FCFS Algorithm Research Articles

Cost-Aware Task Scheduling in Cloud Computing Environment

Cloud computing is a new generation of computing environment which delivers the applications as a service to users over the internet. The users can select any service from a list provided by service providers depending on their demands or needs. The nature of this new computing environment leads to tasks scheduling and load balancing problems which become a booming research area. In this paper, we have proposed Scheduling Cost Approach (SCA) that calculates the cost of CPU, RAM, bandwidth, storage available. In this approach, the tasks will be distributed among the VMs based on the priority given by user. The priority depends on the user budget satisfaction. The proposed SCA will try to improve the load balance by selecting suitable VM for each task. The results of SCA are compared with the results of FCFS and SJF algorithms which proves that, the proposed SCA approach significantly reduces the cost of CPU, RAM, bandwidth, storage compared to FCFS and SJF algorithms.

Priority queue based polling mechanism on seismic equipment cluster monitoring

This paper proposes a passive monitoring system model suitable for seismic observation equipment cluster. Through detailed analysis and design of two key technologies affecting the performance of the system under this model, we pose the priority queue based (PQB) polling mechanism on the cluster node monitoring and the volatility-removed (VR) fault alarm mechanism. New polling mechanism initializes priorities of cluster nodes according to the importance of them, then put them into the polling queue according to the priority order, nodes with same priority follow FCFS algorithm, thus solving problems that important cluster nodes be monitored first. A formula judging whether equipment is in the fluctuant state is given by VR fault alarm mechanism, which improves the accuracy of the monitoring results. Finally, we build an experimental platform to analyze the data of PQB polling and VR warning mechanisms, the conclusion shows that the PQB increases the count of polling nodes than FCFS polling mechanism and VR is more close to the actual fault results than normal alarm mechanism. Furthermore, PQB and VR mechanisms have no significant impact on server performance.

PENJADWALAN FLOWSHOP DENGAN MIXED INTEGER PROGRAMMING UNTUK MEMINIMASI TOTAL FLOWTIME DAN TOTAL TARDINESS

Abstract Adi Bungsu (AB) Cigarette Company produces Clove Cigarette in four stages. Each stage is processed by one machine. Thus, the production line is described as flowshop. AB receive order from customer over country. Every month, AB should schedule the order from customer. Thus, AB should prioritize which order to be processed firstly. At this time, AB schedules their order by using First Come First Serve algorithm which prioritize the first order as the first processed job. Previously, this algorithm results in tardiness and high work-in-process. Therefore, this study aims to reduce the tardiness and work-in-process level. In this study, the scheduling problem is solved by using mixed integer programming. To minimize tardiness and work-in-process simultaneously, the objective include minimizing total tardiness and total flowtime. Meanwhile, this study also consider setup time separated from processing time so that setup can be done early while waiting job to be processed in previous stage. The results of this study show that mixed integer programming with two-criteria can be used to schedule job ordered by customer. In case of bicriteria scheduling problem with separated setup time, mixed integer programming generate better schedule than FCFS algorithm.

Artificial Bee Colony Algorithm for Cloud Task Scheduling

Cloud computing services are becoming ubiquitous, and are becoming the primary source of computing power for both enterprises and personal computing applications. One of the fundamental issues in this environment is related to task scheduling. The scheduler should do the scheduling process efficiently in order to utilize the available resources. In this paper a cloud task scheduling policy based on artificial bee colony algorithm compared with different scheduling algorithms has been proposed. The main goal of the proposed algorithm is minimizing the makespan of a given tasks set. Artificial bee colony algorithm models the behavior of honey bees and can be used to find solutions for difficult or impossible combinatorial problems. Algorithms have been simulated using Cloudsim toolkit package. Experimental results showed that the artificial bee colony algorithm outperformed ACO, FPLTF and FCFS algorithms.

Scheduling parallel jobs with tentative runs and consolidation in the cloud

Since the success of cloud computing, more and more high performance computing parallel applications run in the cloud. Carefully scheduling parallel jobs is essential for cloud providers to maintain their quality of service. Existing parallel job scheduling mechanisms do not take the parallel workload consolidation into account to improve the scheduling performance. In this paper, after introducing a prioritized two-tier virtual machines architecture for parallel workload consolidation, we propose a consolidation-based parallel job scheduling algorithm. The algorithm employs tentative run and workload consolidation under such a two-tier virtual machines architecture to enhance the popular FCFS algorithm. Extensive experiments on well-known traces show that our algorithm significantly outperforms FCFS, and it can even produce comparable performance to the runtime-estimation-based EASY algorithm, though it does not require users to provide runtime estimation of the job. Moreover, our algorithm allows inaccurate CPU usage estimation and only requires trivial modification on FCFS. It is effective and robust for scheduling parallel workload in the cloud.

