• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.591-593
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• 2022

This paper proposes a partial offloading system for minimizing application service processing latency in an FEC (Fog/Edge Computing) environment, and it analyzes the offloading effect of the proposed system against local-only and edge-server-only processing based on network load. A partial offloading algorithm based on reconstruction linearization of multi-branch structures is included in the proposed system, as is an optimal collaboration algorithm between mobile devices and edge servers [1,2]. The experiment was conducted by applying layer scheduling to a logical CNN model with a DAG topology. When compared to local or edge-only executions, experimental results show that the proposed system always provides efficient task processing strategies and processing latency.

• KIISE Transactions on Computing Practices
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• v.23 no.6
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• pp.392-396
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• 2017

Although mobile device-related technologies have developed rapidly, many problems arising from resource constraints have not been solved. Computation offloading that uses resources of cloud servers over the Internet was proposed to overcome physical limitations, and many studies have been conducted in terms of energy saving. However, completing tasks within their deadlines is more important than saving energy in real-time applications. In this paper, we proposed an offloading decision scheme considering the scheduling latency in the cloud to support real-time applications. The proposed scheme can improve the reliability of real-time tasks by comparing the estimated laxity of offloading a task with the estimated laxity of executing a task in a mobile device and selecting a more effective way to satisfy the task's deadline.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.167-176
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• 2010

Managing the growing number of data generated through various processes requires the aid of Database Management System (DBMS) to efficiently handle the huge amount of data. These data can be inserted into database m real time or in batch, that come from multiple sources, including those that are coming from inside and outside of a network. The insertion of large amount of data is commonly done through specific bulk loading or insertion function supplied by each individual DBMS. In this paper, we analyze and evaluate on handling data bulk loading for heterogeneous systems that is organised as multi-tiered architecture and compare the result of DBMS bulk loader against program insertion from a software development perspective. We propose a hybrid solution using staging database that can be easily deployed for enhancing bulk loading performance compared to insertion by application.

• Journal of KIISE:Databases
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• v.29 no.3
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• pp.193-206
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• 2002

In this paper, we design and implement an efficient bulk-loading algorithm for CIR-Tree. Bulk-loading techniques increase node utilization, improve query performance and reduce index construction time. The CIR-tree has variable size of internal node entries since it only maintains minimal dimensions to decriminate child nodes. This property increases fan-out of internal nodes and improves search performance. Even though several bulk-loading algorithms for multi/high-dimensional index structures have been proposed, we cannot apple them to CIR-tree because of the variable size of internal node entries. In this paper, we propose an efficient bulk- loading algorithm for CIR-tree that improves the existing bulk-loading algorithm and accomodates the property of CIR-tree. We also implement it on a storage system MiDAS-III and show superiority of our algorithm through various experiments.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.11
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• pp.377-386
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• 2020

Mobile cloud computing involves mobile or embedded devices as clients, and features small devices with constrained resource and low availability. Due to the fast expansion of smart phones and smart peripheral devices, researches on mobile cloud computing attract academia's interest more than ever. Computation offloading, or code offloading, enhances the performance of computation by migrating a part of computation of a mobile system to nearby cloud servers with more computational resources through wired or wireless networks. Code offloading is considered as one of the best approaches overcoming the limited resources of mobile systems. In this paper, we analyze the factors and the performance of code offloading, especially focusing on static program partitioning and data protection. We survey state-of-the-art researches on analyzed topics. We also describe directions for future research.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1551-1558
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• 2022

We propose a two-tier cooperative computing system comprised of a mobile device and an edge server for partial offloading of multi-branch structures in Fog/Edge Computing environments in this paper. The proposed system includes an algorithm for splitting up application service processing by using reconstructive linearization techniques for multi-branch structures, as well as an optimal collaboration algorithm based on partial offloading between mobile device and edge server. Furthermore, we formulate computation offloading and CNN layer scheduling as latency minimization problems and simulate the effectiveness of the proposed system. As a result of the experiment, the proposed algorithm is suitable for both DAG and chain topology, adapts well to different network conditions, and provides efficient task processing strategies and processing time when compared to local or edge-only executions. Furthermore, the proposed system can be used to conduct research on the optimization of the model for the optimal execution of application services on mobile devices and the efficient distribution of edge resource workloads.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2027-2028
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• 2006

