• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.151-153
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• 2016

모바일 디바이스의 성능 발전에 따라 높은 연산 능력을 요구하는 어플리케이션이 증가하고 있다. 3D 프로세싱, 영상 인코팅, 벡터 이미지 처리 등 연산량이 많아 기존에 데스크탑 컴퓨터에서 처리하던 작업들이 모바일 디바이스로 이전되고 있다. 그러나 모바일 디바이스 하드웨어의 발전 속도와 상이하게 사용자의 모바일 디바이스 교체 주기는 발전 속도를 따라잡지 못하고 있다. 따라서 추가적인 비용부담 없이 모바일 디바이스의 연산 능력을 향상시키기 위해 Mobile Cloud Computing(MCC)의 필요성이 대두되고 있다. MCC는 다수의 모바일 디바이스 리소스를 통합 관리하고 자원 서비스를 제공함으로써 작업 처리 및 자원 고가용성등의 성능 향상이 가능하다. 본 연구에서는 MCC 인프라 구축 방법인 Mobile Resource Integration(MRI)와 MRI에 적용되는 오프로드 방법을 제안한다. 모바일 디바이스로만 구성된 MRI는 분산된 모바일 디바이스로 작업를 분할 전송 처리는 방법이다. 이를 통해 중거리 및 원거리 통신망 연결이 어려운 경우에 단일 모바일 디바이스의 작업 처리 대비 높은 향상 속도를 보인다.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.822-825
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• 2012

모바일 디바이스의 사용이 폭발적으로 증가하고 있고, 특히 스마트폰을 위한 많은 어플리케이션이 소개 되고 있지만, 여전히 스마트폰이 가지는 낮은 하드웨어 성능과 제한적인 배터리용량은 PC 환경을 대체하기가 어렵다. 본 논문에서는 클라우드가 가지는 서버 환경을 통해서 모바일 디바이스의 특정 태스크를 서버 사이드로 오프로딩 하여 실행하고, 단말은 그 결과만 전달받는 방식의 모바일 오프로딩 시스템을 제안한다. 실험을 통해서 모바일 오프로딩의 방법이 어플리케이션의 응답성을 높일 수 있음을 확인하였다. 또한 제시된 오프로딩 기법을 통해서 단말의 CPU utilization을 줄이고, 따라서 단말의 소모전력을 최대 70%까지 줄일 수 있었다.

• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.14-23
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• 2014

This paper proposed controlling methods for SDN(Software Defined Network) based multiple radio access technology as the solutions of following two issues which were mainly occurred by explosive increasing of video traffic. The first one is a requirement for traffic off-loading caused by 3rd-party video service providers from the mobile network operator's viewpoint. The other one is a provision of high-speed video contents transmission services with low price. Furthermore, the performance evaluation was also conducted on the real test-bed which is composed of OpenStack cloud and SDN technology such as OpenFlow and Open vSwitch. A virtual machine running on the OpenStack provide a video service and the terminal which is able to use multiple radio access technology supports two 2.4GHz WLANs(Wireless Local Area Network) and three 5GHz WLANs, concurrently. Finally, we can get 820Mbps of the maximum transmission speed by using that five WLAN links for the single service at the same time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2671-2676
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• 2015

Nowadays, Mobile devices are now capable of supporting a wide range of applications. Unfortunately, some of applications demand an ever increasing computational power and mobile devices have limited resources due to their constraints, such as low processing power, limited memory, unpredictable connectivity, and limited battery life. To deal with mobile devices' constraints, researchers envision extending cloud computing services to mobile devices using virtualization techniques to shift the workload from mobile devices to a powerful computational infrastructure. Those techniques consist of migrating resource-intensive computations from a mobile device to the resource-rich cloud, or server (called nearby infrastructure). In this paper, we want to highlight on cloudlet architecture (nearby infrastructure with mobile devices), its functioning and in our future work, analyze effects of distance between cloudlet and mobile devices.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.305-316
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• 2022

With the flood of digital data owing to the Internet of Things and big data, cloud service providers that process and store vast amount of data from multiple users can apply duplicate data elimination technique for efficient data management. The user experience can be improved as the notion of edge computing paradigm is introduced as an extension of the cloud computing to improve problems such as network congestion to a central cloud server and reduced computational efficiency. However, the addition of a new edge device that is not entirely reliable in the edge computing may cause increase in the computational complexity for additional cryptographic operations to preserve data privacy in duplicate identification and elimination process. In this paper, we propose an efficiency-improved duplicate data elimination protocol while preserving data privacy with an optimized user-edge-cloud communication framework by utilizing a trusted execution environment. Direct sharing of secret information between the user and the central cloud server can minimize the computational complexity in edge devices and enables the use of efficient encryption algorithms at the side of cloud service providers. Users also improve the user experience by offloading data to edge devices, enabling duplicate elimination and independent activity. Through experiments, efficiency of the proposed scheme has been analyzed such as up to 78x improvements in computation during data outsourcing process compared to the previous study which does not exploit trusted execution environment in edge computing architecture.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.163-168
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• 2022

As cloud computing and the Internet of things are getting popular, the number of devices in the Internet of things computing environments is increasing. In addition, there exist various Internet-based applications, such as home automation and healthcare. In turn, existing studies explored the quality of service, such as downtime and reliability of tasks for Internet of things applications. To enhance the quality of service of Internet of things applications, cloud-fog computing (combining cloud computing and edge computing) can be used for offloading burdens from the central cloud server to edge servers. However, when devices inherit the mobility property, continuity and the quality of service of Internet of things applications can be reduced. In this paper, we propose a resource management scheme based on live migrations for mobility support in edge-based fog computing environments. The proposed resource management algorithm is based on the mobility direction and pace to predict the expected position, and migrates tasks to the target edge server. The performance results show that our proposed resource management algorithm improves the reliability of tasks and reduces downtime of services.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.17-22
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• 2022

In this paper, we design a lightweight embedded device that can support intelligent edge computing, and show that the device quickly detects an object in an image input from a camera device in real time. The proposed system can be applied to environments without pre-installed infrastructure, such as an intelligent video control system for industrial sites or military areas, or video security systems mounted on autonomous vehicles such as drones. The On-Device AI(Artificial intelligence) technology is increasingly required for the widespread application of intelligent vision recognition systems. Computing offloading from an image data acquisition device to a nearby edge device enables fast service with less network and system resources than AI services performed in the cloud. In addition, it is expected to be safely applied to various industries as it can reduce the attack surface vulnerable to various hacking attacks and minimize the disclosure of sensitive data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.786-792
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• 2022

In the cloud computing environment, there has been a lot of research into the Fog/Edge Computing (FEC) paradigm for securing user proximity of application services and computation offloading to alleviate service delay difficulties. The method of predicting dynamic location change patterns of edge devices (moving objects) requesting application services is critical in this FEC environment for efficient computing resource distribution and deployment. This paper proposes an optimal moving pattern extraction algorithm in which variable weights (distance, time, congestion) are applied to selected paths in addition to a support factor threshold for frequency patterns (moving objects) of edge devices. The proposed algorithm is compared to the OPE_freq [8] algorithm, which just applies frequency, as well as the A^* and Dijkstra algorithms, and it can be shown that the execution time and number of nodes accessed are reduced, and a more accurate path is extracted through experiments.