• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.89-92
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• 2020

According to the various IoT(Internet of Things) services, there have been lots of task offloading researches for IoT devices. Since there are service response delay and core network load issues in conventional cloud computing based offloadings, fog computing based offloading has been focused whose location is close to the IoT devices. However, even in the fog computing architecture, the load can be concentrated on the for computing node when the number of requests increase. To solve this problem, the opportunistic fog computing concept which offloads task to available computing resources such as cars and drones is introduced. In previous fog and opportunistic fog node researches, the offloading is performed immediately whenever the service request occurs. This means that the service requests can be offloaded to the opportunistic fog nodes only while they are available. However, if the service response delay requirement is satisfied, there is no need to offload the request immediately. In addition, the load can be distributed by making the best use of the opportunistic fog nodes. Therefore, this paper proposes a delayed offloading scheme to satisfy the response delay requirements and offload the request to the opportunistic fog nodes as efficiently as possible.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.67-75
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• 2014

In this paper, we proposed offloading delay method which determines effectively offloading timing by measuring of handoff delay and offloading time at mobile node side in mobile environment. The propose method measures each of handoff delay and offloading time and making decision of proper offloading timing on mobile node side. Therefore, it is possible to support cloud computing without changing previous implemented cloud computing structure for fixed node in a mobile environment. We compare the energy consumption of server and node to analyze efficiency of proposed method by using existing method of energy consumption measurement. Simulation results shows the reducing energy consumption more than previous method and operation time similar to previous method.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.52-55
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• 2022

5G 시대에 스마트 모바일 기기가 기하급수적으로 증가하면서 멀티 액세스 엣지 컴퓨팅(MEC)이 유망한 기술로 부상했다. 낮은 지연시간 안에 계산 집약적인 서비스를 제공하기 위해 MEC 서버로 오프로딩하는 특히, 태스크 도착률과 무선 채널의 상태가 확률적인 MEC 시스템 환경에서의 오프로딩 연구가 주목받고 있다. 본 논문에서는 차량의 전력과 지연시간을 최소화하기 위해 로컬 실행을 위한 연산 자원과 오프로딩을 위한 전송 전력을 할당하는 심층 강화학습 기반의 오프로딩 기법을 제안하였다. Deep Deterministic Policy Gradient (DDPG) 기반 기법과 Deep Q-network (DQN) 기반 기법을 차량의 전력 소비량과 큐잉 지연시간 측면에서 성능을 비교 분석하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.625-627
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• 2022

FEC (Fog/Edge Computing) 환경에서 지연시간 최소화는 로컬과 에지 서버 간의 효율적인 협력을 보장하기 위한 최적의 계산 오프로딩 방법 결정을 통해 실현될 수 있다. 본 논문은 임의의 응용 서비스 실행모듈에 대한 부분 오프로딩 기반의 로컬(모바일 장치)과 에지 서버 간의 협업 경로를 추출하는 방법을 제안한다. 제안 방법은 다중 분기구조를 포함하는 응용 서비스 실행모듈에 대한 부분 오프로딩 기반의 최적 협업 실행경로 추출 방법을 제안한다. 제안 방법은 각 부분 모듈들의 실행위치에 따라 변화되는 지연시간 측정 및 분석에 적용가능하다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.100-103
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• 2021

5G 의 발전과 함께 차량과 IT 통신 기술을 융합한 어플리케이션들이 급증하면서 멀티 액세스 엣지 컴퓨팅(MEC)이 차세대 기술로 등장했다. 낮은 지연시간 안에 계산 집약적인 서비스들을 제공하기 위해 단독적인 MECS 서버(MECS)에서의 수행이 아닌 다수의 MECS 에서 동시에 연산을 수행할 수 있도록 태스크를 파티셔닝하는 기법이 주목받고 있다. 특히 차량이 다수의 MECS 로 태스크를 파티셔닝하여 오프로딩하는 기법과 하나의 MECS 로 오프로딩한 후 다른 MECS 들로 파티셔닝하여 마이그레이션하는 기법들이 연구되고 있다. 본 논문에서는 오프로딩과 마이그레이션을 이용한 파티셔닝 기법들을 서비스 지연시간과 차량의 에너지 소비량 측면에서 성능을 비교 분석을 하였다.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.511-513
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• 2021

Recently, various studies have been conducted on Fog/Edge Computing (FEC) to meet the demand for large numbers of devices and new IoT by efficiently coordinating and managing computing resources deployed on network edges. This paper presents key issues and its solutions for determining offloading targets and improving the efficiency of offloading methods to induce minimizing execution latency in FEC environments. TThe proposals in this paper can be effectively applied to building the FEC framework required by relevant stakeholders in the future.

• 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.

• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.39-44
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• 2020

In this paper, we propose a SDN-based fog computing service mobility support scheme. Fog computing architecture has been attracted because it enables task offloading services to IoT(Internet of Things) devices which has limited computing and power resources. However, since static as well as mobile IoT devices are candidate service targets for the fog computing service, the efficient task offloading scheme considering the mobility should be required. Especially for the IoT services which need low-latency response, the new connection and task offloading delay with the new fog computing node after handover can occur QoS(Quality of Service) degradation. Therefore, this paper proposes an efficient service mobility support scheme which considers both task migration and flow rule pre-installations. Task migration allows for the service connectivity when the fog computing node needs to be changed. In addition, the flow rule pre-installations into the forwarding nodes along the path after handover enables to reduce the connection delay and service interruption time.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.5
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• pp.139-146
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• 2022

With the recent development of the Internet of Things (IoT) and the convergence of vehicles and IT technologies, high-performance applications such as autonomous driving are emerging, and multi-access edge computing (MEC) has attracted lots of attentions as next-generation technologies. In order to provide service to these computation-intensive tasks in low latency, many methods have been proposed to partition tasks so that they can be performed through cooperation of multiple MEC servers(MECSs). Conventional methods related to task partitioning have proposed methods for partitioning tasks on vehicles as mobile devices and offloading them to multiple MECSs, and methods for offloading them from vehicles to MECSs and then partitioning and migrating them to other MECSs. In this paper, the performance of task partitioning methods using offloading and migration is compared and analyzed in terms of service delay, blocking rate and energy consumption according to the method of selecting partitioning targets and the number of partitioning. As the number of partitioning increases, the performance of the service delay improves, but the performance of the blocking rate and energy consumption decreases.