추정된 채널 정보를 바탕으로 적용하는 적응형 비트 로딩 방식은, 전력선 통신 시스템의 고속화 및 대용량 데이터 전송을 위해 최근 대두되고 있는 LDPC(Low Density Parity Check) coded OFDM 시스템에 대해, 한정된 주파수 대역과 신호 전력의 효율적 사용을 제공한다. 그러나 적응형 비트로딩 방식은 한정된 수의 일정 SNR(신호대 잡음 전력비) 구간에 대한 mapping 방식으로 적용되기 때문에 송수신 과정에서 추정된 채널 정보를 이용하는 sum-product 복호기가 채널 변화에 민감하게 반응하지 못하는 상황이 발생하며, 결국 송신단에서 채널 추정 결과를 바탕으로 선택된 SNR 범위에 대해서는 실제 수신되는 신호에 대한 SNR과의 차이가 존재하고 시스템의 성능은 그 만큼의 성능 열하로 나타나게 된다. 본 논문에서는 이러한 성능 열하 정도를 시뮬레이션을 통해 확인하였다.

• Annual Conference on Human and Language Technology
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• 2012.10a
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• pp.119-120
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• 2012

GPU를 이용한 병렬 알고리즘은 어떤 메모리를 사용하는지에 따라 시스템의 전체적인 성능이 달라진다. 본 논문은 GPU 환경에서 실행되는 CUDA 프레임워크에서 병렬처리를 이용하여 문서 분류 시스템의 속도를 향상시키고자 할 때 메모리 로딩 시간이 전체적인 시스템의 성능에 미치는 영항을 연구하였다. 기존의 CPU 환경에서 구현했을 때와 비교하여 어느 정도의 성능 향상이 있었는지 실험하였으며 이전 연구에서 고려하지 않았던 메모리를 읽는데 걸리는 시간을 고려하여 현실적인 실행 시간을 비교하였다. 실험 결과에 의하면 CPU 에서 구현했을 때의 연산 속도보다 GPU의 텍스쳐 메모리를 사용하여 구현하였을 때 문서분류 성능이 향상되는 효과가 있음을 알 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.179-180
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• 2016

최근까지 모바일 디바이스와 고성능 클라우드 서버는 동일한 DVM 실행시간 환경에서 오프로딩을 통해 모바일 디바이스의 어플리케이션에 대해 실행속도 개선하려는 연구가 진행되고 있다. 본 논문에서는 안드로이드 실행시간 환경이 네이티브 어플리케이션을 지원하는 ART로 완전하게 전환되는 상황에서 DVM에서 실행되고 있는 모바일 레거시 어플리케이션에 대해 모바일 디바이스의 복잡한 계산 부담을 줄여 실행속도를 향상시고, 이를 통해 배터리 소모를 감소시키는 프로세스 단위 오프로딩 프레임워크에 대한 설계 내용을 제시한다.

• Electronics and Telecommunications Trends
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• v.30 no.4
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• pp.140-150
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• 2015

모바일 트래픽 증대는 향후 급격히 증가할 것으로 예측되며 LTE에서 AII-IP기반의 고속 대용량 데이터처리는 망의 부하증가를 의미한다. 비디오 컨퍼런싱, 온라인 음악, 게임, 영화 스트리밍, 네트워크 브라우징, 블로깅 등 현재 온라인 상에서 급격하게 사용되고 있는 새로운 애플리케이션들은 높은 대역폭(Bandwidth)과 고속용량이 필요하고 망 운용자들은 대역폭과 네트워크 용량 한계점을 줄이기 위한 해결책에 관심을 갖게 되었다. 이를 위해 3GPP에서는 데이터 오프로딩(Data Offloading) 기술을 정의하였으며 이는 펨토셀과 non-3GPF액서스 망 등과 정합하는 기술을 제공하였다. 본고에서는 망의 부하를 줄이기 위한 이들 오프로딩 기술에 대한 요구사항, 구조 및 구현 시 고려해야 하는 주요 이슈들에 대해서 살펴본다.