• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2282-2303
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• 2021

To solve the problems of heavy computing load and system transmission pressure in energy internet (EI), we establish a three-tier cloud-edge integrated EI network based on a cloud-edge collaborative computing to achieve the tradeoff between energy consumption and the system delay. A joint optimization problem for resource allocation and task offloading in the threetier cloud-edge integrated EI network is formulated to minimize the total system cost under the constraints of the task scheduling binary variables of each sensor node, the maximum uplink transmit power of each sensor node, the limited computation capability of the sensor node and the maximum computation resource of each edge server, which is a Mixed Integer Non-linear Programming (MINLP) problem. To solve the problem, we propose a joint task offloading and resource allocation algorithm (JTOARA), which is decomposed into three subproblems including the uplink transmission power allocation sub-problem, the computation resource allocation sub-problem, and the offloading scheme selection subproblem. Then, the power allocation of each sensor node is achieved by bisection search algorithm, which has a fast convergence. While the computation resource allocation is derived by line optimization method and convex optimization theory. Finally, to achieve the optimal task offloading, we propose a cloud-edge collaborative computation offloading schemes based on game theory and prove the existence of Nash Equilibrium. The simulation results demonstrate that our proposed algorithm can improve output performance as comparing with the conventional algorithms, and its performance is close to the that of the enumerative algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3416-3435
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• 2023

Task offloading in vehicular networks is hot topic in the development of autonomous driving. In these scenarios, due to the role of vehicles and pedestrians, task characteristics are changing constantly. The classical deep learning algorithm always uses a pre-trained neural network to optimize task offloading, which leads to system performance degradation. Therefore, this paper proposes a neural network fine-tuning task offloading algorithm, combining with location prediction for pedestrians and vehicles by the Payne model of fluid dynamics and the car-following model, respectively. After the locations are predicted, characteristics of tasks can be obtained and the neural network will be fine-tuned. Finally, the proposed algorithm continuously predicts task characteristics and fine-tunes a neural network to maintain high system performance and meet low delay requirements. From the simulation results, compared with other algorithms, the proposed algorithm still guarantees a lower task offloading delay, especially when congestion occurs.

• 인터넷정보학회논문지
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• 제16권5호
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• pp.49-57
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• 2015

본 논문에서는 응용 맞춤형 그래픽 분할 실행 라이브러리에 기반한 효율적인 온라인 소프트웨어 서비스에 대하여 소개한다. 그래픽 분할 실행을 이용한 소프트웨어 서비스는 클라이언트 렌더링을 통하여 3D 그래픽 소프트웨어와 같은 고사양의 소프트웨어를 서버 기반의 온라인 소프트웨어 서비스로 제공할 수 있다. 그래픽 분할 실행은 서버에서 소프트웨어가 실행될 때, 그래픽 관련된 작업은 클라이언트의 GPU를 이용하여 처리하고, 데이터 관련 작업은 서버의 CPU를 이용하여 처리하는 방식이다. 그래픽 분할 실행 소프트웨어 서비스의 성능을 향상시키기 위하여, 비동기 전송 채널을 추가하고, 최적화 된 소프트웨어 공통 모듈과 소프트웨어 맞춤형 모듈을 기존의 그래픽 분할 실행 엔진에 추가한다. 이를 위하여, 본 논문에서는 그래픽 관련 API와 메시지들을 분석하여 소프트웨어 맞춤형 모듈을 구현하고, 서버 사이드 캐싱 방법을 통하여 최적화된 소프트웨어 공통 모듈을 구현하는 방법에 대하여 기술한다. 마지막으로, 성능 비교 실험을 통하여 개선된 분할 실행 엔진이 더 나은 성능을 가짐을 보여준다.

• 한국항공우주학회지
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• 제34권11호
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• pp.54-60
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• 2006

본 논문에서는 광학 탑재체를 장착하기 위해 단일 태양전지판으로 구성된 통신해양기상위성(COMS)에 대한 휠오프로딩의 접근 방법에 대하여 다루었다. 먼저 연료 소모량을 줄이고 실제적 구현 측면을 고려하기 위해 COMS의 형상을 바탕으로 수치계산을 수행하였고 이를 바탕으로 휠오프로딩에 사용되는 추력기 조합을 제안하였다. 본 논문에서는 매일 두 번에 걸쳐 휠오프로딩을 수행하는 것으로 가정하였다. 또한 제안된 추력기 조합의 효율성을 검증하기 위하여 궤도 시뮬레이션을 수행하였으며 몇 가지 접근 방법의 결과와 비교하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권2호
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• pp.383-403
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• 2021

With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3603-3618
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• 2022

