma dos tempos de processamento de todos os estágios. O TOTAL utiliza a ideia do Método de Aproximação de Vogel, considerando a matriz composta pela soma dos tempos de processamento e setup de todos os estágios. A denominação SCT foi atribuída a este método por escolher o par tarefa-máquina que conduz à menor data de término (Shortest Completion Time). E o método LPST atribui à máquina de menor carga a tarefa com a maior soma dos tempos de processamento e setup do primeiro estágio. Os resultados da experimentação computacional mostraram que o método SCT foi claramente superior aos demais, com 55,3% de sucesso, enquanto o LPT obteve 26,9%, o TOTAL atingiu 16,4% e por último o LPST apresentou 2,2% de sucesso. Palavras-chave: programação da produção; flow shop híbrido; setup independente. ABSTRACT This paper addresses the hybrid flow shop scheduling problem with sequence-independent setup times. Four constructive heuristic methods (called LPT, TOTAL, SCT and LPST) were proposed with objective to evaluate the influence of the schedule of the first production stage in this environment. The minimization of the total time to complete the schedule (makespan) was used as performance criterion. The LPT method is based on the well-known Longest Processing Time rule and it sequence the jobs by the ascending order of the total processing times of all stages. The TOTAL method uses the idea of Vogel Approximation Method, considering the matrix composed by the sum of processing and setup times of all stages. The SCT method chooses the pair machine-job that leads the Shortest Completion Time. And the LPST method designates to the lower load machine the job with the largest sum of processing and setup times of the first stage. Results of computational experiments reveled that the SCT method was clearly the superior, with 55.3% of success, while LPT obtained 26.9%, TOTAL reached 16.4% and lastly LPST presented 2.2% of success. Keywords: production scheduling; hybrid flow shop; sequence-independent setup time.
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Kaur, Mandeep, and Rajni Mohana. "Static Load Balancing Technique for Geographically Partitioned Public Cloud." Scalable Computing: Practice and Experience 20, no. 2 (May 2, 2019): 299–316. http://dx.doi.org/10.12694/scpe.v20i2.1520.
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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.
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Olofintuyi, Sunday Samuel, Temidayo Oluwatosin Omotehinwa, and Joshua Segun Owotogbe. "A SURVEY OF VARIANTS OF ROUND ROBIN CPU SCHEDULING ALGORITHMS." FUDMA JOURNAL OF SCIENCES 4, no. 4 (June 15, 2021): 526–46. http://dx.doi.org/10.33003/fjs-2020-0404-513.
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Quite a number of scheduling algorithms have been implemented in the past, including First Come First Served (FCFS), Shortest Job First (SJF), Priority and Round Robin (RR). However, RR seems better than others because of its impartiality during the usage of its quantum time. Despite this, there is a big challenge with respect to the quantum time to use. This is because when the quantum time is too large, it leads to FCFS, and if the quantum time is too short, it increases the number of switches from the processes. As a result of this, this paper provides a descriptive review of various algorithms that have been implemented in the past 10 years, for various quantum time in order to optimize the performance of CPU utilization. This attempt will open more research areas for researchers, serve as a reference source and articulate various algorithms that have been used in the previous years – and as such, the paper will serve as a guide for future work. This research work further suggests novel hybridization and ensemble of two or more techniques so as to improve CPU performance by decreasing the number of context switch, turnaround time, waiting time and response time and in overall increasing the throughput and CPU utilization.
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Prasad Arya, Govind, Kumar Nilay, and Devendra Prasad. "An Improved Round Robin CPU Scheduling Algorithm based on Priority of Process." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 238. http://dx.doi.org/10.14419/ijet.v7i4.5.20077.
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The most important and integral part of a computer system is its operating system. Scheduling various resources is one of the most critical tasks an operating system needs to perform. Process scheduling being one of those tasks, involves various techniques that define how more than one processes can be executed simultaneously. The primary aim here is to the system more efficient and faster. The fundamental scheduling algorithms are: First Come First Serve (FCFS), Round Robin, Priority Based Scheduling, and Shortest Job First (SJF). This paper focuses on Round Robin Scheduling algorithm and various issues related to it. One major issue in RR scheduling is determining the length of Time Quantum. If the Time Quantum is too large RR scheduling behaves as FCFS. On the other hand, if it is too small it forces considerable increase in the number of context switches. Our main objective is to overcome this limitation of traditional RR scheduling algorithm and maximize CPU utilization, further, leading to more efficient and faster system. Here we propose an algorithm that categorizes available processes into High Priority processes and Low Priority process. The proposed algorithm reduces the average waiting time of High Priority processes in all cases and of Low Priority processes in not all but some cases. The overall waiting time changes on the basis of set of processes considered. The simulation results justify that the proposed schemes reduces the overall average waiting time when compared to the existing schemes.
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Stan, Roxana-Gabriela, Lidia Băjenaru, Cătălin Negru, and Florin Pop. "Evaluation of Task Scheduling Algorithms in Heterogeneous Computing Environments." Sensors 21, no. 17 (September 2, 2021): 5906. http://dx.doi.org/10.3390/s21175906.
