• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.608-611
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• 2004

Grid computing environment can be used to interconnect a wide variety of geographically distributed heterogeneous computing resources based on high-speed network. To make business service, it is necessary for Grid accounting system to measure the computational cost by consuming computer resources. To collect resource consumption data, and to keep track of process without needing to recompile kernel source, we use system call wrapping. By making use of this technique, we modifies system call table and replace existing system call to new system call that can monitor processes running in CPU kernel currently. Therefore we can measure user process resource usage for Grid accounting system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5174-5190
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• 2016

Recent years have witnessed an explosive growth of mobile devices, mobile cloud computing services offered by these devices and the remote clouds behind them. In this paper, we noticed ultra-low latency service, as a type of mobile cloud computing service, requires extremely short delay constraints. Hence, such delay-sensitive applications should be satisfied with strong QoS guarantee. Existing solutions regarding this problem have poor performance in terms of throughput. In this paper, we propose an end-to-end bandwidth resource reservation via software defined scheduling inspired by the famous SDN framework. The main contribution of this paper is the end-to-end resource reservation and flow scheduling algorithm, which always gives priority to delay sensitive flows. Simulation results confirm the advantage of the proposed solution, which improves the average throughput of ultra-low latency flows.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.389-401
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• 2022

The business of Internet of Vehicles (IoV) is growing rapidly, and the large amount of data exchange has caused problems of large mobile network communication delay and large energy loss. A strategy for resource allocation of IoV communication based on mobile edge computing (MEC) is thus proposed. First, a model of the cloud-side collaborative cache and resource allocation system for the IoV is designed. Vehicles can offload tasks to MEC servers or neighboring vehicles for communication. Then, the communication model and the calculation model of IoV system are comprehensively analyzed. The optimization objective of minimizing delay and energy consumption is constructed. Finally, the on-board computing task is coded, and the optimization problem is transformed into a knapsack problem. The optimal resource allocation strategy is obtained through genetic algorithm. The simulation results based on the MATLAB platform show that: The proposed strategy offloads tasks to the MEC server or neighboring vehicles, making full use of system resources. In different situations, the energy consumption does not exceed 300 J and 180 J, with an average delay of 210 ms, effectively reducing system overhead and improving response speed.

• Journal of Information Processing Systems
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• v.20 no.2
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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.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.226-230
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• 2007

This paper proposes a local scheduling method based on user pattern for Volunteer computing project, Korea@Home. It enables Korea@Home participants to run the agent without disturbance. It is devised to prevent user's application from delay while running the agent and decreases the frequency of switching resource between the user and the agent. We analyze the user's patterns of donating computing resource with Korea@Home which is a representative volunteer computing project in Korea. It has contributed the computing power to several applications including climate prediction and virtual screening. It promotes the volunteers to participate continuously without disturbance and increases the potential computing power with non-disturbance scheduling based on user usage pattern for Volunteer Computing.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.69-75
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• 2006

Internet-based distributed computing environment have been developed for advanced science and engineering by sharing large-scale resources. Therefore efficient scheduling algorithms for allocating user job to resources in the Internet-based distributed computing environment are required. Many scheduling algorithms have been proposed. but these algorithms are not suitable for the Internet-based Distributed computing environment. That is the previous scheduling algorithm does not consider peer self-control. In this paper, we propose a Peer Availability Period Prediction Strategy for Internet-based distributed computing environment and show that our Strategy has better performance than other Strategy through extensive simulation.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.12
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• pp.479-486
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• 2017

Volunteer computing is a new computing paradigm that performs operations on idle resources of many nodes. The operation method of the client application for the execution of the volunteer computing is determined by the setting information of the user. Ideal operation requires optimized settings for system features and operating methods of other applications. In this paper, we analyze the usage ratio of CPU and GPU periodically, and develop a manager that dynamically applies optimized options. Through our proposed mechanism, the performance of the task computing is higher than that of the existing Volunteer Computing, and the performance interference is minimized. It is expected that volunteers will be able to provide higher computing resources for Volunteer Computing Project.

• Journal of KIISE:Information Networking
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• v.31 no.1
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• pp.112-122
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• 2004

In this paper, we propose a mobility estimation model based on inner regions in a cell and a dynamic resource reservation scheme which can control dynamically classes of mobile hosts on the mobile network. The mobility estimation model is modeled based on the reducible Markov chain. And the mobility estimation model provides a new hand off probability and a new remaining time for the dynamic resource reservation scheme. The remaining time is n estimated time that mobile hosts can stay in a cell. The dynamic resource reservation scheme can reserve dynamically a requested resource according to the classes of mobile hosts. This scheme can efficiently improve the connection blocking probability and connection dropping probability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.83-104
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• 2011

Mobile grid is a combination of grid computing and mobile computing to build grid systems in a wireless mobile environment. The development of network technology is assisting in realizing mobile grid. Mobile grid based on established grid infrastructures needs effective resource management and reliable job scheduling because mobile grid utilizes not only static grid resources but also dynamic grid resources with mobility. However, mobile devices are considered as unavailable resources in traditional grids. Mobile resources should be integrated into existing grid sites. Therefore, this paper presents a mobile grid middleware interconnecting existing grid infrastructures with mobile resources and a mobile service agent installed on the mobile resources. This paper also proposes a mobile resource reliability-based job scheduling model in order to overcome the unreliability of wireless mobile devices and guarantee stable and reliable job processing. In the proposed job scheduling model, the mobile service agent calculates the mobile resource reliability of each resource by using diverse reliability metrics and predicts it. The mobile grid middleware allocated jobs to mobile resources by predicted mobile resource reliability. We implemented a simulation model that simplifies various functions of the proposed job scheduling model by using the DEVS (Discrete Event System Specification) which is the formalism for modeling and analyzing a general system. We also conducted diverse experiments for performance evaluation. Experimental results demonstrate that the proposed model can assist in improving the performance of mobile grid in comparison with existing job scheduling models.

• Journal of KIBIM
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• v.10 no.3
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• pp.43-53
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• 2020

BIM-based Cloud platform gained popularity coupled with the convergence of Fourth Industrial Revolution technology. However, most of the previous work has not guaranteed sufficient efficiency to meet user requirements according to BIM service. Furthermore, the Cloud environment is only used as a server and it does not consider cloud characteristics. For the processing of High Capacity Data like BIM and using seamless BIM service, Resource management technology is required in the cloud environment. In this paper, to solve the problems, we propose a BIM platform for BaaS and an efficient resource allocation scheme. We also proved the efficiency of resource for the proposed scheme by using existing schemes. By doing this, the proposed scheme looks forward to accelerating the growth of the BaaS through improving the user experience and resource efficiency.