• Journal of Computing Science and Engineering
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• 제9권3호
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• pp.155-162
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• 2015

The last decade has seen a rapid growth in the use of mobile devices all over the world. With an increasing use of mobile devices, mobile applications are becoming more diverse and complex, demanding more computational resources. However, mobile devices are typically resource-limited (i.e., a slower-speed CPU, a smaller memory) due to a variety of reasons. Mobile users will be capable of running applications with heavy computation if they can offload some of their computations to other places, such as a desktop or server machines. However, mobile users are typically subject to dynamically changing network environments, particularly, due to user mobility. This makes it hard to choose good offloading decisions in mobile environments. In general, users' mobility can provide some hints for upcoming changes to network environments. Motivated by this, we propose a mobility model of each individual user taking advantage of the regularity of his/her mobility pattern, and develop an offloading decision-making technique based on the mobility model. We evaluate our technique through trace-based simulation with real log data traces from 14 Android users. Our evaluation results show that the proposed technique can help boost the performance of mobile devices in terms of response time and energy consumption, when users are highly mobile.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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• pp.4347-4366
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• 2015

Distinguished with traditional strategies which offload an application's computation to a single server, parallel computation offloading can promote the performance by simultaneously delivering the computation to multiple computing resources around the mobile terminal. However, due to the variability of communication and computation environments, static application component multi-partitioning algorithms are difficult to maintain the optimality of their solutions in time-varying scenarios, whereas, over-frequent algorithm executions triggered by changes of environments may bring excessive algorithm costs. To this end, an adaptive application component mapping algorithm for parallel computation offloading in variable environments is proposed in this paper, which aims at minimizing computation costs and inter-resource communication costs. It can provide the terminal a suitable solution for the current environment with a low incremental algorithm cost. We represent the application component multi-partitioning problem as a graph mapping model, then convert it into a pathfinding problem. A genetic algorithm enhanced by an elite-based immigrants mechanism is designed to obtain the solution adaptively, which can dynamically adjust the precision of the solution and boost the searching speed as transmission and processing speeds change. Simulation results demonstrate that our algorithm can promote the performance efficiently, and it is superior to the traditional approaches under variable environments to a large extent.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권11호
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• pp.4372-4394
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• 2020

The distribution of popular videos incurs a large amount of traffic at the base stations (BS) of networks. Device-to-multi-device (D2MD) communication has emerged an efficient radio access technology for offloading BS traffic in recent years. However, traditional studies have focused on synchronous user requests whereas asynchronous user requests are more common. Hence, offloading BS traffic in case of asynchronous user requests while considering their time-varying characteristics and the quality of experience (QoE) of video request users (VRUs) is a pressing problem. This paper uses social stability (SS) and video loading duration (VLD)-tolerant property to group VRUs and seed users (SUs) to offload BS traffic. We define the average amount of data transmission (AADT) to measure the network's capacity for offloading BS traffic. Based on this, we formulate a time-varying bipartite graph matching optimization problem. We decouple the problem into two subproblems which can be solved separately in terms of time and space. Then, we propose the socially aware D2MD user selection (SA-D2MD-S) algorithm based on finite horizon optimal stopping theory, and propose the SA-D2MD user matching (SA-D2MD-M) algorithm to solve the two subproblems. The results of simulations show that our algorithms outperform prevalent algorithms.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권6호
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• pp.392-396
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• 2017

모바일 기기 관련 기술의 급속한 발달에도 자원 제약적인 특성으로 인한 많은 문제들이 아직까지 해결되지 못하고 있다. 이러한 물리적인 한계성을 극복하기 위해 인터넷으로 연결된 클라우드 서버의 자원을 활용하는 컴퓨테이션 오프로딩이 고안되었고 에너지 절약 측면에서 다양한 연구들이 진행되었다. 그러나 실시간성을 만족시켜야 하는 응용에서는 에너지 보다 마감시간 내에 작업의 수행을 완료하는 것이 더 중요하다. 본 논문에서는 이러한 실시간 응용을 지원하기 위해서 클라우드의 스케줄링 지연 시간을 고려한 오프로딩 결정 기법을 제안했다. 제안 기법에서는 오프로딩의 예상 여유시간과 모바일 기기 내에서 수행했을 때의 여유 시간을 비교하여 마감시간을 더 효과적으로 만족할 수 있는 방법을 선정함으로써 실시간 작업에 대한 신뢰성을 향상 시킬 수 있다.

• 정보과학회 논문지
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• 제45권2호
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• pp.141-149
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• 2018

모바일 디바이스의 보편적인 보급으로 인하여 모바일 컴퓨팅은 사용자들의 일상 생활에 편리를 가져다 주는 컴퓨팅 패러다임으로 되었다. 다양한 타입의 모바일 애플리케이션의 출현으로 인하여 사용자들은 언제 어디서나 자신의 스케줄 관리 등 다양한 업무 수행이 가능해졌지만 모바일 디바이스의 리소스 제한적인 문제로 인하여 일정 수준의 컴퓨팅 작업만 수행 가능하고 비교적 큰 컴퓨팅 작업을 수행하기에는 불편한 점이 존재한다. 클라우드 컴퓨팅 연구에서는 제한된 모바일 디바이스의 자원을 해결하기 위하여 기능 컴포넌트를 다른 서버 노드로 오프로딩(Offloading) 시킴으로써, 모바일 노드의 자원 문제를 해결하는 솔루션을 제공하였다. 그러나, 현재 진행되고 있는 동적 오프로딩 기법에 관한 연구는 개념적인 수준의 기법만 제시되고 있다. 본 논문에서는 모바일 노드의 성능을 보장하기 위한 예측 기반 동적 오프로딩 기법 및 프레임워크 설계 모델을 제안한다. 그리고 제안한 예측 기반 오프로딩 기법의 유효성 검증을 위한 실험 및 평가를 수행한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권7호
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• pp.3134-3155
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• 2020