A NEW-THRESHOLD BASED JOB SCHEDULING FOR GRID SYSTEM

In heterogeneous distributed systems, utility grids have emerged as a new model of service. In this se rvice, workflow scheduling is one of the challenging probl ems for satisfying user’s quality requirements. One of the main issues in work flow scheduling is to minim ize the workflow execution cost in terms of time an d makespan. In this study, we propose a new workflow scheduling algorithm based on a novel concept calle d New-Threshold Based Scheduling (NTBS) that attempts to minimize the cost of workflow execution time and provides service fairness. It works under two p hases. The scheduler computes threshold in first ph ase and in second phase it schedules the grid jobs to r educe the execution time without affecting fairness . From the simulation results it is observed that NTBS giv es better performance in terms of reduced makespan and consistent turnaround time as compared to FCFS, EDF and other scheduling algorithms.

Multi-Processing Feed-Back Decision Making for Scheduling Gates in Manufacturing System

This research paper investigates the optimal solution to increase the productivity of production line by the assistance of decision making and job scheduling algorithms. Through the help of FCFS algorithm which was utilized in Task Queue, as well as Multi-Level Feedback for decision making and job scheduling, the minimization of idle times during the process is expected. Moreover in case of any tardiness of machines, system immediately sends feedback to renovate the machine station. Basically the novel RFID technology (Radio Frequency Identification) is used for any data transaction between system structures, including: reading, storing and categorizing the information to each tag that attached to pieces. Besides, the software has been developed for managing the global database. All in all the current system which is based on optimal suggestion of algorithms is under implementation in the CIM laboratory of Eastern Mediterranean University.

Load Balancing Approach for Scheduling Sequential Task in Grid Computing Environment

In grid computing environment, the efficiency of computing node is affected by several factors such as node utilization, allocation of jobs etc. The incoming job is allocated to appropriate node in such a way that the node utilization is maximized and a well-balanced load across all the participating computing nodes that enhances the overall performance of grid computing. This paper presents an amended hybrid approach for scheduling sequential task. The proposed approach uses combination of first-come-firstserved (FCFS) and genetic algorithm (GA). A sliding-window technique is presented to initiate alteration between the FCFS and GA, to offers a rapid task assignment. For GA we initially generate random population and use straightforward encoding. The proposed method is evaluated in the terms of makespan value and node utilization with a varying set of simulation cases and parameters after then it is compared with a well design first-come-first-server (FCFS) and hybrid genetic algorithm (HGA). Experimental results have shown significant improvement compared to the both FCFS and HGA algorithms. The result gives minimized makespan value with increased node utilization for both homogeneous and heterogeneous types of nodes.

Simple, space-efficient, and fairness improved FCFS mutual exclusion algorithms

Let n be the number of threads that can compete for a shared resource R. The mutual exclusion problem involves coordinating these n concurrent threads in accessing R in a mutually exclusive way. This paper addresses two basic questions related to the First-Come-First-Served (FCFS) mutual exclusion algorithms that use only read–write operations: one is regarding the lower bound on the shared space requirement and the other is about fairness.The current best FCFS algorithm using read–write operations requires 5n shared bits. Could the shared space requirement be further reduced?The existing FCFS mutual exclusion algorithms assure fairness only among the threads which cross the ‘doorway’ sequentially. In systems with multicore processors, which are becoming increasingly common nowadays, threads can progress truly in parallel. Therefore, it is quite likely that several threads can cross the doorway concurrently. In such systems, the threads which cross the doorway sequentially may constitute only a fraction of all competing threads. While this fraction of threads follow the FCFS order, the rest of the threads have to rely on a biased scheme which always favors threads with smaller identifiers. Is there a simpler way to remove this bias to assure global fairness?This paper answers the above two questions affirmatively by presenting simple FCFS mutual exclusion algorithms using only read–write operations—the first one using 3n shared bits and the latter algorithms using 4n shared bits. The resulting algorithms are simple, space-efficient, and highly fair.

Optimization of FCFS Based Resource Provisioning Algorithm for Cloud Computing

In our project, we propose an optimized version of the FCFS scheduling algorithm to address the major challenges of task scheduling in cloud. The incoming tasks are grouped on the basis of task requirement like minimum execution time or minimum cost and prioritized (FCFS manner). Resource selection is done on the basis of task constraints using a greedy approach. The proposed model will be implemented and tested on simulation toolkit. We intend to create a module depicting the normal FCFS algorithm in comparison to our optimized version algorithm for resource provisioning in the cloud.

Relaxed Parallel Scheduling Algorithms Investigation of Support Resource Reservation from Grid

In this paper, the grid computing environment resource reservation problem of using the parallel machine, proposed the relaxed time parallel scheduling models and algorithms support resource reservation. The simulation results of FCFS and EASY backfill algorithms in resource utilization, job bounded slowdown factor and the success rate of Advanced Reservation (AR) jobs were studied. Show that the relaxation mechanism, the average waiting time and the average bounded slowdown factor of non-reserved jobs down. EASY backfill algorithm which guarantees AR jobs quality of service at the same time, the better the performance improvement of non-AR jobs.

