• Journal of KIISE:Computing Practices and Letters
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• v.16 no.2
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• pp.212-216
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• 2010

Dynamic Thermal Management (DTM) degrades the processor performance for lowering temperature. For this reason, reducing the peak temperature on microprocessors can improve the performance by reducing the performance loss due to DTM. In this study, we analyze various unit selection techniques for computation migration. According to our simulation results, dynamic computation migration based on the thermal difference between the units shows best performance among compared models.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.419-421
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.238-240
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• Geophysics and Geophysical Exploration
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• v.11 no.1
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• pp.60-67
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• 2008

In this paper we consider an application of the method of electromagnetic (EM) migration to the interpretation of a typical marine controlled-source (MCSEM) survey consisting of a set of sea-bottom receivers and a moving electrical bipole transmitter. Three-dimensional interpretation of MCSEM data is a very challenging problem because of the enormous number of computations required in the case of the multi-transmitter and multi-receiver data acquisition systems used in these surveys. At the same time, we demonstrate that the MCSEM surveys with their dense system of transmitters and receivers are extremely well suited for application of the migration method. In order to speed up the computation of the migration field, we apply a fast form of integral equation (IE) solution based on the multigrid quasi-linear (MGQL) approximation which we have developed. The principles of migration imaging formulated in this paper are tested on a typical model of a sea-bottom petroleum reservoir.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.60-66
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• 2024

Host's data during transmission. Data tempering results in loss of host's sensitive information, which includes number of VM, storage availability, and other information. In the distributed cloud environment, each server (computing server (CS)) configured with Local Resource Monitors (LRMs) which runs independently and performs Virtual Machine (VM) migrations to nearby servers. Approaches like predictive VM migration [21] [22] by each server considering nearby server's CPU usage, roatative decision making capacity [21] among the servers in distributed cloud environment has been proposed. This approaches usage underlying server's computing power for predicting own server's future resource utilization and nearby server's resource usage computation. It results in running VM and its running application to remain in waiting state for computing power. In order to reduce this, a decentralized decision making hybrid model for VM migration need to be proposed where servers in decentralized cloud receives, future resource usage by analytical computing system and takes decision for migrating VM to its neighbor servers. Host's in the decentralized cloud shares, their detail with peer servers after fixed interval, this results in chance to tempering messages that would be exchanged in between HC and CH. At the same time, it reduces chance of over utilization of peer servers, caused due to compromised host. This paper discusses, an roatative decisive (RD) approach for VM migration among peer computing servers (CS) in decentralized cloud environment, preserving confidentiality and integrity of the host's data. Experimental result shows that, the proposed predictive VM migration approach reduces extra VM migration caused due over utilization of identified servers and reduces number of active servers in greater extent, and ensures confidentiality and integrity of peer host's data.

• Proceedings of the KSME Conference
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• 2001.06d
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• pp.97-103
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• 2001

The effects of the electrohydrodynamic (EHD) flow and turbulent diffusion on the collection efficiency of a model ESP composed of the plates with a cavity were studied through numerical computation. The electric field and ion space charge density were calculated by the Poisson equation of the electrical potential and the current continuity equation. The EHD flow field was solved by the continuity and momentum equations of the gas phase including the electrical body force induced by the movement of ions under the electric field. The RNG $k-{\varepsilon}$ model was used to analyze the turbulent flow. The particle concentration distribution was calculated from the convective diffusion equation of the particle phase. As the ion space charge increased, the particulate collection efficiency increased because the electrical potential increased over the entire domain in the ESP. The collection efficiency decreased and then increased, i.e. had a minimum value, as the EHD circulating flow became stronger when the electrical migration velocity of the charged particle was low. However, the collection efficiency decreased with the stronger EHD flow when the electrical migration of the particle was higher relatively. The collection efficiency of the model ESP increased as the turbulent diffusivity of the particle increased when the electrical migration velocity of the particle was low. However, the collection efficiency decreased for increasing the turbulent diffusivity when the electrical migration of the particle was higher relatively.

