• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.107-108
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• 2019

모바일 엣지 컴퓨팅(Mobile Edge Computing, MEC)은 높은 컴퓨팅 성능을 요구하는 작업을 모바일 장치에서 가까운 MEC 서버로 오프로딩함으로써 모바일 서비스에 높은 계산 요구량을 효율적으로 제공할 수 있는 기술로 부상하였다. 본 논문에서는 실행 대기 시간과 장치 에너지 소비를 줄이기 위해 여러 가지의 독립적 작업을 통해 MEC 시스템에 대한 작업 오프로드 일정 및 전송 에너지 할당을 최적화하는 기법을 제안한다. 시뮬레이션 결과로 MEC 시스템에서 사용 가능한 무선 및 계산 리소스가 상대적으로 균형 잡혀있는 경우 작업 오프로딩 일정이 더 중요하다는 것을 확인했다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5614-5633
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• 2018

Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are both recognized as promising techniques, one is for solving the resource insufficient of mobile devices and the other is for powering the mobile device. Naturally, by integrating the two techniques, task will be capable of being executed by the harvested energy which makes it possible that less intrinsic energy consumption for task execution. However, this innovative integration is facing several challenges inevitably. In this paper, we aim at prolonging the battery life of mobile device for which we need to maximize the harvested energy and minimize the consumed energy simultaneously, which is formulated as residual energy maximization (REM) problem where the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device are all considered as key factors. To this end, we jointly optimize the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device to solve the REM problem. Furthermore, we propose an efficient convex optimization and sequential unconstrained minimization technique based combining method to solve the formulated multi-constrained nonlinear optimization problem. The result shows that our joint optimization outperforms the single optimization on REM problem. Besides, the proposed algorithm is more efficiency.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.103-111
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• 2016

Offloading tasks from mobile devices to available cloud servers were improved since the introduction of the cloudlet. With the implementation of dynamic offloading algorithms, mobile devices can choose the appropriate server for the set of tasks. However, current task distribution approaches do not consider the number of VM, which can be a critical factor in the decision making. This paper proposes a dynamic task distribution on clustered data centers. A proportional VM migration approach is also proposed, where it migrates virtual machines to the cloud servers proportionally according to their allocated CPU, in order to prevent overloading of resources in servers. Moreover, we included the resource capacity of each data center in terms of the maximum CPU in order to improve the migration approach in cloud servers. Simulation results show that the proposed mechanism for task distribution greatly improves the overall performance of the system.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.11
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• pp.291-296
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• 2021

The development of the Internet of Things(IoT) requires large computational resources for tasks from numerous devices. Mobile Edge Computing(MEC) has attracted a lot of attention in the IoT environment because it provides computational resources geographically close to the devices. Task offloading to MEC servers is efficient for devices with limited battery life and computational capability. In this paper, we assumed an industrial IoT environment requiring high reliability. The complexity of optimization problem in industrial IoT environment with many devices and multiple MEC servers is very high. To solve this problem, the problem is divided into two. After selecting the MEC server considering the queue status of the MEC server, we propose an offloading decision algorithm that optimizes reliability and energy consumption using genetic algorithm. Through experiments, we analyze the performance of the proposed algorithm in terms of energy consumption and reliability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2422-2443
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• 2020

This paper studies a single-user Mobile Edge Computing (MEC) system where mobile device (MD) includes an application consisting of multiple computation components or tasks with dependencies. MD can offload part of each computation-intensive latency-sensitive task to the AP integrated with MEC server. In order to accomplish the application faultlessly, we calculate out the optimal task offloading strategy in a time-division manner for a predetermined execution order under the constraints of limited computation and communication resources. The problem is formulated as an optimization problem that can minimize the energy consumption of mobile device while satisfying the constraints of computation tasks and mobile device resources. The optimization problem is equivalently transformed into solving a nonlinear equation with a linear inequality constraint by leveraging the Lagrange Multiplier method. And the proposed dual Bi-Section Search algorithm Bi-JOTD can efficiently solve the nonlinear equation. In the outer Bi-Section Search, the proposed algorithm searches for the optimal Lagrangian multiplier variable between the lower and upper boundaries. The inner Bi-Section Search achieves the Lagrangian multiplier vector corresponding to a given variable receiving from the outer layer. Numerical results demonstrate that the proposed algorithm has significant performance improvement than other baselines. The novel scheme not only reduces the difficulty of problem solving, but also obtains less energy consumption and better performance.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.310-328
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• 2022

