• International Journal of Contents
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• 제8권4호
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• pp.7-11
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• 2012

Cloud Computing can be viewed as a dynamically-scalable pool of resources. Virtualization is one of the key technologies enabling Cloud Computing functionalities. Virtual machines (VMs) scheduling and allocation is essential in Cloud Computing environment. In this paper, two dynamic VMs scheduling and allocating schemes are presented and compared. One dynamically on-demand allocates VMs while the other deploys optimal threshold to control the scheduling and allocating of VMs. The aim is to dynamically allocate the virtual resources among the Cloud Computing applications based on their load changes to improve resource utilization and reduce the user usage cost. The schemes are implemented by using SimPy, and the simulation results show that the proposed adaptive scheme with one threshold can be effectively applied in a Cloud Computing environment both performance-wise and cost-wise.

• Journal of applied mathematics & informatics
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• 제18권1_2호
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• pp.383-391
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• 2005

The paper is devoted to some flow shop scheduling problems, where job processing times are defined by functions dependent on their positions in the schedule. An example is constructed to show that the classical Johnson's rule is not the optimal solution for two different models of the two-machine flow shop scheduling to minimize makespan. In order to solve the makespan minimization problem in the two-machine flow shop scheduling, we suggest Johnson's rule as a heuristic algorithm, for which the worst-case bound is calculated. We find polynomial time solutions to some special cases of the considered problems for the following optimization criteria: the weighted sum of completion times and maximum lateness. Some furthermore extensions of the problems are also shown.

• 한국정보과학회논문지:시스템및이론
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• 제37권4호
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• pp.246-255
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• 2010

CPU 자원이 다수의 가상머신에 의해 공유되는 Xen 가상화 환경에서는, CPU가 하나의 가상머신의 요청을 처리하는 동안 다른 가상머신은 CPU를 기다려야 하는 가상머신 스케줄링 지연이 존재한다. 가상화 환경에서 응용프로그램의 QoS 요구사항을 만족시키기 위하여 자원을 관리하는 대부분의 시스템은 가상머신의 자원 사용률과 가상머신에서 운영하는 응용프로그램의 성능을 모니터링하고 분석하여 자원을 재할당한다. 이 때 응용프로그램의 성능 분석을 위해 큐잉 모델 등과 같은 수학적인 모델링 기법이 사용되지만 비가상화 환경에서 사용되던 모델은 가상머신 스케줄링 지연을 고려하지 않으므로, 가상화 환경에서는 정확한 분석과 예측이 어렵고, 따라서 이를 기반으로 자원을 관리하는 시스템은 요구되는 응용프로그램의 성능을 제공하지 못할 수 있다. 따라서 본 논문에서는 Xen 가상화 환경에서 가상머신 스케줄링 지연을 반영하여 응용프로그램의 성능을 측정하고, 모든 가상머신이 일으킬 수 있는 스케줄링 지연을 최소화하는 방향으로 CPU 사용 우선순위를 설정하는 기법을 제안하고, 제안한 기법이 스케줄링을 고려하지 않은 방법에 비해 응용 프로그램의 성능을 향상시킴을 보인다.

• Industrial Engineering and Management Systems
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• 제4권1호
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• pp.1-22
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• 2005

This paper presents new scheduling heuristics, namely Mean Progressive Weighted Tardiness Estimator (MPWT) Heuristic Method and modified priority rules with sequence-dependent setup times consideration. These are designed to solve job shop scheduling problems with new performance measures - progressive weighted tardiness penalties. More realistic constraints, which are inter-machine overlapping sequence-dependent setup times, are considered. In real production environments, inter-machine overlapping sequence-dependent setups are significant. Therefore, modified scheduling generation algorithms of active and nondelay schedules for job shop problems with inter-machine overlapping sequence-dependent setup times are proposed in this paper. In addition, new customer-based measures of performance, which are total earliness and progressive weighted tardiness, and total progressive weighted tardiness, are proposed. The objective of the first experiment is to compare the proposed priority rules with the consideration of sequence-dependent setup times and the standard priority rules without setup times consideration. The results indicate that the proposed priority rules with setup times consideration are superior to the standard priority rules without the consideration of setup times. From the second experiment and the third experiment to compare the proposed MPWT heuristic approach with the efficient priority rules with setup times consideration, the MPWT heuristic method is significantly superior to the Batched Apparent Tardiness Cost with Sequence-dependent Setups (BATCS) rule, and other priority rules based on total earliness and progressive weighted tardiness, and total earliness and tardiness.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.482-487
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• 1991

The scheduling algorithm based on the hierarchical scheduling policy is presented for the Job Shop type FMC control, and the simulation results using this hierarchical scheduling policy are with the results which are based on the heuristic scheduling policy. The results show that the hierarchical ling policy is more efficient than the heuristic scheduling policy in either case that there exist machine failures in the FMC or not.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.370-375
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• 1992

This paper deals with the scheduling and simulation for FMC(Flexible Manufacturing Cells). In order to achieve CIM, there is a critical need to link factory level and machine level. The primary functions performed by this link for all jobs issued to the shop floor and cell include short-term scheduling and dynamic operational scheduling. Here, hierarchical control structure is introduced to define these functions. And Intelligent scheduling through expert module is adopted for efficiency of FMC operation. Computer simulation reveals that expert scheduling method is better than heuristics in various performance index.

• 산업경영시스템학회지
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• 제42권1호
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• pp.33-40
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• 2019

This study focuses on a job-shop scheduling problem with the objective of minimizing total tardiness for the job orders that have different due dates and different process flows. We suggest the dispatching rule based scheduling algorithm to generate fast and efficient schedule. First, we show the delay schedule can be optimal for total tardiness measure in some cases. Based on this observation, we expand search space for selecting the job operation to explore the delay schedules. That means, not only all job operations waiting for process but also job operations not arrived at the machine yet are considered to be scheduled when a machine is available and it is need decision for the next operation to be processed. Assuming each job operation is assigned to the available machine, the expected total tardiness is estimated, and the job operation with the minimum expected total tardiness is selected to be processed in the machine. If this job is being processed in the other machine, then machine should wait until the job arrives at the machine. Simulation experiments are carried out to test the suggested algorithm and compare with the results of other well-known dispatching rules such as EDD, ATC and COVERT, etc. Results show that the proposed algorithm, MET, works better in terms of total tardiness of orders than existing rules without increasing the number of tardy jobs.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• Journal of applied mathematics & informatics
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• 제25권1_2호
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• pp.305-314
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• 2007

This paper considers single machine scheduling problems where the processing time of a job increases as a function of its position in the sequence. In this model, the later a given job is scheduled in the sequence, the longer its processing time. It is shown that the optimal schedule may be very different from that of the classical version of the problem. We introduce polynomial solutions for the makespan minimization problem, the sum of completion times minimization problem and the sum of earliness penalties minimization problem. For two resource constrained problems, based on the analysis of the problems, the optimal resource allocation methods are presented, respectively.

• 경영과학
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• 제29권3호
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• pp.157-168
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• 2012

In this study, we consider an order promising problem at parallel machine shops where orders arrive dynamically. We develop methods for the problem, which instantly quote the due-dates of arrived orders. In this study, we first propose methods which can estimate flow times of orders, in which the current and future inventory status as well as the specific scheduling scheme used in the shop are taken into account, and then the due-dates are set by the order promising methods using the estimation results. The quoted due-dates of orders are compared with the actual completion times of those which are obtained from the simulation runs. The series of computational experiments show that the superior performance of the proposed methods in terms of the accuracy of due-date quotation.