• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2257-2285
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• 2022

Cyber-physical systems (CPS) have been growing exponentially due to improved cloud-datacenter infrastructure-as-a-service (CDIaaS). Incremental expandability (scalability), Quality of Service (QoS) performance, and reliability are currently the automation focus on healthy Tier 4 CDIaaS. However, stable QoS is yet to be fully addressed in Cyber-physical data centers (CP-DCS). Also, balanced agility and flexibility for the application workloads need urgent attention. There is a need for a resilient and fault-tolerance scheme in terms of CPS routing service including Pod cluster reliability analytics that meets QoS requirements. Motivated by these concerns, our contributions are fourfold. First, a Distributed Non-Recursive Cloud Model (DNRCM) is proposed to support cyber-physical workloads for remote lab activities. Second, an efficient QoS stability model with Routh-Hurwitz criteria is established. Third, an evaluation of the CDIaaS DCN topology is validated for handling large-scale, traffic workloads. Network Function Virtualization (NFV) with Floodlight SDN controllers was adopted for the implementation of DNRCM with embedded rule-base in Open vSwitch engines. Fourth, QoS evaluation is carried out experimentally. Considering the non-recursive queuing delays with SDN isolation (logical), a lower queuing delay (19.65%) is observed. Without logical isolation, the average queuing delay is 80.34%. Without logical resource isolation, the fault tolerance yields 33.55%, while with logical isolation, it yields 66.44%. In terms of throughput, DNRCM, recursive BCube, and DCell offered 38.30%, 36.37%, and 25.53% respectively. Similarly, the DNRCM had an improved incremental scalability profile of 40.00%, while BCube and Recursive DCell had 33.33%, and 26.67% respectively. In terms of service availability, the DNRCM offered 52.10% compared with recursive BCube and DCell which yielded 34.72% and 13.18% respectively. The average delays obtained for DNRCM, recursive BCube, and DCell are 32.81%, 33.44%, and 33.75% respectively. Finally, workload utilization for DNRCM, recursive BCube, and DCell yielded 50.28%, 27.93%, and 21.79% respectively.

• Journal of KIISE
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• v.42 no.10
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• pp.1195-1206
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• 2015

For the management of a knowledge system, systems that automatically infer and manage scalable knowledge are required. Most of these systems use ontologies in order to exchange knowledge between machines and infer new knowledge. Therefore, approaches are needed that infer new knowledge for scalable ontology. In this paper, we propose an approach to perform rule based reasoning for scalable SHIF ontologies in a spark framework which works similarly to MapReduce in distributed memories on a cluster. For performing efficient reasoning in distributed memories, we focus on three areas. First, we define a data structure for splitting scalable ontology triples into small sets according to each reasoning rule and loading these triple sets in distributed memories. Second, a rule execution order and iteration conditions based on dependencies and correlations among the SHIF rules are defined. Finally, we explain the operations that are adapted to execute the rules, and these operations are based on reasoning algorithms. In order to evaluate the suggested methods in this paper, we perform an experiment with WebPie, which is a representative ontology reasoner based on a cluster using the LUBM set, which is formal data used to evaluate ontology inference and search speed. Consequently, the proposed approach shows that the throughput is improved by 28,400% (157k/sec) from WebPie(553/sec) with LUBM.

• The KIPS Transactions:PartA
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• v.19A no.3
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• pp.139-146
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• 2012

In the server cluster environment, one of the ways saving energy is to control server's power according to traffic conditions. This is to determine the ON/OFF state of servers according to energy usage of data center and each server. To do this, we need a way to estimate each server's energy. In this paper, we use a software-based power consumption estimation model because it is more efficient than the hardware model using power meter in terms of energy and cost. The traditional software-based power consumption estimation model has a drawback in that it doesn't know well the computing status of servers because it uses only the idle status field of CPU. Therefore it doesn't estimate consumption power effectively. In this paper, we present a CPU field based power consumption estimation model to estimate more accurate than the two traditional models (CPU/Disk/Memory utilization based power consumption estimation model and CPU idle utilization based power consumption estimation model) by using the various status fields of CPU to get the CPU status of servers and the overall status of system. We performed experiments using 2 PCs and compared the power consumption estimated by the power consumption model (software) with that measured by the power meter (hardware). The experimental results show that the traditional model has about 8-15% average error rate but our proposed model has about 2% average error rate.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.22-30
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• 2008

Warehouse has many changes for the stocks by entering and taking. Management about the stocks is important. To manage, sensor network based on RFID is deployed in warehouse. Sensor network uses fluster based on energy and hierarchical routing. Supporting mobility between clusters makes guarantee connectivity for mobile nodes.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.69-76
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• 2020

This paper presents direction for efficient software education. Due to the impact of the Fourth Industrial Revolution, the whole world is interested in software education. However, simply teaching how to code is not software education. The thinking abilities used in coding for software implementation are even more important. Therefore, computational thinking is getting great attention. Several institutions suggest factors for computational thinking and encourage to teach in a relevant way based on the suggestion. In this study, the verification of the factors they suggested was conducted through a questionnaire. The total of 419 freshman students of the College of Social Sciences who were taking "Computational Thinking and Software Coding" class participated in the survey at the beginning and the end of the semester. We first analyzed Wing's proposal that summarized the concept of computational thinking, and reviewed the proposal of ISTE (International Society for Technology in Education) for defining computational thinking factors for coding education, also checked on the suggestion of Google for factors necessary for software coding. As a result of research analysis, this paper suggests a direction for efficient software education.