Internet of things (IoT) has emerged as the most popular technique that facilitates enhancing humans' quality of life. However, most time sensitive IoT applications require quick response time. So, processing these IoT applications in cloud servers may not be effective. Therefore, fog computing has emerged as a promising solution that addresses the problem of managing large data bandwidth requirements of devices and quick response time. This technology has resulted in processing a large amount of data near the data source compared to the cloud. However, efficient management of computing resources involving balancing workload, allocating resources, provisioning resources, and scheduling tasks is one primary consideration for effective computing-based solutions, specifically for time-sensitive applications. This paper provides a comprehensive review of the source management strategies considering resource limitations, heterogeneity, unpredicted traffic in the fog computing environment. It presents recent developments in the resource management field of the fog computing environment. It also presents significant management issues such as resource allocation, resource provisioning, resource scheduling, task offloading, etc. Related studies are compared indifferent mentions to provide promising directions of future research by fellow researchers in the field.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1282-1301
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• 2015

Traditional infrastructure has been superseded by cloud computing, due to its cost-effective and ubiquitous computing model. Cloud computing not only brings multitude of opportunities, but it also bears some challenges. One of the key challenges it faces is recovery of computing nodes, when an Information Technology (IT) failure occurs. Since cloud computing mainly depends upon its nodes, physical servers, that makes it very crucial to recover a failed node in time and seamlessly, so that the customer gets an expected level of service. Work has already been done in this regard, but it has still proved to be trivial. In this study, we present a Cost-Time aware Genetic scheduling algorithm, referred to as CTaG, not only to globally optimize the performance of the cloud system, but also perform recovery of failed nodes efficiently. While modeling our work, we have particularly taken into account the factors of network bandwidth and customer's monetary cost. We have implemented our algorithm and justify it through extensive simulations and comparison with similar existing studies. The results show performance gain of our work over the others, in some particular scenarios.

• Journal of Internet Computing and Services
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• v.18 no.5
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• pp.79-85
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• 2017

This paper proposes the use of mobile ad-hoc cloud for reducing the process time of panoramic image generation in mobile smart devices. In order to effectively assign tasks relevant to panoramic image generation to the mobile ad-hoc cloud, a method for image acquisition and sorting and an algorithm for task distribution and offloading decision making are proposed. The proposed methods are applied to Android OS based smart devices, and their effects on panoramic image generation are analyzed.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.443-456
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• 2022

An efficient and reasonable resource allocation strategy can greatly improve the service quality of Internet of Vehicles (IoV). However, most of the current allocation methods have overestimation problem, and it is difficult to provide high-performance IoV network services. To solve this problem, this paper proposes a network resource allocation strategy based on deep learning network model DDQN. Firstly, the method implements the refined modeling of IoV model, including communication model, user layer computing model, edge layer offloading model, mobile model, etc., similar to the actual complex IoV application scenario. Then, the DDQN network model is used to calculate and solve the mathematical model of resource allocation. By decoupling the selection of target Q value action and the calculation of target Q value, the phenomenon of overestimation is avoided. It can provide higher-quality network services and ensure superior computing and processing performance in actual complex scenarios. Finally, simulation results show that the proposed method can maintain the network delay within 65 ms and show excellent network performance in high concurrency and complex scenes with task data volume of 500 kbits.