• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.261-267
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• 2023

Public research, which requires large computational resources, utilizes the supercomputers of the National Supercomputing Center in the Republic of Korea. The average utilization rate of resources over the past three years reached 80%. Therefore, to ensure the operational stability of this national infrastructure, specialized centers have been established to distribute the computational demand concentrated in the national centers. It is necessary to predict the computational demand accurately to build an appropriate resource scale. Therefore, it is important to estimate the inflow and outflow of computational demand between the national and specialized centers to size the resources required to construct specialized centers. We conducted a logit model analysis using the probabilistic utility theory to derive the preferences of individual users for future supercomputer resources. This analysis shows that the computational demand share of specialized centers is 59.5%, which exceeds the resource utilization plan of existing specialized centers.

• Environmental and Resource Economics Review
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• v.29 no.3
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• pp.313-333
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• 2020

This paper shows that when the exchange rate changes, there are conflicting effects on improving the quality and increasing market share of eco-friendly vehicles. In a vertically differentiated duopoly model consisting of high quality clean cars and low quality internal combustion engine cars, I set up a two-stage noncooperative game under perfect information that the quality levels and the prices of the cars are competitively determined. The vehicles are assumed to be produced in countries that use distinct currencies. When the exchange rate of the country that produces low quality cars rises, the producer prefers to intensify competition due to the relatively lowed cost, and the incentive for quality improvement arises from the intension of attempting to reduce the degree of differentiation of quality level. At this time, the clean car manufacturing firm tries to avoid competition due to weakened competitiveness, and increases the quality level to expand quality differentiation. However, in this case, the market share of eco-friendly vehicles shrinks. On the other hand, if the exchange rate changes in the opposite direction, the market share of eco-friendly vehicles is expected to increase, but the quality of both cars are deteriorated, causing a conflict effect.

• 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.

• Convergence Security Journal
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• v.23 no.5
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• pp.81-89
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• 2023

Kubernetes is a representative open-source software for container orchestration, playing a crucial role in monitoring and managing resources allocated to containers. As container environments become prevalent, security threats targeting containers continue to rise, with resource exhaustion attacks being a prominent example. These attacks involve distributing malicious crypto-mining software in containerized form to hijack computing resources, thereby affecting the operation of the host and other containers that share resources. Previous research has focused on detecting resource depletion attacks, so technology to respond when attacks occur is lacking. This paper proposes a reinforcement learning-based dynamic resource management framework for detecting and responding to resource exhaustion attacks and malicious containers running in Kubernetes environments. To achieve this, we define the environment's state, actions, and rewards from the perspective of responding to resource exhaustion attacks using reinforcement learning. It is expected that the proposed methodology will contribute to establishing a robust defense against resource exhaustion attacks in container environments

• Journal of Digital Convergence
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• v.14 no.10
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• pp.303-310
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• 2016

Due to the ossification of the Internet, it is difficult to accommodate variety services. One of the efficient solution to this problem is network virtualization. It allows multiple parallel virtual networks to run on the shared physical infrastructure. It needs new resource allocation mechanism to share efficient physical resources. In this paper, we present efficient bandwidth allocation algorithm for virtual network request with high service priority. Our proposed algorithm can withdraw allocated bandwidth from low-level priority virtual network and maintain low-level virtual network service. We evaluated the performance of our proposed algorithm using simulation and found the improvement of approximately 8% acceptance rate.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.3
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• pp.86-96
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• 2008

In ubiquitous environments, invisible devices and software are connected to one another to provide convenient services to users. In order to provide such services, we must have mobile devices that connect users and services. However, the types of available services have thus far been limited due to the limited resources of mobile devices. This paper proposes a solution to the resource limitation problem of mobile devices by presenting a context-based collaboration system that allows mobile devices to share various nearby resources. Our system has a feature to enable personalized resource sharing by dynamically re-configuring user's preference and resource information.

• Journal of Computing Science and Engineering
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• v.6 no.4
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• pp.287-293
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• 2012

We introduce a methodology for the study of the application-level performance of time-sharing parallel jobs on a set of compute nodes in high performance clusters and report our findings. We assume that parallel jobs arriving at a cluster need to share a set of nodes with the jobs of other users, in that they must compete for processor time in a time-sharing manner and other limited resources such as memory and I/O in a space-sharing manner. Under the assumption, we developed a methodology to simulate job arrivals to a set of compute nodes, and gather and process performance data to calculate the percentage slowdown of parallel jobs. Our goal through this study is to identify a better combination of jobs that minimize performance degradations due to resource sharing and contention. Through our experiments, we found a couple of interesting behaviors for overlapped parallel jobs, which may be used to suggest alternative job allocation schemes aiming to reduce slowdowns that will inevitably result due to resource sharing on a high performance computing cluster. We suggest three job allocation strategies based on our empirical results and propose further studies of the results using a supercomputing facility at the San Diego Supercomputing Center.

• Journal of Communications and Networks
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• v.17 no.6
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• pp.592-601
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• 2015

Multitenancy has gained growing importance with the development and evolution of cloud computing technology. In a multitenant environment, multiple tenants with different demands can share a variety of computing resources (e.g., CPU, memory, storage, network, and data) within a single system, while each tenant remains logically isolated. This useful multitenancy concept offers highly efficient, and cost-effective systems without wasting computing resources to enterprises requiring similar environments for data processing and management. In this paper, we propose a novel approach supporting multitenancy features for Apache Hadoop, a large scale distributed system commonly used for processing big data. We first analyze the Hadoop framework focusing on "yet another resource negotiator (YARN)", which is responsible for managing resources, application runtime, and access control in the latest version of Hadoop. We then define the problems for supporting multitenancy and formally derive the requirements to solve these problems. Based on these requirements, we design the details of multitenant Hadoop. We also present experimental results to validate the data access control and to evaluate the performance enhancement of multitenant Hadoop.

• Journal of Communications and Networks
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• v.11 no.4
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• pp.368-374
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• 2009

This paper focuses on a distributed multiple spectrum pricing scheme to maximize system capacity in next generation multiaccess systems, where multimode user equipments (MUEs) can connect simultaneously to multiple base stations (BSs) with multiple radio access technologies (RATs). The multi-price based scheme provides a distributed decision making for an optimal solution where radio resource allocations are determined by each MUE, unlike most centralized mechanisms where BS controls the whole radio resource. By the proposed optimal solution, MUEs can decide their share of spectrum bands and power allocation according to the spectrum price of each RAT, and at the same time the multiaccess system can achieve maximized total throughput. Numerical analysis shows that the proposed scheme achieves the maximal capacity by distributed resource allocation for the multiaccess system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5800-5818
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• 2018

Interference may occur when several co-located wireless body area networks (WBANs) share the same channel simultaneously, which is compressed by resource scheduling generally. In this paper, a QoS-aware Adaptive Coloring (QAC) scheduling algorithm is proposed, which contains two components: interference sets determination and time slots assignment. The highlight of QAC is to determine the interference graph based on the relay scheme and adapted to the network QoS by multi-coloring approach. However, the frequent resource assignment brings in extra energy consumption and packet loss. Thus we come up with a launch condition for the QAC scheduling algorithm, that is if the interference duration is longer than a threshold predetermined, time slots rescheduling is activated. Furthermore, based on the relative distance and moving speed between WBANs, a prediction model for interference duration is proposed. The simulation results show that compared with the state-of-the-art approaches, the QAC scheduling algorithm has better performance in terms of network capacity, average delay and resource utility.