• The Korean Journal of Petroleum Geology
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• v.1 no.1 s.1
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• pp.47-52
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• 1993

A method and results of computations are presented for the 2-D seismic migration process in the frequency-wavenumber domain for the laterally and vertically inhomogeneous medium. In order to take the intrinsic attenuation effect into account in the migration process the complex-valued wave velocity is used in the wavefield extrapolation operator, improving the generalized frequency-wavenumber migration technique. The imaginary part of the complex-valued wave velocity includes the seismic quality factor Q value. In derivation of the solution of the wave equation for the medium of inhomogeneous wave velocity and anelasticity, the inhomogeneous medium is mathematically converted to an equivalent system which consists of a homogeneous medium of averaged slowness and an inhomogeneous distribution of hypothetical wave source. The strength of the hypothetical wave source depends on the deviation of squared slowness from the averaged value of the medium. Results of numerical computation using the technique show more distinct geologic images than those using the convensional generalized frequency-wavenumber migration. Especially, the obscured images due to the wave attenuation by anelasticity are restored to show sharp boundaries of structures. The method will be useful in the imaging of the reflection data obtained in the regions of possible petroleum or natural gas reservoir and of fractured zone.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3916-3936
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• 2014

Smartphone applications like games, image processing, e-commerce and social networking are gaining exponential growth, with the ubiquity of cellular services. This demands increased computational power and storage from mobile devices with a sufficiently high bandwidth for mobile internet service. But mobile nodes are highly constrained in the processing and storage, along with the battery power, which further restrains their dependability. Adopting the unlimited storage and computing power offered by cloud servers, it is possible to overcome and turn these issues into a favorable opportunity for the growth of mobile cloud computing. As the mobile internet data traffic is predicted to grow at the rate of around 65 percent yearly, even advanced services like 3G and 4G for mobile communication will fail to accommodate such exponential growth of data. On the other hand, developers extend popular applications with high end graphics leading to smart phones, manufactured with multicore processors and graphics processing units making them unaffordable. Therefore, to address the need of resource constrained mobile nodes and bandwidth constrained cellular networks, the computations can be migrated to resourceful servers connected to cloud. The server now acts as a bridge that should enable the participating mobile nodes to offload their computations through Wi-Fi directly to the virtualized server. Our proposed model enables an on-demand service offloading with a decision support system that identifies the capabilities of the client's hardware and software resources in judging the requirements for offloading. Further, the node's location, context and security capabilities are estimated to facilitate adaptive migration.

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.11
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• pp.581-591
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• 2001

We consider some problems of remote file accesses of multiple clients in mobile computing environments. In this environment, user mobility affects file access latency. Mobile hosts have severe resource constraints in terms of limited size of non-volatile storage. Thus, the burden of computation and communication load raise file access latency. In this paper, we propose a scheme for reducing the file access latency through the file migration. The objective is to minimize of file accesses for all mobile hosts which delivering the file to clients as quickly as possible. We develope an on-demand scheme which determines when the file server should migrate files to another server, or when it should transfer files to mobile hosts. Using simulation, we examine the effects which parameters such as file access frequency, file size, mobility rate have on file system access latency. Through simulation results, we show that our proposed migration scheme is effective in reducing the access latency on the requested file of a mobile host with high access rate and low mobility.

• Economic and Environmental Geology
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• v.39 no.6 s.181
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• pp.711-717
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• 2006

In order to study gas hydrate, potential future energy resources, Korea Institute of Geoscience and Mineral Resources has conducted seismic reflection survey in the East Sea since 1997. one of evidence for presence of gas hydrate in seismic reflection data is a bottom simulating reflector (BSR). The BSR occurs at the interface between overlaying higher velocity, hydrate-bearing sediment and underlying lower velocity, free gas-bearing sediment. That is often characterized by large reflection coefficient and reflection polarity reverse to that of seafloor reflection. In order to apply depth migration to seismic reflection data. we need high performance computers and a parallelizing technique because of huge data volume and computation. Phase shift plus interpolation (PSPI) is a useful method for migration due to less computing time and computational efficiency. PSPI is intrinsically parallelizing characteristic in the frequency domain. We conducted conventional data processing for the gas hydrate data of the Ease Sea and then applied prestack depth migration using message-passing-interface PSPI (MPI_PSPI) that was parallelized by MPI local-area-multi-computer (MPI_LAM). Velocity model was made using the stack velocities after we had picked horizons on the stack image with in-house processing tool, Geobit. We could find the BSRs on the migrated stack section were about at SP 3555-4162 and two way travel time around 2,950 ms in time domain. In depth domain such BSRs appear at 6-17 km distance and 2.1 km depth from the seafloor. Since energy concentrated subsurface was well imaged we have to choose acquisition parameters suited for transmitting seismic energy to target area.