In this paper, we study the joint radio and computational resource allocation in the ultra-dense mobile-edge computing networks. In which, the scenario which including both computation offloading and communication service is discussed. That is, some mobile users ask for computation offloading, while the others ask for communication with the minimum communication rate requirements. We formulate the problem as a joint channel assignment, power control and computational resource allocation to minimize the offloading cost of computing offloading, with the precondition that the transmission rate of communication nodes are satisfied. Since the formulated problem is a mixed-integer nonlinear programming (MINLP), which is NP-hard. By leveraging the particular mathematical structure of the problem, i.e., the computational resource allocation variable is independent with other variables in the objective function and constraints, and then the original problem is decomposed into a computational resource allocation subproblem and a joint channel assignment and power allocation subproblem. Since the former is a convex programming, the KKT (Karush-Kuhn-Tucker) conditions can be used to find the closed optimal solution. For the latter, which is still NP-hard, is further decomposed into two subproblems, i.e., the power allocation and the channel assignment, to optimize alternatively. Finally, two heuristic algorithms are proposed, i.e., the Co-channel Equal Power allocation algorithm (CEP) and the Enhanced CEP (ECEP) algorithm to obtain the suboptimal solutions. Numerical results are presented at last to verify the performance of the proposed algorithms.

• 한국정보통신학회논문지
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• 제26권10호
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• pp.1551-1558
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• 2022

본 논문에서는 FEC (Fog/Edge Computing) 환경에서 다중 분기구조의 부분 오프로딩을 위해 모바일 장치와 에지서버로 구성된 2계층 협력 컴퓨팅 시스템을 제안한다. 제안 시스템은 다중 분기구조에 대한 재구성 선형화 기법을 적용하여 응용 서비스 처리를 분할하는 알고리즘과 모바일 장치와 에지 서버 간의 부분 오프로딩을 통한 최적의 협업 알고리즘을 포함한다. 또한 계산 오프로딩 및 CNN 계층 스케줄링을 지연시간 최소화 문제로 공식화하고 시뮬레이션을 통해 제안 시스템의 효과를 분석한다. 실험 결과 제안 알고리즘은 DAG 및 체인 토폴로지 모두에 적합하고 다양한 네트워크 조건에 잘 적응할 수 있으며, 로컬이나 에지 전용 실행과 비교하여 효율적인 작업 처리 전략 및 처리시간을 제공한다. 또한 제안 시스템은 모바일 장치에서의 응용 서비스 최적 실행을 위한 모델의 경량화 및 에지 리소스 워크로드의 효율적 분배 관련 연구에 적용 가능하다.

• International Journal of Ocean Engineering and Technology Speciallssue:Selected Papers
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• 제3권1호
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• pp.1-7
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• 2000

A new alternative for large deepwater field development is described. This "Oil Box" (aka "Box Spar") is a multifunction vessel capable of floating drilling, production, storage and offloading (FDPSO). It is distinguished from other Floating Production, Storage and Offloading (FPSO) vessels by its unique hull form and oil storage system. It's main advantages are flexibility derived from the floatover deck option, use of proven top tensioned riser technology, and motion characteristics which make it operable in a wide range of environmental conditions.

• 한국멀티미디어학회논문지
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• 제24권6호
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• pp.789-795
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• 2021

In order to provide useful Internet of Things (IoT) services, the locations of IoT devices should be well managed. However, frequent location updates of lots of IoT devices result in signaling overhead in networks. To solve this problem, this paper utilizes the opportunistic fog node (OFN) which is opportunistically available according to the mobility to perform the location updates as a representative of IoT devices. Therefore, the location updates through OFN can reduce the signaling loads of networks. To show the performance of the proposed scheme, we develop an analytic model for the opportunistic location update offloading probability that the location update can be offloaded to OFN from the IoT device. Then, the extensive simulation results are given to validate the analytic model and to assess the performance of the proposed scheme in terms of the opportunistic location update offloading probability.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 춘계학술발표대회
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• pp.45-47
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• 2022

Mobile edge computing (MEC), which enables mobile terminals to offload computational tasks to a server located at the user's edge, is considered an effective way to reduce the heavy computational burden and achieve efficient computational offloading. In this paper, we study a multi-user MEC system in which multiple user devices (UEs) can offload computation to the MEC server via a wireless channel. To solve the resource allocation and task offloading problem, we take the total cost of latency and energy consumption of all UEs as our optimization objective. To minimize the total cost of the considered MEC system, we propose an DRL-based method to solve the resource allocation problem in wireless MEC. Specifically, we propose a Asynchronous Advantage Actor-Critic (A3C)-based scheme. Asynchronous Advantage Actor-Critic (A3C) is applied to this framework and compared with DQN, and Double Q-Learning simulation results show that this scheme significantly reduces the total cost compared to other resource allocation schemes