• Journal of Information Processing Systems
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• 제17권3호
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• pp.615-629
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• 2021

With the development of mobile edge computing, how to utilize the computing power of edge computing to effectively and efficiently offload data and to compute offloading is of great research value. This paper studies the computation offloading problem of multi-user and multi-server in mobile edge computing. Firstly, in order to minimize system energy consumption, the problem is modeled by considering the joint optimization of the offloading strategy and the wireless and computing resource allocation in a multi-user and multi-server scenario. Additionally, this paper explores the computation offloading scheme to optimize the overall cost. As the centralized optimization method is an NP problem, the game method is used to achieve effective computation offloading in a distributed manner. The decision problem of distributed computation offloading between the mobile equipment is modeled as a multi-user computation offloading game. There is a Nash equilibrium in this game, and it can be achieved by a limited number of iterations. Then, we propose a distributed computation offloading algorithm, which first calculates offloading weights, and then distributedly iterates by the time slot to update the computation offloading decision. Finally, the algorithm is verified by simulation experiments. Simulation results show that our proposed algorithm can achieve the balance by a limited number of iterations. At the same time, the algorithm outperforms several other advanced computation offloading algorithms in terms of the number of users and overall overheads for beneficial decision-making.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.226-238
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• 2024

Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.

• Journal of Information Processing Systems
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• 제17권5호
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• pp.905-917
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• 2021

In recent years, edge computing technology consists of several Internet of Things (IoT) devices with embedded sensors that have improved significantly for monitoring, detection, and management in an environment where big data is commercialized. The main focus of edge computing is data optimization or task offloading due to data and task-intensive application development. However, existing offloading approaches do not consider correlations and associations between data and tasks involving edge computing. The extent of collaborative offloading segmented without considering the interaction between data and task can lead to data loss and delays when moving from edge to edge. This article proposes a range segmentation of dynamic offloading (RSDO) algorithm that isolates the offload range and collaborative edge node around the edge node function to address the offloading issue.The RSDO algorithm groups highly correlated data and tasks according to the cause of the overload and dynamically distributes offloading ranges according to the state of cooperating nodes. The segmentation improves the overall performance of edge nodes, balances edge computing, and solves data loss and average latency.

• 한국통신학회논문지
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• 제42권2호
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• pp.514-522
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• 2017

트래픽 오프로딩은 폭발적으로 증가하는 모바일 트래픽에 대응하기 위한 유망 솔루션이다. 오프로딩 방법 중, LIPA/SIPTO 오프로딩에서는 애플리케이션의 QoS 요구사항을 만족하면서 트래픽을 오프로딩할 수 있다. 또한, SNS로 인한 많은 트래픽때문에 social context를 이용한 트래픽 오프로딩이 필요하다. 그러므로, 본 논문에서는 social context를 이용하여 트래픽을 오프로딩하는 LIPA/SIPTO 오프로딩 알고리즘을 제안한다. 먼저, 애플리케이션 인기도를 social context로 이용하여 애플리케이션 선택확률을 정의한다. 그 다음, effective data rate 관점에서 소형셀 사용자의 QoS를 최대화하는 최적의 오프로딩 weighting factor를 찾는다. 마지막으로, 애플리케이션 선택확률과 오프로딩 weighting factor를 기반으로 각 애플리케이션의 오프로딩 비율을 정한다. 성능분석 결과, 제안한 알고리즘의 오프로딩 비율이 기존 알고리즘의 약 46%임에도 불구하고, 제안한 알고리즘의 effective data rate achievement ratio 값이 기존 알고리즘과 비슷한 것을 확인하였다.

• ETRI Journal
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• 제45권4호
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• pp.627-635
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• 2023

In vehicular edge computing (VEC) networks, the rapid expansion of intelligent transportation and the corresponding enormous numbers of tasks bring stringent requirements on timely task offloading. However, many tasks typically appear within a short period rather than arriving simultaneously, which makes it difficult to realize effective and efficient resource scheduling. In addition, some key information about tasks could be learned due to the regular data collection and uploading processes of sensors, which may contribute to developing effective offloading strategies. Thus, in this paper, we propose a model that considers the deterministic demand of multiple tasks. It is possible to generate effective resource reservations or early preparation decisions in offloading strategies if some feature information of the deterministic demand can be obtained in advance. We formulate our scenario as a 0-1 programming problem to minimize the average delay of tasks and transform it into a convex form. Finally, we proposed an efficient optimal offloading algorithm that uses the interior point method. Simulation results demonstrate that the proposed algorithm has great advantages in optimizing offloading utility.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2002년도 추계학술발표논문집 (중)
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• pp.1607-1610
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• 2002