The offloading method is important when there are multiple mobile nodes and multiple edge servers. In the environment, those mobile nodes connect with edge servers with different bandwidths, thus taking different time and energy for offloading tasks. Considering the system load of edge servers and the attributes (the number of instructions, the size of files, deadlines, and so on) of tasks, the energy-aware offloading problem becomes difficult under our mobile edge environment (MCE). Most of the past work mainly offloads tasks by judging where the job consumes less energy. But sometimes, one task needs more energy because the preferred edge servers have been overloaded. Those methods always do not pay attention to the influence of the scheduling on the future tasks. In this paper, first, we try to execute the job locally when the job costs a lower energy consumption executed on the MD. We suppose that every task is submitted to the mobile server which has the highest bandwidth efficiency. Bandwidth efficiency is defined by the sending ratio, the receiving ratio, and their related power consumption. We sort the task in the descending order of the ratio between the energy consumption executed on the mobile server node and on the MD. Then, we give a "suffrage" definition for the energy consumption executed on different mobile servers for offloading tasks. The task selects the mobile server with the largest suffrage. Simulations show that our method reduces the execution time and the related energy consumption, while keeping a lower value in the number of uncompleted tasks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.4081-4098
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• 2022

With the aim of tackling the contradiction between computation intensive industrial applications and resource-weak Edge Devices (EDs) in Industrial Internet of Things (IIoT), a novel computation task offloading scheme in SDIN-enabled MEC based IIoT is proposed in this paper. With the aim of reducing the task accomplished latency and energy consumption of EDs, a joint optimization method is proposed for optimizing the local CPU-cycle frequency, offloading decision, and wireless and computation resources allocation jointly. Based on the optimization, the task offloading problem is formulated into a Mixed Integer Nonlinear Programming (MINLP) problem which is a large-scale NP-hard problem. In order to solve this problem in an accessible time complexity, a sub-optimal algorithm GPCOA, which is based on hybrid evolutionary computation, is proposed. Outcomes of emulation revel that the proposed method outperforms other baseline methods, and the optimization result shows that the latency-related weight is efficient for reducing the task execution delay and improving the energy efficiency.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.591-593
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• 2022

본 논문은 FEC 환경에서 응용 서비스의 처리 지연시간 최소화를 위하여 선행연구 제안한 부분 오프로딩 시스템의 네트워크 부하에 따른 오프로딩의 효과를 분석한다. 모바일 장치와 FEC 서버 간의 2계층 협력 컴퓨팅 시스템으로 구성된 제안 시스템을 로컬 전용 및 에지 서버 전용 처리와 비교한다. 제안 시스템은 다중 분기구조의 재구성 선형화를 통한 부분 오프로딩 알고리즘[1]과 두 계층 간의 최적 협업 알고리즘[2]을 포함한다. 실험은 다중 분기구조의 DAG 토폴로지를 갖는 논리적 CNN 모델을 대상으로 계층 스케줄링을 적용하여 수행하였으며, 실험 결과 제안 시스템은 로컬이나 에지 전용 실행과 비교하여 항상 효율적인 작업 처리 전략 및 처리 지연시간을 제공함을 입증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.211-232
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• 2024

In this paper, we consider the resource allocation and offloading decisions of device-to-device (D2D) cooperative UAV-assisted mobile edge computing (MEC) system, where the device with task request is served by unmanned aerial vehicle (UAV) equipped with MEC server and D2D device with idle resources. On the one hand, to ensure the fairness of time-delay sensitive devices, when UAV computing resources are relatively sufficient, an optimization model is established to minimize the maximum delay of device computing tasks. The original non-convex objective problem is decomposed into two subproblems, and the suboptimal solution of the optimization problem is obtained by alternate iteration of two subproblems. On the other hand, when the device only needs to complete the task within a tolerable delay, we consider the offloading priorities of task to minimize UAV computing resources. Then we build the model of joint offloading decision and power allocation optimization. Through theoretical analysis based on KKT conditions, we elicit the relationship between the amount of computing task data and the optimal resource allocation. The simulation results show that the D2D cooperation scheme proposed in this paper is effective in reducing the completion delay of computing tasks and saving UAV computing resources.

• Ocean Systems Engineering
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• 제10권2호
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• pp.131-161
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• 2020

The objective of this study was to evaluate the downtime cost of side-by-side offloading operations in Malaysian waters. With the help of a numerical time domain tool, the structure and cable response of moored FPSO vessel was simulated for heading and beam sea-states under irregular waves. The weather downtime was assessed by comparing the response under operational wave condition with the pre defined industrial safe offloading criteria. Additionally, two cases of cable failure were simulated for each sea-state. The novel study on downtime cost was presented for three different location of Malaysia subcontinent for which the location specific wave scatter diagram facilitated to estimate the probability of occurrence of operational wave condition. It was concluded that an unpredictable increment in wave height by 0.5 m can significantly impact the production cost.