• Journal of Internet Computing and Services
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• v.7 no.5
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• pp.13-24
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• 2006

Context aware computing is an emerging paradigm to achieve ubiquitous computing environments by enabling computer systems to understand their situational contexts. A context aware system uses context to provide relevant information and services to the user depending on the user's task. In this paper, we propose an ontology based context aware modeling methodology that transmits low level contexts acquired by directly accessing various sensors in the physical environments to high level contexts. With these high level contexts, context aware application can provides proactive and intelligent services using ECA (Event Condition Action) rules. We implemented a presentation service in smart office environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.344-349
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• 1999

As the computing environment in a typical organization has changed rapidly an Internet-connected client/server system, new tools to measure the utilization of such system are becoming increasingly important. Most of contemporary performance measuring tools are based on the old computing models, such as the centralized server model or the networked server model. In this paper, we present a tool, railed SysWatcher, which is based on the latest computing paradigm.. What is to be analyzed in the new computing environment and flow can achieve it by using SysWatcher, as well as its detailed design concept, are presented.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.555-566
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• 2012

The cloud environment makes it possible to analyze large data sets in a scalable computing infrastructure. In the bioinformatics field, the applications are composed of the complex workflow tasks, which require huge data storage as well as a computing-intensive parallel workload. Many approaches have been introduced in distributed solutions. However, they focus on static resource provisioning with a batch-processing scheme in a local computing farm and data storage. In the case of a large-scale workflow system, it is inevitable and valuable to outsource the entire or a part of their tasks to public clouds for reducing resource costs. The problems, however, occurred at the transfer time for huge dataset as well as there being an unbalanced completion time of different problem sizes. In this paper, we propose an adaptive resource-provisioning scheme that includes run-time data distribution and collection services for hiding the data transfer time. The proposed adaptive resource-provisioning scheme optimizes the allocation ratio of computing elements to the different datasets in order to minimize the total makespan under resource constraints. We conducted the experiments with a well-known sequence alignment algorithm and the results showed that the proposed scheme is efficient for the cloud environment.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.127-134
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• 2009

Recently, much research have been done on ubiquitous computing models in advanced countries as well as in Korea. Ubiquitous computing is defined as a computing environment that isn't bounded by time and space. Different kinds of computers are embedded in artifacts, devices, and environment, thus people can be connected everywhere and every time. To recognize user's emotion, facial expression, temperature, humidity, weather, and lightning factors are used for building ontology. Ontology Web Language (OWL) is adopted to implement ontology and Jena is used as an emotional inference engine. The context-awareness service infrastructure suggested in this research can be divided into several modules by their functions.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.27-32
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• 2018

IoT technology is developing with various application areas in $4^{th}$ Industrial revolution. There are many users using the application services. Sensing data from various environment need to be transferred to cloud computing storage and store in the cloud storage. However, physical distance from the end node to cloud computing storage is far away, and it is not efficient to transfer data from sensors and store the sensing data in the cloud storage whenever sensing data happen. Therefore, Fog computing is proposed to solve these problems which can process and store the sensing data. However, Fog computing is new emerging technology, there is no standard security model and requirements. This research proposes to security requirements and security model for Fog computing to establish a secure and efficient cloud computing environment.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.4
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• pp.239-250
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• 2013

Context-aware mobile computing has become the primary approach to realize automatic, autonomous, and user-centric computing in the context of largely increasing the amount of mobile devices used that embed available sensors. However, designing an inference engine nonetheless requires the tasks of analyzing contexts, situations that can be inferred, etc. Moreover, a mobile device has limited resources and limited computation capability, which results in recognizing the common sense of its unsuitable environment for processing inference. Hence, we propose context-situation reasoning elements and their formal models in this paper, and we verify the formal models' applicability by applying them to an example. Finally, we design and implement an inference engine that realize the context-situation inference elements in computing environment, and we experiment an example by using the proposed inference engine to verify applicability and reusability of the inference engine.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.21-26
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• 2008

