• 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.

• 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.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.489-504
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• 2021

By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization, distributed computing and parallel computing technologies to enable users dynamically obtain computing power, storage space and other services as needed. Applying edge computing architectures to Internet of Vehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicle applications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloading strategy based on the MEC load state is proposed, which not only considers reducing the delay of calculation results by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, the delay factor and the energy consumption factor are introduced according to the characteristics of tasks, and the cost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective of vehicles, the delay preference factor and the energy consumption preference factor are introduced to define the cost of executing a computing task for another computing task. Furthermore, a mathematical optimization model for minimizing the power overhead is constructed with the constraints of time delay and power consumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. The simulation results show that this strategy can effectively reduce the system power consumption by shortening the task execution delay. Finally, we can choose whether to offload computing tasks to MEC server for execution according to the size of two costs. This strategy not only meets the requirements of time delay and energy consumption, but also ensures the lowest cost.

• IEIE Transactions on Smart Processing and Computing
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• 제2권6호
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• pp.367-372
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• 2013

Cloud computing technology has been developing at an increasing expansion rate. Today most of firms are using this technology, making improving the quality of service one of the most important issues. To achieve this, the system must operate efficiently with less idle time and without deteriorating the customer satisfaction. This paper focuses on enhancing the efficiency of a conventional Genetic Algorithm (GA) for task scheduling in cloud computing using Fuzzy Logic (FL). This study collected a group of task schedules and assessed the quality of each task schedule with the user expectation. The work iterates the best scheduling order genetic operations to make the optimal task schedule. General GA takes considerable time to find the correct scheduling order when all the fitness function parameters are the same. GA is an intuitive approach for solving problems because it covers all possible aspects of the problem. When this approach is combined with fuzzy logic (FL), it behaves like a human brain as a problem solver from an existing database (Memory). The present scheme compares GA with and without FL. Using FL, the proposed system at a 100, 400 and 1000 sample size*5 gave 70%, 57% and 47% better improvement in the task time compared to GA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권9호
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• pp.3126-3145
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• 2014

Cloud computing is a computing paradigm in which users can rent computing resources from service providers according to their requirements. A spot instance in cloud computing helps a user to obtain resources at a lower cost. However, a crucial weakness of spot instances is that the resources can be unreliable anytime due to the fluctuation of instance prices, resulting in increasing the failure time of users' job. In this paper, we propose a Genetic Algorithm (GA)-based workflow scheduling scheme that can find the optimal task size of each instance in a spot instance-based cloud computing environment without increasing users' budgets. Our scheme reduces total task execution time even if an out-of-bid situation occurs in an instance. The simulation results, based on a before-and-after GA comparison, reveal that our scheme achieves performance improvements in terms of reducing the task execution time on average by 7.06%. Additionally, the cost in our scheme is similar to that when GA is not applied. Therefore, our scheme can achieve better performance than the existing scheme, by optimizing the task size allocated to each available instance throughout the evolutionary process of GA.

• 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.

• The Journal of Asian Finance, Economics and Business
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• 제6권1호
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• pp.217-229
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• 2019

The purpose of the research is to verify how task characteristics for business and technology characteristics, economic feasibility, technology readiness, organizational factors, environmental factors of cloud computing affect the performance of cloud computing adoption through Fit and Viability. The research aims to verify the relationship among the success factors for adopting cloud computing based on the Fit-Viability model. Respondents who work for IT companies which is using cloud computing in South Korea were chosen. The data was analyzed by the structural equating model. As a result, Task characteristics and Technology characteristics affected Fit in a positive manner, while Technology readiness, Organizational factors and Environmental factors also positively impacted Viability. Fit and Viability both affected the successful adoption of cloud equally. In particular, Environmental factors were proven to have the biggest impacts on Viability, and affected highly indirect impact on the Performance of cloud computing adoption through Viability. Entering the era of the fourth industrial revolution, corporations have established digital transformation strategies to secure a competitive edge while growing continuously, and are also carrying out various digital transformation initiatives. For the success of adoption of foundational technologies, they need to understand not only the decision-making factors of adopting cloud computing, but also the success factors of adopting cloud computing.

• 정보과학회 논문지
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• 제43권6호
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• pp.613-620
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• 2016

본 논문에서는 대규모의 작업을 고성능으로 처리하기 위한 Many-Task Computing(MTC) 기술을 기존의 빅데이터 처리 플랫폼인 Hadoop에 적용하기 위한 MOHA(Many-Task Computing on Hadoop) 프레임워크에 대해 기술한다. 세부적으로는 MOHA의 기본 개념과 개발 동기, 분산 작업 큐에 기반한 PoC(Proof-of-Concept) 수행 결과를 제시하고 향후 연구 방향에 대해서 논의하고자 한다. MTC 응용은 각각의 태스크들이 요구하는 I/O 처리량은 상대적으로 많지 않지만, 동시에 대량의 태스크들을 고성능으로 처리해야하고 이들이 파일을 통해서 통신한다는 특징을 가지고 있다. 따라서 기존의 상대적으로 큰 데이터 블록 사이즈에 기반한 Hadoop 응용과는 또 다른 패턴의 데이터 집약형 워크로드라고 할 수 있다. 이러한 MTC 기술과 빅데이터 기술의 융합을 통해 멀티 응용 플랫폼으로 진화하고 있는 Hadoop 생태계에 신규 프레임워크로서 대규모 계산과학 응용을 실행할 수 있는 MOHA를 추가하여 기여할 수 있을 것이다.

• 방송공학회논문지
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• 제24권6호
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• pp.1122-1133
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• 2019

본 연구팀에서는 YARN 기반의 하둡 플랫폼에서 대규모의 태스크들로 구성된 Many-Task Computing(MTC) 응용들을 효율적으로 지원할 수 있는 신규 프레임워크로서 MOHA(Mtc On HAdoop)를 연구/개발해왔다. MTC 응용들은 수십만 개에서 수백만 개 이상의 대규모 태스크들로 구성되고 각 응용별로 자원의 사용 패턴이 다를 수 있기 때문에, 전체적인 시스템 성능 향상을 위해 MOHA-TaskExecutor(MTC 응용 태스크를 실행하는 주체)의 Adaptive Parallel Computability 기술 연구를 수행하였다. 이는 한 번에 하나의 태스크를 실행하던 기존의 처리 모델을 고도화하여 하나의 TaskExecutor가 동시에 여러 개의 태스크들을 실행함으로써 YARN Container의 병렬 컴퓨팅 능력을 극대화하기 위함이다. 이를 위해 각각의 TaskExecutor들이 "독립적이고, 동적으로" 동시에 실행시키는 MTC 응용 태스크들을 조정할 수 있도록 하였으며, 최적의 동시 실행 태스크 숫자를 찾기 위해서 Hill-Climbing 알고리즘을 활용하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3638-3657
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• 2022

In urban vehicular edge computing (VEC) environments, one edge server always serves many task requests in its coverage which results in the resource-constrained problem. To resolve the problem and improve system utilization, we first design a general hierarchical resource management framework based on typical VEC network structures. Following the framework, a specific interacting protocol is also designed for our decision algorithm. Secondly, a greedy bidding-based multi-hop task scheduling decision algorithm is proposed to realize effective task scheduling in resource-constrained VEC environments. In this algorithm, the goal of maximizing system utility is modeled as an optimization problem with the constraints of task deadlines and available computing resources. Then, an auction mechanism named greedy bidding is used to match task requests to edge servers in the case of multiple hops to maximize the system utility. Simulation results show that our proposal can maximize the number of tasks served in resource constrained VEC networks and improve the system utility.