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This work establishes a set of methodologies to evaluate the performance of any task scheduling policy in heterogeneous computing contexts. We formally state a scheduling model for hybrid edge–cloud computing ecosystems and conduct simulation-based experiments on large workloads. In addition to the conventional cloud datacenters, we consider edge datacenters comprising smartphone and Raspberry Pi edge devices, which are battery powered. We define realistic capacities of the computational resources. Once a schedule is found, the various task demands can or cannot be fulfilled by the resource capacities. We build a scheduling and evaluation framework and measure typical scheduling metrics such as mean waiting time, mean turnaround time, makespan, throughput on the Round-Robin, Shortest Job First, Min-Min and Max-Min scheduling schemes. Our analysis and results show that the state-of-the-art independent task scheduling algorithms suffer from performance degradation in terms of significant task failures and nonoptimal resource utilization of datacenters in heterogeneous edge–cloud mediums in comparison to cloud-only mediums. In particular, for large sets of tasks, due to low battery or limited memory, more than 25% of tasks fail to execute for each scheduling scheme.
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Jayaram, Asokan, and Sanjoy Deb. "EA-MAC: A QoS Aware Emergency Adaptive MAC Protocol for Intelligent Scheduling of Packets in Smart Emergency Monitoring Applications." Journal of Circuits, Systems and Computers 29, no. 13 (February 28, 2020): 2050205. http://dx.doi.org/10.1142/s0218126620502059.
Full textAbstract:
The evolution of the wireless sensor network (WSN) in recent years has reached its greatest heights and applications are increasing day by day, one such application is Smart Emergency Monitoring Systems (SMESs) which is in vision of implementation in every urban and rural areas. The implementation of WSN architecture in the Smart Monitoring Systems needs an intelligent scheduling mechanism that efficiently handles the high traffic load as well as the emergency traffic load without sacrificing the energy efficiency of the network. However, the traditional scheduling algorithms such as First Come First Served (FCFS), Round Robin, and Shortest Job First (SJF) cannot meet the requirements of high traffic load in SMESs. To address these shortcomings, this paper presents Emergency Adaptive Medium Access Control protocol (EA-MAC), a fuzzy priority scheduling based Quality-of-service (QoS)-aware medium access control (MAC) protocol for hierarchical WSNs. EA-MAC protocol employs the most powerful fuzzy logics to schedule the sensor nodes with both normal and emergency traffic load without any data congestion, and packet loss and maintaining the better QoS which is considered to be more important in SMESs applications. Moreover, a novel rank-based clustering mechanism in EA-MAC protocol prolongs the network lifetime by minimizing the distance between the Cluster Head (CH) and the Base Station (BS). Both analytical and simulation models demonstrate the superiority of the EA-MAC protocol in terms of energy consumption, transmission delay and data throughput when compared with the existing Time Division Multiple Access (TDMA) based MAC protocols such as LEACH protocol and Cluster Head Election Mechanism-Based On Fuzzy Logic (CHEF) protocol.
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Javaid, Sakeena, Nadeem Javaid, Tanzila Saba, Zahid Wadud, Amjad Rehman, and Abdul Haseeb. "Intelligent Resource Allocation in Residential Buildings Using Consumer to Fog to Cloud Based Framework." Energies 12, no. 5 (March 1, 2019): 815. http://dx.doi.org/10.3390/en12050815.
Full textAbstract:
In this work, a new orchestration of Consumer to Fog to Cloud (C2F2C) based framework is proposed for efficiently managing the resources in residential buildings. C2F2C is a three layered framework consisting of cloud layer, fog layer and consumer layer. Cloud layer deals with on-demand delivery of the consumer’s demands. Resource management is intelligently done through the fog layer because it reduces the latency and enhances the reliability of cloud. Consumer layer is based on the residential users and their electricity demands from the six regions of the world. These regions are categorized on the bases of the continents. Two control parameters are considered: clusters of buildings and load requests, whereas four performance parameters are considered: Request Per Hour (RPH), Response Time (RT), Processing Time (PT) and cost in terms of Virtual Machines (VMs), Microgrids (MGs) and data transfer. These parameters are analysed by the round robin algorithm, equally spread current execution algorithm and our proposed algorithm shortest job first. Two scenarios are used in the simulations: resource allocation using MGs and resource allocation using MGs and power storage devices for checking the effectiveness of the proposed work. The simulation results of the proposed technique show that it has outperformed the previous techniques in terms of the above-mentioned parameters. There exists a tradeoff in the PT and RT as compared to cost of VM, MG and data transfer.
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Shakeel, Atif, Riaz Hussain, Adeel Iqbal, Irfan Latif Khan, Qadeer Ul Hasan, and Shahzad Ali Malik. "Spectrum Handoff based on Imperfect Channel State Prediction Probabilities with Collision Reduction in Cognitive Radio Ad Hoc Networks." Sensors 19, no. 21 (October 31, 2019): 4741. http://dx.doi.org/10.3390/s19214741.
Full textAbstract:
The spectrum handoff is highly critical as well as challenging in a cognitive radio ad hoc network (CRAHN) due to lack of coordination among secondary users (SUs), which leads to collisions among the SUs and consequently affects the performance of the network in terms of spectrum utilization and throughput. The target channel selection mechanism as part of handoff process can play an enormously significant role in minimizing the collisions among the SUs and improving the performance of a cognitive radio network (CRN). In this paper, an enhanced target channel selection scheme based on imperfect channel state prediction is proposed for the spectrum handoff among the SUs in a CRAHN. The proposed scheme includes an improved frame structure that increases coordination among the SUs in the ad hoc environment and helps in organizing the SUs according to the shortest job first principle during channel access. Unlike the existing prediction-based spectrum handoff techniques, the proposed scheme takes into account the accuracy of channel state prediction; the SUs affected due to false prediction are compensated by allowing them to contend for channel access within the same transmission cycle and thus enabling them to achieve higher throughput. The proposed scheme has been compared with the contemporary spectrum handoff schemes and the results have demonstrated substantial improvement in throughput and extended data delivery time by virtue of the reduced number of collisions.