It was already realized at a current technological level of home network systems that the systems recognizes a user's simple order and carry out the order in the ubiquitous computing environment. However home is not a simple environment consisting into a large number of family members, so various order and situation would be needed accordingly. From now on we need to reach the technological level to infer that how is the user's behavior patterns and what kinds of service is the fittest to user who belong to the ubiquitous computing environment by using the result of the context interpreter. In this regards, active inferred-model needs to be suggested upgrading user's command into one step more higher level than the simple one adapting diversified feature. This study would like to suggest this active model recognizing context, which is user's environmental information applying basic network and inferring Context-based Service that user wants through the recognized result This study proposes a new method that can infer the user's desire in ubiquitous computing environment. First of all, we define a context as user's information of ubiquitous computing environment situation that user belongs to and we classify the context into 4W1H(Where, Who, When, What) formats. We construct Bayesian network and put the factor of context use as Bayesian network nodes. As a result, we can infer the user's behavior pattern and most proper service for user in the intelligent space from the probabilistic result of Bayesian network.

• International journal of advanced smart convergence
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• v.4 no.2
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• pp.109-119
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• 2015

The interaction between wireless sensors such as Internet of Things (IoT) and Cloud is a new paradigm of communication virtualization to overcome resource and efficiency restriction. Cloud computing provides unlimited platform, resources, services and also covers almost every area of computing. On the other hand, Wireless Sensor Networks (WSN) has gained attention for their potential supports and attractive solutions such as IoT, environment monitoring, healthcare, military, critical infrastructure monitoring, home and industrial automation, transportation, business, etc. Besides, our virtual groups and social networks are in main role of information sharing. However, this sensor network lacks resource, storage capacity and computational power along with extensibility, fault-tolerance, reliability and openness. These data are not available to community groups or cloud environment for general purpose research or utilization yet. If we reduce the gap between real and virtual world by adding this WSN driven data to cloud environment and virtual communities, then it can gain a remarkable attention from all over, along with giving us the benefit in various sectors. We have proposed a Pub/Sub-based sensor virtualization framework Cloud environment. This integration provides resource, service, and storage with sensor driven data to the community. We have virtualized physical sensors as virtual sensors on cloud computing, while this middleware and virtual sensors are provisioned automatically to end users whenever they required. Our architecture provides service to end users without being concerned about its implementation details. Furthermore, we have proposed an efficient content-based event matching algorithm to analyze subscriptions and to publish proper contents in a cost-effective manner. We have evaluated our algorithm which shows better performance while comparing to that of previously proposed algorithms.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.9
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• pp.189-196
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• 2020

Recently, GPU cloud computing technology applying GPU(Graphics Processing Unit) devices to virtual machines is widely used in the cloud environment. In a cloud environment, GPU devices assigned to virtual machines can perform operations faster than CPUs through massively parallel processing, which can provide many benefits when operating high-performance computing services in a variety of fields in a cloud environment. In a cloud environment, a GPU device can help improve the performance of a virtual machine, but the virtual machine scheduler, which is based on the CPU usage time of a virtual machine, does not take into account GPU device usage time, affecting the performance of other virtual machines. In this paper, we test and analyze the performance degradation of other virtual machines due to the virtual machine that performs GPGPU(General-Purpose computing on Graphics Processing Units) task in the direct path based GPU virtualization environment, which is often used when assigning GPUs to virtual machines in cloud environments. Then to solve this problem, we propose a GPGPU task management method for a virtual machine.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.162-169
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• 2022

In a dynamically changing time-varying network environment, the optimal moving pattern of edge devices can be applied to distributing computing resources to edge cloud servers or deploying new edge servers in the FEC(Fog/Edge Computing) environment. In addition, this can be used to build an environment capable of efficient computation offloading to alleviate latency problems, which are disadvantages of cloud computing. This paper proposes an algorithm to extract the optimal moving pattern by analyzing the moving path of multiple edge devices requiring application services in an arbitrary spatio-temporal environment based on frequency. A comparative experiment with A^* and Dijkstra algorithms shows that the proposed algorithm uses a relatively fast execution time and less memory, and extracts a more accurate optimal path. Furthermore, it was deduced from the comparison result with the A^* algorithm that applying weights (preference, congestion, etc.) simultaneously with frequency can increase path extraction accuracy.