• Journal of Korea Society of Industrial Information Systems
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• 제4권4호
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• pp.79-87
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• 1999

This paper proposes a searching system with a mobile agent which retrieve data from the distributed hosts. The proposed system dispatched the movable objects, so called AGLET, to the distributed hosts in order to perform a task given by a client or a server. The network of the existing system must not be disturbed during a task is performing. However our system has the strong point that a task can be performed even if the network is disconnected on the way. When the network is disconnected, the system can get the results later after the network system is connected again. Designing the system has been done by using UML(Unified Modeling Language) which is a standardized object-oriented modeling language. AGLET, a pure JAVA product of IBM, is used for the mobile agent.

• Journal of Information Processing Systems
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• 제17권4호
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• pp.721-736
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• 2021

For the mobile edge computing (MEC) system supporting dense network, a joint allocation algorithm of computing and communication resources based on reinforcement learning is proposed. The energy consumption of task execution is defined as the maximum energy consumption of each user's task execution in the system. Considering the constraints of task unloading, power allocation, transmission rate and calculation resource allocation, the problem of joint task unloading and resource allocation is modeled as a problem of maximum task execution energy consumption minimization. As a mixed integer nonlinear programming problem, it is difficult to be directly solve by traditional optimization methods. This paper uses reinforcement learning algorithm to solve this problem. Then, the Markov decision-making process and the theoretical basis of reinforcement learning are introduced to provide a theoretical basis for the algorithm simulation experiment. Based on the algorithm of reinforcement learning and joint allocation of communication resources, the joint optimization of data task unloading and power control strategy is carried out for each terminal device, and the local computing model and task unloading model are built. The simulation results show that the total task computation cost of the proposed algorithm is 5%-10% less than that of the two comparison algorithms under the same task input. At the same time, the total task computation cost of the proposed algorithm is more than 5% less than that of the two new comparison algorithms.

• Journal of Internet Computing and Services
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• 제18권1호
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• pp.1-9
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• 2017

Innovations from current researches on cloud computing such as applying bio-inspired computing techniques have brought new level solutions in offloading mechanisms. With the growing trend of mobile devices, mobile cloud computing can also benefit from applying bio-inspired techniques. Energy-efficient offloading mechanisms on mobile cloud systems are needed to reduce the total energy consumption but previous works did not consider energy consumption in the decision-making of task distribution. This paper proposes the Particle Swarm Optimization (PSO) as an offloading strategy of cloudlet to data centers where each task is represented as a particle during the process. The collected tasks are classified using K-means clustering on the cloudlet before applying PSO in order to minimize the number of particles and to locate the best data center for a specific task, instead of considering all tasks during the PSO process. Simulation results show that the proposed PSO excels in choosing data centers with respect to energy consumption, while it has accumulated a little more processing time compared to the other approaches.

• Journal of Internet Computing and Services
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• 제17권6호
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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.

• Journal of Korea Society of Digital Industry and Information Management
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• 제9권1호
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• pp.53-58
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• 2013

In this paper, we proposed low power algorithm for battery residual capacity and a task. Algorithm the mobile devices power of the battery residual capacity for the task to perform power consumption to reduce the frequency alters. Task is different in power consumption according to kinds of in time accomplishment device to use. Adjustment of power consumption analyzes kinds of given tasks from having the minimum power consumption task to having the maximum power consumption task. Control frequency so that power consumption waste to be exposed to battery residual capacity can be happened according to the results analyzed. Experiment the frequency by adjusting power consumption a method to reduce using [7] and in the same environment power of the battery residual capacity consider the task to perform frequency were controlled. Efficiency was proved compare with the experiment results [7]. The experiments results show increment in the number of processing by 45.46% comparing with that [7] algorithm.

• Fashion & Textile Research Journal
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• 제23권1호
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• pp.70-83
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• 2021

Live-streaming commerce is attracting attention as a noticeable trend in the retail industry. It is a new mobile shopping service platform developed by combining live streaming with e-commerce technologies. This study examined the impact of para-social interactions on consumer impulse buying behavior and investigated the impact through task complexity as well as perceived amount of information. To achieve this goal, 203 women using a mobile commerce participated in an online survey after experiencing beauty live-streaming commerce. The collected data were analyzed using SPSS 25.0, AMOS 23.0, and SPSS PROCESS Macro program. The results of the study revealed that para-social interactions negatively influenced task complexity, positively influenced perceived amounts of information, and positively influenced impulse buying behavior. In addition, impulse buying behavior was negatively influenced by task complexity versus positively that was influenced by perceived amounts of information. The impact of para-social interactions on impulse buying behavior is mediated by task complexity and perceived information. The findings of this study contribute to the theoretical extension of para-social interaction on impulse buying behavior in the context of live-streaming commerce. The implications of the findings suggest practical marketing strategies for digital media commerce retailers.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• 제4권5호
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• pp.153-160
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• 2015

The effectiveness of multicores depends on how well a scheduler can assign tasks onto the cores efficiently. In a heterogeneous multicore platform, the execution time of an application depends on which core it executes on. That is to say, the effectiveness of task assignment is one of the important components for a multicore systems' performance. This work proposes a load scheduling technique that analyzes execution time of each task by profiling. The profiling result provides a basic information to predict which task-to-core mapping is likely to provide the best performance. By using such information, the proposed technique is about 26% performance gain.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권6호
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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.

• Journal of Information Processing Systems
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• 제19권4호
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• pp.450-464
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• 2023

To address the problems of large system overhead and low timeliness when dealing with task scheduling in mobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computing based on an improved genetic algorithm was proposed. First, a user task scheduling system model based on edge cloud computing was constructed using the Shannon theorem, including calculation, communication, and network models. In addition, a multi-objective optimization model, including delay and energy consumption, was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operations of the genetic algorithm were improved using the best reservation selection algorithm and normal distribution crossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objective problem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimental analysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss does not exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.