• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.409-416
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• 2000

In the shared-memory mutiprocessor systems, shared processing techniques such as time-sharing, space¬sharing, and gang-scheduling are used to improve the overall system utilization for the parallel operations. Recently, LLPC(Loop-Level Process Control) allocation technique was proposed. It dynamically adjusts the needed number of processors for the execution of the parallel code portions based on the current system load in the given job. This method allocates as many available processors as possible, and does not save any processors for the parallel sections of other later-arriving applications. To solve this problem, in this paper, we propose a new processor allocation technique called FPA(Fuzzy Processor Allocation) that dynamically adjusts the number of processors by fuzzifYing the amounts ofueeded number of processors, loads, and estimated execution times of job. The proposed method provides the maximum possibility of the parallism of each job without system overload. We compare the performances of our approaches with the conventional results. The experiments show that the proposed method provides a better performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1071-1090
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• 2016

Most of scheduling methods in the Grid only consider one special attribute of the resource or one aspect of QoS (Quality of Service) of the job. In this paper, we focus on the problem that how to consider two aspects simultaneously. Based on the requirements of the jobs and the attributes of the resources, jobs are categorized into three kinds: CPU-overload, memory-overload, and bandwidth-overload jobs. One job may belong to different kinds according to different attributes. We schedule the jobs in different categories in different orders, and then propose a scheduling method-MTS (multiple attributes scheduling method) to schedule Grid resources. Based on the comparisons between our method, Min-min, ASJS (Adaptive Scoring Job Scheduling), and MRS (Multi-dimensional Scheduling) show: (1) MTS reduces the execution time more than 15% to other methods, (2) MTS improves the number of the finished jobs before the deadlines of the jobs, and (3) MTS enhances the file size of transmitted files (input files and output files) and improves the number of the instructions of the finished jobs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.39-60
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• 2017

In the Cloud, for the scheduling of parallel jobs, there are many tasks in a job and those tasks are executed concurrently on different VMs (Visual machines), where each task of the job will be executed synchronously. The goal of scheduling is to reduce the execution time and to keep the fairness between jobs to prevent some jobs from waiting more time than others. We propose a Cloud model which has multiple Clouds, and under this model, jobs are in different lists according to the waiting time of the jobs and every job has different parallelism. At the same time, a new method-ZOMT (the scheduling parallel tasks based on ZERO-ONE scheduling with multiple targets) is proposed to solve the problem of scheduling parallel jobs in the Cloud. Simulations of ZOMT, AFCFS (Adapted First Come First Served), LJFS (Largest Job First Served) and Fair are executed to test the performance of those methods. Metrics about the waiting time, and response time are used to test the performance of ZOMT. The simulation results have shown that ZOMT not only reduces waiting time and response time, but also provides fairness to jobs.

• Journal of Internet Computing and Services
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• v.7 no.6
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• pp.87-103
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• 2006

Advances in computer and network technology provide new computing environment that were only possible with supercomputers before. In order to provide the environment, a distributed runtime system has to be provided, but most of the conventional distributed runtime systems lack in providing dynamic and flexible system reconfiguration depending on workload variance, due to a static architecture of fixed master node and slave working nodes. This paper proposes and implements a new model for distributed job allocation and management which is a distributed runtime system is P2P environment for flexible and dynamic system reconfiguration. The implemented systems enables job program transfer and management, remote compile and execution among cooperative developers based on P2P standard protocol Jxta platform. Since it makes dynamic and flexible system reconfiguration possible, the proposed method has some advantages in that it can collect and utilize idle computing resources immediately at a needed time for distributed job processing. Moreover, the implemented system's effectiveness and performance increase are shown by applying and processing the crawler jobs, in a distributed way, for collecting a large amount of data needed for internet search.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3603-3618
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• 2022

The offloading method is important when there are multiple mobile nodes and multiple edge servers. In the environment, those mobile nodes connect with edge servers with different bandwidths, thus taking different time and energy for offloading tasks. Considering the system load of edge servers and the attributes (the number of instructions, the size of files, deadlines, and so on) of tasks, the energy-aware offloading problem becomes difficult under our mobile edge environment (MCE). Most of the past work mainly offloads tasks by judging where the job consumes less energy. But sometimes, one task needs more energy because the preferred edge servers have been overloaded. Those methods always do not pay attention to the influence of the scheduling on the future tasks. In this paper, first, we try to execute the job locally when the job costs a lower energy consumption executed on the MD. We suppose that every task is submitted to the mobile server which has the highest bandwidth efficiency. Bandwidth efficiency is defined by the sending ratio, the receiving ratio, and their related power consumption. We sort the task in the descending order of the ratio between the energy consumption executed on the mobile server node and on the MD. Then, we give a "suffrage" definition for the energy consumption executed on different mobile servers for offloading tasks. The task selects the mobile server with the largest suffrage. Simulations show that our method reduces the execution time and the related energy consumption, while keeping a lower value in the number of uncompleted tasks.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.6
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• pp.337-343
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• 2006

