• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1712-1732
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• 2011

Cloud provides dynamically scalable virtualized computing resources as a service over the Internet. To achieve higher resource utilization over virtualization technology, an optimized strategy that deploys virtual machines on physical machines is needed. That is, the total number of active physical host nodes should be dynamically changed to correspond to their resource usage rate, thereby maintaining optimum utilization of physical machines. In this paper, we propose a pattern-based prediction model for resource provisioning which facilitates best possible resource preparation by analyzing the resource utilization and deriving resource usage patterns. The focus of our work is on predicting future resource requests by optimized dynamic resource management strategy that is applied to a virtualized data center in a Cloud computing environment. To this end, we build a prediction model that is based on user request patterns and make a prediction of system behavior for the near future. As a result, this model can save time for predicting the needed resource amount and reduce the possibility of resource overuse. In addition, we studied the performance of our proposed model comparing with conventional resource provisioning models under various Cloud execution conditions. The experimental results showed that our pattern-based prediction model gives significant benefits over conventional models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2783-2804
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• 2021

In the network function virtualization environment, dynamic changes in network traffic will lead to the dynamic changes of service function chain resource demand, which entails timely dynamic adjustment of service function chain resource configuration. At present, most researches solve this problem through virtual network function migration and link rerouting, and there exist some problems such as long service interruption time, excessive network operation cost and high penalty. This paper proposes a dynamic adjustment method of service function chain resource configuration for the dynamic changes of network traffic. First, a dynamic adjustment request of service function chain is generated according to the prediction of network traffic. Second, a dynamic adjustment strategy of service function chain resource configuration is determined according to substrate network resources. Finally, the resource configuration of a service function chain is pre-adjusted according to the dynamic adjustment strategy. Virtual network functions combination and virtual machine reusing are fully considered in this process. The experimental results show that this method can reduce the influence of service function chain resource configuration dynamic adjustment on quality of service, reduce network operation cost and improve the revenue of service providers.

• Journal of Information Processing Systems
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• v.17 no.3
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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.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.1
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• pp.99-113
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• 2004

A new architecture called SAN(Storage Area Network) was developed in response to the requirements of high availability of data, scalable growth, and system performance. In order to use SAN more efficiently, most of SAN operating softwares support storage virtualization concepts that allow users to view physical storage devices attached to SAN as a large volume virtually h logical volume manager plays a key role in storage virtualization. It realizes the storage virtualization by mapping logical addresses to physical addresses. A logical volume manager also supports a snapshot that preserves a volume image at certain time and on-line reorganization to allow users to add/remove storage devices to/from SAN even while the system is running. To support the snapshot and the on-line reorganization, most logical volume managers have used table based mapping methods. However, it is very difficult to manage mapping table because the mapping table is large in proportion to a storage capacity. In this paper, we design and implement an efficient and flexible hybrid mapping method based on mathematical equations. The mapping method in this paper supports a snapshot and on-line reorganization. The proposed snapshot and on-line reorganization are performed on the reserved area which is separated from data area of a volume. Due to this strategy normal I/O operations are not affected by snapshot and reorganization. Finally, we show the superiority of our proposed mapping method through various experiments.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.23 no.3
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• pp.57-84
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• 2012

With the advent of Big data era unleashed by tremendous increase of structured and unstructured data, a library needs an effective method to store, manage and preserve various information resources. Also, needs of collaboration of libraries are continuously increased in digital environment. As an effective method to handle the changes and challenges in libraries, interest on cloud computing is growing more and more. This study aims to propose a method to introduce cloud computing in libraries. To achieve the goals, this study performed the literature review to analyze problems of existing library systems. Also, this study proposed considerations, expectations, service scenario, phased strategy to introduce cloud computing in libraries. Based on the results extracted from cases that libraries have introduced cloud computing-based systems, this study proposed introduction strategy and specific applying areas in library works as considered features of cloud computing models. The proposed phased strategy and service scenario may reduce time and effort in the process of introduction of cloud computing and maximize the effect of cloud computing.