CPU 스케줄링을 학습하는 운영체제 시뮬레이션 프로그램의 설계 및 구현

컴퓨터 운영체제는 과거에는 대학에서만 배우는 과목이었으나, 컴퓨터가 점점 보편화되면서 고등학교와 중학교에서도 운영체제의 교육이 실시되고 있다. 학교에서 이루어지는 컴퓨터 교육을 살펴보면 컴퓨터에 대한 기본 원리와 핵심 철학은 이론 수업만으로 진행되고 있다. 이론 수업은 학습 매체의 활용 부족으로 때로 중요한 부분을 지나치거나 학습자가 흥미를 느끼지 못해 수업이 자칫 어려운 공부로 인식될 위험이 있다. 이러한 교육 환경에서 학습자에게 이론 수업에 추가로 다양한 매체를 활용해서 학습에 대한 이해를 돕고 수업 흥미를 유도해서 과목에 대한 이해를 높이는 연구가 필요한 실정이다. 본 논문에서는 프로그램을 구현하여 운영체제의 이론 중 CPU 스케줄링에 대해서 다양한 예를 진행 과정과 함께 시뮬레이션 하고 그 결과를 표시하는 프로그램을 개발하였다. 본 연구를 통해 개발된 프로그램을 이용하여 학습자를 교육하면 수업에 대한 흥미를 높일 수 있고 컴퓨터 교과의 이론적인 부분을 보강할 수 있다. 또한 다양한 프로그래밍 환경에서의 각 알고리즘(FCFS, SJFS, PS, RR)끼리의 비교 분석도 가능함을 입증한다. In the field of computer science, operating system concept is taught in university, but we now teach it in the middle and/or high school. Computer is also taught not only in college but also in middle and high school. If we look up the education of computer that is trained in school, basic principles or core techniques of computer science is educated only with its theory. If the theoretical education of computer science is just trained, sometimes students are not interested in it because of lack of shortage of mass media. Therefore, we could say that it is important that the computer education features a diverse range of media, including prints, paintings, sculpture, digital photographs, mixed media, and a simulation program. For all this reason, we design and implement a program for simulation with computer operating systems especially, CPU scheduling. There are many CPU scheduling algorithms we suggest to make students understand scheduling with some different examples in practical use. In this paper, we practically propose a new approach to be used with a study tool to make a motivation for students. We design a simulation program for teaching computer operation systems to show CPU scheduling and we implement a program to make use of comparison of FCFS, SJFS, PS, and RR scheduling algorithms. With our simulation program we present a comparative analysis between scheduling algorithms could be possible.

Online Due Date Strategy on Mass Customization

In order to make response rapidly for customization demand and reduce the cost, this paper studied the due date strategies between MTS stage and MTO stage of mass customization supply chain. We consider the sequencing and due date of a series of orders that arrive at MTO stage over time. Using of queuing theory, the paper puts forward SPTA-DE algorithm which is the on-line heuristic algorithm of orders scheduling and due date quotation. And, the paper make comparative analyses on the due date and cost for the SPTA- DE heuristic algorithm and FCFS algorithm under the various control parameters, and draws constructive conclusions.

A Class of Algorithms for Collision Resolution with Multiplicity Estimation

The wireless connectivity in pervasive computing has ephemeral character and can be used for creating ad hoc networks, sensor networks, connection with RFID (Radio Frequency Identification) tags etc. The communication tasks in such wireless networks often involve an inquiry over a shared channel, which can be invoked for: discovery of neighboring devices in ad hoc networks, counting the number of RFID tags that have a certain property, estimating the mean value contained in a group of sensors etc. Such an inquiry solicits replies from possibly large number of terminals n. This necessitates the usage of algorithms for resolving batch collisions (conflicts) with unknown conflict multiplicity n. In this paper we present a novel approach to the problem of collision resolution for batch conflicts. We show how the conventional tree algorithms for collision resolution can be used to obtain progressively accurate estimation of the multiplicity. We use the estimation to propose a more efficient binary tree algorithm, termed Estimating Binary Tree (EBT) algorithm. The EBT algorithm is suited for implementation when the conflicting nodes are passive, such as e.g. RFID tags. We extend the approach to design the Interval Estimation Conflict Resolution (IECR) algorithm. For n→∞ we prove that the efficiency achieved by IECR for batch arrivals is identical with the efficiency that Gallager’s FCFS algorithm achieves for Poisson packet arrivals. For finite n, the simulation results show that IECR is, to the best of our knowledge, the most efficient batch resolution algorithm reported to date.

Modeling and simulation of dynamic job scheduling in multiprocessor systems

Realizing the full potential of a multiprocessor system requires a good job scheduling algorithm. We propose a new job scheduling algorithm, called K-FCFS, which is an extension of the FCFS algorithm. We developed an approximate analytic model for this algorithm by decomposing the system into two interdependent submodels: (i) queue submodel, and (ii) processor submodel. The overall model is solved using an iterative technique. Validation with simulation results shows the errors to be within acceptable limits. The impact of key parameters is studied. The proposed algorithm is found to perform better than the FCFS algorithm.