• The KIPS Transactions:PartA
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• v.11A no.6
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• pp.419-424
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• 2004

Mobile Internet using mobile devices provides Portability to users and is Spreading throughout out lives. But Practical use of the mobile Internet is insufficient due to it's fundamental limitations. Especially, mobile resources are not sufficient because they are not closely connected under current mobile Internet services which are provided with pre-designed structure. For these reasons it will take a lot more time and effort for mobile Internet to grow as a popular service. In this paper, we are focusing to overcome the local limitation and to enhance the 'Sharing of Mobile Resources' by expanding mutual connectivity between resource objects scattered over the mobile Internet environment. To archive this, we propose the MRSS(Mobile Resources Sharing System). MRSS automatically converts single request from user to multiple instructions based on 'Context-Awareness' to search for proper information. Using MRSS, we can expect 'Mobile Ubiquitous Computing' environment which users can reach to information anywhere, anytime.

• KNOM Review
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• v.23 no.1
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• pp.43-50
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• 2020

As General Purpose Graphics Processing Unit (GPGPU) recently plays an essential role in high-performance computing, several cloud service providers offer GPU service. Most cluster orchestration platforms in a cloud environment using containers allocate the integer number of GPU to jobs and do not allow a node shared with other jobs. In this case, resource utilization of a GPU node might be low if a job does not intensively require either many cores or large size of memory in GPU. GPU virtualization brings opportunities to realize kernel concurrency and share resources. However, performance may vary depending on characteristics of applications running concurrently and interference among them due to resource contention on a node. This paper proposes GPU container co-execution framework with multiple server creation and execution based on Kubernetes, container orchestration platform for measuring interference which may be occurred by sharing GPU resources. Performance changes according to scheduling policies were investigated by executing several jobs on GPU. The result shows that optimal scheduling is not possible only considering GPU memory and computing resource usage. Interference caused by co-execution among applications is measured using the framework.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.3
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• pp.86-96
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• 2008

In ubiquitous environments, invisible devices and software are connected to one another to provide convenient services to users. In order to provide such services, we must have mobile devices that connect users and services. However, the types of available services have thus far been limited due to the limited resources of mobile devices. This paper proposes a solution to the resource limitation problem of mobile devices by presenting a context-based collaboration system that allows mobile devices to share various nearby resources. Our system has a feature to enable personalized resource sharing by dynamically re-configuring user's preference and resource information.

• Journal of Korean Society for Quality Management
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• v.28 no.4
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• pp.29-46
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• 2000

Advent of the distributed computing has contributed to the rapid distribution of information technology throughout the organization. While powerful and user-friendly information technology are more available to end users, managing the distributed, heterogeneous IT environment has become a serious problem for corporate IT managers. Integrated Control Architecture (ICA) enables the monitoring and controlling of the dispersed information resources for effective enterprise-wide information resource management. This paper empirically examines the propositions that the level of information resource control is positively related to IS effectiveness measured in user satisfaction. Measures are developed and validated for the control of information resource object such as data, application, platform, and control of relationships among such objects. Results from a study of 130 organizations support that the organizations with high information resource control exhibit user satisfaction.

• Journal of Internet Computing and Services
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• v.4 no.6
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• pp.13-24
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• 2003

Grid technology requires use of geographically distributed resources from multiple domains. Resource monitoring services or tools consisting sensors or agents will run on many systems to find static resource information (such as architecture vendor, OS name and version, MIPS rate, memory size, CPU capacity, disk size, and NIC information) and dynamic resource information (CPU usage, network usage(bandwidth, latency), memory usage, etc.). Thus monitoring itself may cause system overhead. This paper proposes the optimal monitoring interval to reduce the cost of monitoring services and the dynamic monitoring interval to measure monitoring events accurately. By employing two features, we find out unnecessary system overhead is significantly reduced and accuracy of events is still acquired.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1302-1320
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• 2015

In cloud, everything can be provided as a service wherein a large number of users submit their jobs and wait for their services. hus, scheduling plays major role for providing the resources efficiently to the submitted jobs. The brainwave of the proposed ork is to improve user satisfaction, to balance the load efficiently and to bolster the resource utilization. Hence, this paper roposes an Adaptive Multilevel Scheduling System (AMSS) which will process the jobs in a multileveled fashion. The first level ontains Preprocessing Jobs with Multi-Criteria (PJMC) which will preprocess the jobs to elevate the user satisfaction and to itigate the jobs violation. In the second level, a Deadline Based Dynamic Priority Scheduler (DBDPS) is proposed which will ynamically prioritize the jobs for evading starvation. At the third level, Contest Mapping Jobs with Virtual Machine (CMJVM) is roposed that will map the job to suitable Virtual Machine (VM). In the last level, VM Scheduler is introduced in the two-tier VM rchitecture that will efficiently schedule the jobs and increase the resource utilization. These contributions will mitigate job iolations, avoid starvation, increase throughput and maximize resource utilization. Experimental results show that the performance f AMSS is better than other algorithms.

• Journal of Internet Computing and Services
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• v.9 no.1
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• pp.179-186
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• 2008

The access to web applications has continuously evolved toward the adoption of content centric interfaces. Services and information items are much of user's concern than web sites. For an efficient accommodation of increasing needs of content oriented operations, the scope of domain names is extended in two directions. A naming scheme is derived that is suitable for resource level access units and the semantics of domain names is conceived as very flexible functions. First extension regards the removal of unnecessary TLD's of domain names for resource level binding. Second extension makes the resource level domain names rich in functional binding and consequently various applications can be directly triggered by the invocation of domain names without intervening medium. These extensions may lay a certain direction for the future internet evolution.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.1-10
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• 2014

Cloud computing provides flexible and efficient mass data analysis platform. In this paper, we define a new resource provisioning architecture in the public cloud, named PCIA. In addition, we provide a service model of PCIA and its new naming scheme. Our model selects the proper number of physical or virtual resources based on the requirements of clients. By the analysis of performance variation in the PCIA, we evaluate the relationship between performance variation and resource provisioning, and we present key standards for cloud system constructions, which can be an important resource provisioning criteria for both cloud service providers and clients.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1486-1492
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• 2007

Searching distributed resources efficiently is very important in distributed computing environments like P2P. But distributed resource searching may have system overheads and take a lot of time in proportion to the searching number, because distributed resource searching has to circuit many peers for searching information. In this paper, we design a proxy peer algorithm based on DHT(Distributed Hash Table) for efficient distributed resource searching in JXTA network environments. By containing the rendezvous information in proxy peer and searching a rendezvous peer firstly which has higher hit ratio, we can reduce the searching number and minimize system overheads.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1545-1559
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• 2023

In the cloud environment, microservices are implemented through Kubernetes, and these services can be expanded or reduced through the autoscaling function under Kubernetes, depending on the service request or resource usage. However, the increase in the number of nodes or distributed microservices in Kubernetes and the unpredictable autoscaling function make it very difficult for system administrators to conduct operations. Artificial Intelligence for IT Operations (AIOps) supports resource management for cloud services through AI and has attracted attention as a solution to these problems. For example, after the AI model learns the metric or log data collected in the microservice units, failures can be inferred by predicting the resources in future data. However, it is difficult to construct data sets for generating learning models because many microservices used for autoscaling generate different metrics or logs in the same timestamp. In this study, we propose a cloud data refining module and structure that collects metric or log data in a microservice environment implemented by Kubernetes; and arranges it into computing resources corresponding to each service so that AI models can learn and analogize service-specific failures. We obtained Kubernetes-based AIOps learning data through this module, and after learning the built dataset through the AI model, we verified the prediction result through the differences between the obtained and actual data.