• Journal of Environmental Health Sciences
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• v.38 no.5
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• pp.369-379
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• 2012

Objectives: Spatial analysis is useful for understanding complicated causal relationships. This paper focuses trends and appling methods for spatial analysis associated with environmental asbestos exposure. Methods: Literature review and reflection of experience of authors were conducted to know academic background of spatial analysis, appling methods on epidemiology and asbestos exposure. Results: Spatial analysis based on spatial autocorrelation provides a variety of methods through which to conduct mapping, cluster analysis, diffusion, interpolation, and identification. Cause of disease occurrence can be investigated through spatial analysis. Appropriate methods can be applied according to contagiousness and continuity. Spatial analysis for asbestos exposure source is needed to study asbestos related diseases. Although a great amount of research has used spatial analysis to study exposure assessment and distribution of disease occurrence, these studies tend to focus on the construction of a thematic map without different forms of analysis. Recently, spatial analysis has been advanced by merging with web tools, mobile computing, statistical packages, social network analysis, and big data. Conclusions: Because the trend in spatial analysis has evolved from simple marking into a variety of forms of analyses, environmental researchers including asbestos exposure study are required to be aware of recent trends.

• KIISE Transactions on Computing Practices
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• v.21 no.6
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• pp.436-441
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• 2015

There are many types of clustering algorithms such as centroid, hierarchical, or density-based methods. Each algorithm has unique data grouping principles, which creates different varieties of clusters. Ordering Points To Identify the Clustering Structure (OPTICS) is a well-known density-based algorithm to analyze arbitrary shaped and varying density clusters, but the obtained clusters only correlate loosely. Hierarchical agglomerative clustering (HAC) reveals a hierarchical structure of clusters, but is unable to clearly find non-convex shaped clusters. In this paper, we provide a novel hierarchy generation framework and application which can aid users by combining the advantages of the two clustering methods.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.83-97
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• 2015

IEEE 802.15.4 is a global standard designed for emerging applications in low-rate wireless personal area networks (LR-WPANs). The standard provides beneficial features, such as a beacon-enabled mode and guaranteed time slots for realtime data delivery. However, how to optimally operate those features is still an open issue. For the optimal operation of the features, this paper proposes a holistic optimization method that jointly optimizes three cross-related problems: cluster-tree construction, nodes' power configuration, and duty-cycle scheduling. Our holistic optimization method provides a solution for those problems so that all the real-time packets can be delivered within their deadlines in the most energy-efficient way. Our simulation study shows that compared to existing methods, our holistic optimization can guarantee the on-time delivery of all real-time packets while significantly saving energy, consequently, significantly increasing the lifetime of the network. Furthermore, we show that our holistic optimization can be extended to take advantage of the spatial reuse of a radio frequency resource among long distance nodes and, hence, significantly increase the entire network capacity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.382-385
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• 2011

The sensor network technology for core technology of ubiquitous computing is in the spotlight recently, the research on sensor network is proceeding actively which is composed many different sensor node. One of the important condition for design of sensor node is to extend for network life which is to minimize power-consumption under the limited resources of sensor network. This study suggest routing protocol that was used second level cluster structure to reduce power-consumption of sensor node. the first level use the previous routing protocol under the LEACH, second level decide to transmit or not by comparision of data value for Effective Usage, reduce the unnecessary power-consumption.

• Proceedings of the KSME Conference
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• 2002.08a
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• pp.55-58
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• 2002

Parallel finite element code has been recently developed for the analysis of the incompressible Wavier-Stokes equations using domain decomposition method. Metis and MPI libraries are used for the domain partitioning of an unstructured mesh and the data communication between sub-domains, respectively. For unsteady computation of the incompressible Navier-Stokes equations, 4-step splitting method is combined with P1P1 finite element formulation. Smagorinsky and dynamic model are implemented for the simulation of turbulent flows. For the validation performance-estimation of the developed parallel code, three-dimensional Laplace equation has been solved. It has been found that the speed-up of 40 has been obtained from the present parallel code fir the bench mark problem. Lastly, the turbulent flows around the MIRA model and Tiburon model have been solved using 32 processors on IBM SMP cluster and unstructured mesh. The computed drag coefficient agrees better with the existing experiment as the mesh resolution of the region increases, where the variation of pressure is severe.