• 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.13 no.6
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• pp.2824-2837
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• 2019

Container technologies are widely used in infrastructures to deploy and manage applications in cloud computing environment. As containers are light-weight software, the cluster of cloud applications can easily scale up or down to provide Internet-based services. Container-based applications can well deal with fluctuate workloads by dynamically adjusting physical resources. Current works of scheduling applications often construct applications' performance models with collected historical training data, but these works with static models cannot self-adjust physical resources to meet the dynamic requirements of cloud computing. Thus, we propose a self-adaptive automatic container scheduling framework AutoScale for cloud applications, which uses a feedback-based approach to adjust physical resources by extending, contracting and migrating containers. First, a queue-based performance model for cloud applications is proposed to correlate performance and workloads. Second, a fuzzy Kalman filter is used to adjust the performance model's parameters to accurately predict applications' response time. Third, extension, contraction and migration strategies based on predicted response time are designed to schedule containers at runtime. Furthermore, we have implemented a framework AutoScale with container scheduling strategies. By comparing with current approaches in an experiment environment deployed with typical applications, we observe that AutoScale has advantages in predicting response time, and scheduling containers to guarantee that response time keeps stable in fluctuant workloads.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3663-3679
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• 2020

Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.3
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• pp.12-18
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• 2003

A web crawler is used widely in a variety of Internet applications such as search engines. As the Internet continues to grow, high performance web crawlers become more essential. Crawl scheduling which manages the allocation of web pages to each process for downloading documents is one of the important issues. In this paper, we identify issues that are important and challenging in the crawl scheduling. To address the issues, we propose a dynamic owl scheduling framework and subsequently a system architecture for a web crawler subject to the framework. This paper presents the architecture of a web crawler with dynamic scheduling support. The result of our preliminary performance evaluation made to the proposed crawler architecture is also presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3096-3117
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• 2013

To execute the performance driven Grid applications, an effective and scalable workflow scheduling is seen as an essential. To optimize cost & makespan, in this paper, we propose a Scalable Cost-Time Trade-off (SCTT) model for scheduling workflow tasks. We have developed a heuristic algorithm known as Scalable Cost-Time Trade-off Scheduling (SCTTS) with a lower runtime complexity based on the proposed SCTT model. We have compared the performance of our proposed approach with other heuristic and meta-heuristic based scheduling strategies using simulations. The results show that the proposed approach improves performance and scalability with different workflow sizes, task parallelism and heterogeneous resources. This method, therefore, outperforms other methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.559-573
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• 2016

In this paper, we investigate the problem of achieving proportional fairness in hierarchical wireless sensor networks. Combining clustering formulation and scheduling, we maximize total bandwidth utility for proportional fairness while controlling the power consumption to a minimum value. This problem is decomposed into two sub-problems and solved in two stages, which are Clustering Formulation Stage and Scheduling Stage, respectively. The above algorithm, called CSPF_PC, runs in a network formulation sequence. In the Clustering Formulation Stage, we let the sensor nodes join to the cluster head nodes by adjusting transmit power in a greedy strategy; in the Scheduling Stage, the proportional fairness is achieved by scheduling the time-slot resource. Simulation results verify the superior performance of our algorithm over the compared algorithms on fairness index.

• Journal of Internet Computing and Services
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• v.6 no.2
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• pp.25-35
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• 2005

The Rate Monotonic(RM) scheduling algorithm and Earliest Deadline First(EDF) scheduling algorithm are normally used in Real-Time scheduling algorithm. In those scheduling algorithm, we could predict the performance possibility with total utilization value of task group. But. it had problems with prediction of the boundedness in individual task when the utilization value was over in temporary task. In this paper, the suggested scheduling algorithm can predict task when the utilization value was over and it suggested the method of predicting scheduling possibility based on the utilization value of individual task as well. it predicted the boundedness of scheduling possibility test through simulation In Real-Time scheduling algorithm and analyzed the result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4103-4121
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• 2018

Quality of Service (QoS) awareness is recognized as a key point for the success of Internet of Things (IOT).Realizing the full potential of the Internet of Things requires, a real-time task scheduling algorithm must be designed to meet the QoS need. In order to schedule tasks with diverse QoS requirements in cloud environment efficiently, we propose a task scheduling strategy based on dynamic priority and load balancing (DPLB) in this paper. The dynamic priority consisted of task value density and the urgency of the task execution, the priority is increased over time to insure that each task can be implemented in time. The scheduling decision variable is composed of time attractiveness considered earliest completion time (ECT) and load brightness considered load status information which by obtain from each virtual machine by topic-based publish/subscribe mechanism. Then sorting tasks by priority and first schedule the task with highest priority to the virtual machine in feasible VMs group which satisfy the QoS requirements of task with maximal. Finally, after this patch tasks are scheduled over, the task migration manager will start work to reduce the load balancing degree.The experimental results show that, compared with the Min-Min, Max-Min, WRR, GAs, and HBB-LB algorithm, the DPLB is more effective, it reduces the Makespan, balances the load of VMs, augments the success completed ratio of tasks before deadline and raises the profit of cloud service per second.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.161-166
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• 1999

Virtual Manufacturing System(VMS) is a computer model that represents the precise and whole structure of manufacturing systems and simulates their physical and logical behavior in operation. In this paper, a real time simulation for the virtual factory is proposed to integrate a process planning with scheduling under distributed environments. In order to communicate the information under distributed environments, we use a server/client concept using socket program and internet.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4856-4871
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• 2015

Backpressure based scheduling has been considered as a promising technique for improving the throughput of a wide range of communication networks. However, this scheduling technique has not been well studied for heterogeneous wireless networks. In this paper, we propose a virtual-queue based backpressure scheduling (VQB) algorithm for heterogeneous multi-hop wireless networks. The VQB algorithm introduces a simple virtual queue for each flow at a node for backpressure scheduling, whose length depends on the cache size of the node. When calculating flow weights and making scheduling decisions, the length of a virtual queue is used instead of the length of a real queue. We theoretically prove that VQB is throughput-optimal. Simulation results show that the VQB algorithm significantly outperforms a classical backpressure scheduling algorithm in heterogeneous multi-hop wireless networks in terms of the packet delivery ratio, packet delivery time, and average sum of the queue lengths of all nodes per timeslot.