TCP/IP 의 성능을 향상 시키기 위하여 주로 커널 레벨에 구현되어 있는 TCP/IP 프로토콜 계층을 네트워크 인터페이스 카드로 이전시키는 기술을 TCP/IP offloading 기술이라고 한다. 본 논문은 최근의 TCP/IP offloading기술의 동향을 살펴보고 또한 Tcp/IP offloading 기술을 시스템에 적용할 때 고려해야 할 점등을 기술한다.

• 인터넷정보학회논문지
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• 제14권2호
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• pp.73-85
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• 2013

제한된 모바일 디바이스의 자원을 해결하기 위해, 클라우드에 있는 서비스 또는 자원을 활용하는 모바일 클라우드 컴퓨팅(Mobile Cloud Computing, MCC) 연구가 활발히 진행되고 있다. MCC에서는 주로 기능 컴포넌트를 다른 노드로 오프로딩 (Offloading) 시킴으로써, 모바일 노드의 자원 문제를 해결하는 접근법을 주로 사용한다. 그러나, 현재 진행되고 있는 MCC에 대한 연구는 사전에 결정된 노드로 오프로딩 시키는 기법들이 주로 진행되고 있으며, 개념적인 수준에서 기법이 제시되고 있다. 본 논문에서는 복잡도가 높은 모바일 애플리케이션의 성능을 보장하기 위한 4가지 종류의 오프로딩 기법을 제안한다. 제시된 기법은 구현이 가능하도록 실용적인 수준으로 설계되며, 비용 모델을 제시하여 오프로딩을 통한 성능향상이 있음을 정량적으로 증명한다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.191-195
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• 2006

This paper discusses wheel offloading approaches of COMS which has a single side solar array system for the accommodation of the optical payloads. First of all, in an effort to reduce fuel consumption and reflect practical implementation point of view, thruster sets for wheel offloading are proposed based on numerical analyses taking into account the COMS configuration. In this analysis, it is assumed that the wheel offloading is conducted twice a day. Secondly, in order to evaluate the effectiveness of the proposed thruster sets, orbit simulations have been conducted for several wheel offloading approaches and compared.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.952-973
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• 2021

In recent years, mobile devices have become an essential part of daily life. More and more applications are being supported by mobile devices thanks to edge computing, which represents an emergent architecture that provides computing, storage, and networking capabilities for mobile devices. In edge computing, heavy tasks are offloaded to edge nodes to alleviate the computations on the mobile side. However, offloading computational tasks may incur extra energy consumption and delays due to network congestion and server queues. Therefore, it is necessary to optimize offloading decisions to minimize time, energy, and payment costs. In this article, different offloading models are examined to identify the offloading parameters that need to be optimized. The paper investigates and compares several optimization techniques used to optimize offloading decisions, specifically Swarm Intelligence (SI) models, since they are best suited to the distributed aspect of edge computing. Furthermore, based on the literature review, this study concludes that a Cuckoo Search Algorithm (CSA) in an edge-based architecture is a good solution for balancing energy consumption, time, and cost.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.163-172
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• 2022

The interconnection of an enormous number of devices into the Internet at a massive scale is a consequence of the Internet of Things (IoT). As a result, tasks offloading from these IoT devices to remote cloud data centers become expensive and inefficient as their number and amount of its emitted data increase exponentially. It is also a challenge to optimize IoT device energy consumption while meeting its application time deadline and data delivery constraints. Consequently, Fog Computing was proposed to support efficient IoT tasks processing as it has a feature of lower service delay, being adjacent to IoT nodes. However, cloud task offloading is still performed frequently as Fog computing has less resources compared to remote cloud. Thus, optimized schemes are required to correctly characterize and distribute IoT devices tasks offloading in a hybrid IoT, Fog, and cloud paradigm. In this paper, we present a detailed survey and classification of of recently published research articles that address the energy efficiency of task offloading schemes in IoT-Fog-Cloud paradigm. Moreover, we also developed a taxonomy for the classification of these schemes and provided a comparative study of different schemes: by identifying achieved advantage and disadvantage of each scheme, as well its related drawbacks and limitations. Moreover, we also state open research issues in the development of energy efficient, scalable, optimized task offloading schemes for Fog computing.