• Korean Management Science Review
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• v.30 no.2
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• pp.11-30
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• 2013

Small and medium-sized enterprises (SMEs) constitute an important part of current industrial economies. Information technologies can be useful strategic weapons for SMEs by enhancing their competitiveness. Categorized as one kind of cloud computing, SaaS is a computing resource and software sharing model which can be accessed via the Internet. Based on virtualization technology, SaaS is expected to improve the efficiency and quality of the IT service level in SMEs. This study attempts to identify the determinants of SaaS adoption intention among Korean SMEs. Through the lens of the theory of planned behavior, this study adopts technological, organizational, environmental factors to explore the determinants of cloud computing adoption intention. The research population is the SMEs that have been funded by the Korean government. Partial least square method was used for empirical analysis of 190 samples collected through on-line survey. The results show that the positive attitude is influenced by business process improvements. Vender support and top management support are positively associated with subjective norm. Vendor support, top management support can relieve perceived behavior control factors. Government support directly influences adoption intention of cloud computing. These findings can provide useful strategy for both SMEs and vendors of SaaS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.514-535
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• 2019

Information-centric networks operate under the assumption that all network components have built-in caching capabilities. Integrating the caching strategies of information centric networking (ICN) with wireless virtualization improves the gain of virtual infrastructure content caching. In this paper, we propose a framework for software-defined information centric virtualized wireless device-to-device (D2D) networks. Enabling D2D communications in virtualized ICN increases the spectral efficiency due to reuse and proximity gains while the software-defined network (SDN) as a platform also simplifies the computational overhead. In this framework, we propose a joint virtual resource and cache allocation solution for latency-sensitive applications in the next-generation cellular networks. As the formulated problem is NP-hard, we design low-complexity heuristic algorithms which are intuitive and efficient. In our proposed framework, different services can share a pool of infrastructure items. We evaluate our proposed framework and algorithm through extensive simulations. The results demonstrate significant improvements in terms of visiting latency, end user QoE, InP resource utilization and MVNO utility gain.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4364-4384
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• 2021

The dynamic network state and the mobility of the terminals make the service function chain (SFC) orchestration mechanisms based on static and deterministic assumptions hard to be applied in SDN/NFV mobile edge computing networks. Designing dynamic and online SFC orchestration mechanism can greatly improve the execution efficiency of compute-intensive and resource-hungry applications in mobile edge computing networks. In order to increase the overall profit of service provider and reduce the resource cost, the system running time is divided into a sequence of time slots and a dynamic orchestration scheme based on an improved column generation algorithm is proposed in each slot. Firstly, the SFC dynamic orchestration problem is formulated as an integer linear programming (ILP) model based on layered graph. Then, in order to reduce the computation costs, a column generation model is used to simplify the ILP model. Finally, a two-stage heuristic algorithm based on greedy strategy is proposed. Four metrics are defined and the performance of the proposed algorithm is evaluated based on simulation. The results show that our proposal significantly provides more than 30% reduction of run time and about 12% improvement in service deployment success ratio compared to the Viterbi algorithm based mechanism.

• Journal of Platform Technology
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• v.10 no.4
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• pp.70-81
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• 2022

In this paper, we proposed a Virtual Machine Placement (VMP) method to provide live vertical scaling services for cloud resources. Since free space on the physical server must be secured in advance for vertical scaling, a "general-mixed-vertical" mode conversion algorithm based on the FirstFit placement strategy that variably adjusts the allocation ratio of virtual servers to physical servers for this purpose is presented. Simulations were performed using parameters such as vertical scaling ratio, virtualization ratio, and free resource ratio. When the vertical scaling ratio is 50%, considering free space, 150% of resources are required as a whole, but simulation results of the proposed algorithm show that only up to 125% of free space is required.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4748-4765
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• 2016

We consider a virtualized cognitive radio (CR) network, where multiple virtual network operators (VNOs) who own different virtual cognitive base stations (VCBSs) share the same physical CBS (PCBS) which is owned by an infrastructure provider (InP), sharing the spectrum with the primary user (PU). The uplink scenario is considered where the secondary users (SUs) transmit to the VCBSs. The PU is protected by constraining the interference power from the SUs. Such constraint is applied by the InP through pricing the interference. A Stackelberg game is formulated to jointly maximize the revenue of the InP and the individual utilities of the VNOs, and then the Stackelberg equilibrium is investigated. Specifically, the optimal interference price and channel allocation for the VNOs to maximize the revenue of the InP and the optimal power allocation for the SUs to maximize the individual utilities of the VNOs are derived. In addition, a low‐complexity ±‐optimal solution is also proposed for obtaining the interference price and channel allocation for the VNOs. Simulations are provided to verify the proposed strategies. It is shown that the proposed strategies are effective in resource allocation and the ±‐optimal strategy achieves practically the same performance as the optimal strategy can achieve. It is also shown that the InP will not benefit from a large interference power limit, and selecting VNOs with higher unit rate utility gain to share the resources of the InP is beneficial to both the InP and the VNOs.