The Stack Resource Policy (SRP) is a real-time synchronization protocol with some distinct properties. One of such properties is early blocking; the execution of a job is delayed instead of being blocked when requesting shared resources. If SRP is used with dynamic priority scheduling such as Earliest Deadline First (EDF), the early blocking requires that a scheduler should select the highest-priority job among the jobs that will not be blocked, incurring runtime overhead. In this paper, we analyze the runtime overhead of EDF scheduling when SRP is used. We find out that the overhead of job search using the conventional implementations of ready queue and job search algorithms becomes serious as the number of jobs increases. To solve this problem, we propose an alternative data structure for the ready queue and an efficient job-search algorithm with O([log$_2n$]) time complexity.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.6
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• pp.465-476
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• 2005

In this paper, we propose a new dynamic reconfiguration method using application-level checkpointing in a grid computing environment with Cactus and Globus. The existing dynamic reconfiguration methods have been dependent on a specific hardware and operating system. But the proposed method performs a dynamic reconfiguration without supporting specific hardwares and operating systems and, an application is programmed without considering a dynamic reconfiguration. In the proposed method, the job starts with an initial configuration of Computing resources and the job restarts including new resources dynamically found at run-time. The proposed method determines whether to include the newly found idle sites by considering processor performance and available memory of the sites. Our method writes the intermediate results of the job on the disks using system-independent application-level checkpointing for real-time visualization during the job runs. After reconfiguring idle sites and idle processors newly found, the job resumes using checkpointing files. The proposed dynamic reconfiguration method is proved to be valid by decreasing total execution time In K*Grid.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3126-3145
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• 2014

Cloud computing is a computing paradigm in which users can rent computing resources from service providers according to their requirements. A spot instance in cloud computing helps a user to obtain resources at a lower cost. However, a crucial weakness of spot instances is that the resources can be unreliable anytime due to the fluctuation of instance prices, resulting in increasing the failure time of users' job. In this paper, we propose a Genetic Algorithm (GA)-based workflow scheduling scheme that can find the optimal task size of each instance in a spot instance-based cloud computing environment without increasing users' budgets. Our scheme reduces total task execution time even if an out-of-bid situation occurs in an instance. The simulation results, based on a before-and-after GA comparison, reveal that our scheme achieves performance improvements in terms of reducing the task execution time on average by 7.06%. Additionally, the cost in our scheme is similar to that when GA is not applied. Therefore, our scheme can achieve better performance than the existing scheme, by optimizing the task size allocated to each available instance throughout the evolutionary process of GA.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.563-570
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• 2004

Genetic algorithms have been successfully applied to various optimization problems belonging to NP-hard problems. The sequential ordering problems(SOP) and the job shop scheduling problems(JSP) are well-known NP-hard problems with strong influence on industrial applications. Both problems share some common properties in that they have some imposed precedence constraints. When genetic algorithms are applied to this kind of problems, it is desirable for genetic operators to be designed to produce chromosomes satisfying the imposed precedence constraints. Several genetic operators applicable to such problems have been proposed. We call such genetic operators precedence-preserving genetic operators. This paper presents three existing precedence-preserving genetic operators: Precedence -Preserving Crossover(PPX), Precedence-preserving Order-based Crossover (POX), and Maximum Partial Order! Arbitrary Insertion (MPO/AI). In addition, it proposes two new operators named Precedence-Preserving Edge Recombination (PPER) and Multiple Selection Precedence-preserving Order-based Crossover (MSPOX) applicable to such problems. It compares the performance of these genetic operators for SOP and JSP in the perspective of their solution quality and execution time.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.8
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• pp.753-757
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• 2008

In this paper, we propose a new scheduling strategy for dynamic programming in Grid environment. The key idea of this scheme is to reduce the execution time of a job by dividing the dynamic table based on the locality of table and allocating jobs to nodes which minimize network latency. This scheme obtains optimal concurrency by constructing the dynamic table using a distributed top down method. Through simulation, we show that the proposed Grid strategy improves the performance of Grid environment compared to previous branch-bound strategies.