• Journal of Multimedia Information System
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• v.2 no.1
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• pp.143-152
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• 2015

Multimedia services in the cloud have become a popular trend in the big data environment. However, how to efficiently schedule a large number of multimedia services in the cloud is still an open and challengeable problem. Current cloud-based scheduling algorithms exist the following problems: 1) the content of the multimedia is ignored, and 2) the cloud platform is a known parameter, which makes current solutions are difficult to utilize practically. To resolve the above issues completely, in this work, we propose a novel distributed multimedia scheduling to satisfy the objectives: 1) Develop a general cloud-based multimedia scheduling model which is able to apply to different multimedia applications and service platforms; 2) Design a distributed scheduling algorithm in which each user makes a decision based on its local information without knowing the others' information; 3) The computational complexity of the proposed scheduling algorithm is low and it is asymptotically optimal in any case. Numerous simulations have demonstrated that the proposed scheduling can work well in all the cloud service environments.

• Journal of Industrial Technology
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• v.22 no.B
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• pp.61-70
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• 2002

The shipbuilding scheduling has many possible alternatives because of its long time horizon and a lot of jobs, so it is required that the scheduling system can generate and search feasible alternatives rapidly. The scheduling in the assembly shop is initiated by allocating, namely loading, the assembly blocks to each shop. In this step, the resource capacities (such as available man/hour, available crane) of each shop must be considered. The result of loading without above consideration can make the scheduling result based on that infeasible or worse one. In this paper, we developed the optimal scheduling system of shipbuilding, specifically for loading and scheduling of assembly block, using ILOG Solver/Scheduler. ILOG Solver/Scheduler is a general-purposed commercial software which supports to find a feasible or optimal solution using object oriented technique and constraints satisfaction programming, given constraints and objectives. Also, in order to enhance the system performance, we conducted various experiments of ILOG search strategies. The experimental results showed that the impact of search strategies is significant.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1265-1278
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• 2017

Dynamic task scheduling is one of the most popular research topics in the cloud computing field. The cloud scheduler dynamically provides VM resources to variable cloud tasks with different scheduling strategies in cloud computing. In this study, we utilized a valid model to describe the dynamic changes of both computing facilities (such as hardware updating) and request task queuing. We built a novel approach called Policy Iteration Scheduling (PIS) to globally optimize the independent task scheduling scheme and minimize the total execution time of priority tasks. We performed experiments with randomly generated cloud task sets and varied the performance of VM resources using Poisson distributions. The results show that PIS outperforms other popular schedulers in a typical cloud computing environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.12
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• pp.2262-2264
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• 2007

In this paper, we propose a distributed opportunistic scheduling scheme for wireless LAN network. Proportional fair scheduling is one of the opportunistic scheduling schemes and used for centralized networks, whereas we design distributed proportional fair scheduling (DPFS). In the proposed DPFS scheme, each receiver estimates channel condition and calculates independently its own priority with probabilistic manner, which can reduce excessive probing overhead required to gather the channel conditions of all receivers. We evaluate the proposed DPFS using extensive simulation and simulation results show that DPFS obtains up to 23% higher throughput than conventional scheduling schemes and has a flexibility to control the fairness and throughput by controlling the system parameter.

• Journal of the Korea Society of Computer and Information
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• v.21 no.2
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• pp.9-16
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• 2016

Recently, many research groups have focused on GPGPUs in order to improve the performance of computing systems. GPGPUs can execute general-purpose applications as well as graphics applications by using parallel GPU hardware resources. GPGPUs can process thousands of threads based on warp scheduling and CTA scheduling. In this paper, we utilize the traditional CTA scheduler to assign a various number of CTAs to SMs. According to our simulation results, increasing the number of CTAs assigned to the SM statically does not improve the performance. To solve the problem in traditional CTA scheduling schemes, we propose a new IPC-based dynamic CTA scheduling scheme. Compared to traditional CTA scheduling schemes, the proposed dynamic CTA scheduling scheme can increase the GPU performance by up to 13.1%.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.123-137
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• 1995

Most of solution methods in scheduling attempt to generate good solutions by either developing algorithms or heuristic rules. However, scheduling problems in the real world require considering more factors such as multiple objectives, different combinations of heuristic rules due to problem characteristics. In this respect, the traditional mathematical a, pp.oach showed limited performance so that new a, pp.oaches need to be developed. Expert system is one of them. When an expert system is developed for scheduling one of the most difficult processes faced could be knowledge acquisition on scheduling heuristics. In this paper we propose a method for the acquisition of knowledge on the selection of scheduling heuristics using Dempster-Shafer Theory(DST). We also show the examples in the multi-objectives environment.

• Journal of Intelligence and Information Systems
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• v.2 no.2
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• pp.69-83
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• 1996

The objective of this research is to develop a knowledge acquisition and refinement method for a multi-objective and multi-decision FMS scheduling problem. A competitive neural network and an inductive learning algorithm are integrated to extract and refine necessary scheduling knowledge from simulation outputs. The obtained scheduling knowledge can assist the FMS operator in real-time to decide multiple decisions simultaneously, while maximally meeting multiple objective desired by the FMS operator. The acquired scheduling knowledge for an FMS scheduling problem is tested by comparing the desired and the simulated values of the multiple objectives. The result show that the knowledge acquisition and refinement method is effective for the multi-objective and multi-decision FMS scheduling problems.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.60.3-60
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• 2001

Usually the static scheduling algorithms such as DMS(Deadline Monotonic Scheduling) or RMS(Rate Monotonic Scheduling) are used for CAN scheduling due to its ease with implementation. However, due to their inherently low utilization of network media, some dynamic scheduling approaches have been studied to enhance the utilization. In case of dynamic scheduling algorithms, two considerations are needed. The one is a priority inversion due to rough deadline encoding into stricted arbitration fields of CAN. The other is an arbitration delay due to the non-preemptive feature of CAN. In this paper, an extended algorithm is proposed from an existing EDS(Earliest Deadline Scheduling) approach of CAN scheduling algorithm having a solution to the priority inverstion ....

• International conference on construction engineering and project management
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• 2015.10a
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• pp.117-121
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• 2015

Construction managers use scheduling methods to improve the outcome of their project. Despite the many obvious advantages of the critical path method (CPM), its use in construction has been limited. Understanding the reasons why CPM is not used as extensively as expected could improve its level of acceptance in the construction industry. The link between construction scheduling methods and the tasks expected to be performed by schedulers has been an on-going concern in the construction industry. This study proposes a task-technology fit model to understand why CPM is not used as extensively as expected in construction scheduling. A task-technology fit model that aims to measure the extent to which a construction scheduling method functionally matches the tasks expected to be performed by the scheduling staff. The model that is proposed is an answer to the lack of proper instruments for evaluating the extent to which scheduling methods are used in the industry.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.631-635
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• 2015

Construction managers use scheduling methods to improve the outcome of their project. In spite of the many obvious advantages of the critical path method (CPM), its use in construction has been limited. Understanding the reasons why CPM is not used as extensively as expected could improve its level of acceptance in the construction industry. The link between construction scheduling methods and the capabilities of the scheduling staff has been an on-going concern in the construction industry. This study proposes a staffing-technology fit model to understand why CPM is not used as extensively as expected in construction scheduling. A staffing-technology fit model that aims to measure the extent to which a construction scheduling method matches the staff's experience, know-how and capabilities. The model that is proposed is an answer to the lack of proper instruments for evaluating the extent to which scheduling methods are used in the industry.