• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권12호
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• pp.4950-4966
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• 2015

Cloud providers now face the problem of estimating the amount of computing resources required to satisfy a future workload. In this paper, a virtual machine provisioning (VMP) mechanism is designed to adapt workload fluctuation. The arrival rate of forthcoming jobs is predicted for acquiring the proper service rate by adopting an exponential smoothing (ES) method. The proper service rate is estimated to guarantee the service level agreement (SLA) constraints by using a diffusion approximation statistical model. The VMP problem is formulated as a facility location problem. Furthermore, it is characterized as the maximization of submodular function subject to the matroid constraints. A greedy-based VMP algorithm is designed to obtain the optimal virtual machine provision pattern. Simulation results illustrate that the proposed mechanism could increase the average profit efficiently without incurring significant quality of service (QoS) violations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1520-1542
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• 2020

Traditional software testing typically uses many physical resources to manually build various test environments, resulting in high resource costs and long test time due to limited resources, especially for small enterprises. Cloud computing can provide sufficient low-cost virtual resources to alleviate these problems through the virtualization of physical resources. However, the provision of various test environments and services for implementing software testing rapidly and conveniently based on cloud computing is challenging. This paper proposes a multilayer cloud testing model based on cloud computing and implements a hybrid cloud testing system based on virtual machines (VMs) and networks. This system realizes the automatic and rapid creation of test environments and the remote use of test tools and test services. We conduct experiments on this system and evaluate its applicability in terms of the VM provision time, VM performance and virtual network performance. The experimental results demonstrate that the performance of the VMs and virtual networks is satisfactory and that this system can improve the test efficiency and reduce test costs through rapid virtual resource provision and convenient test services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1164-1183
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• 2019

The rapid development of cloud computing and high requirements of operators requires strong support from the underlying Data Center Networks. Therefore, the effectiveness of using resources in the data center networks becomes a point of concern for operators and material for research. In this paper, we discuss the online virtual-cluster provision problem for multiple tenants with an aim to decide when and where the virtual cluster should be placed in a data center network. Our objective is maximizing the total revenue for the data center networks under the constraints. In order to solve this problem, this paper divides it into two parts: online multi-tenancy scheduling and virtual cluster placement. The first part aims to determine the scheduling orders for the multiple tenants, and the second part aims to determine the locations of virtual machines. We first approach the problem by using the variational inequality model and discuss the existence of the optimal solution. After that, we prove that provisioning virtual clusters for a multi-tenant data center network that maximizes revenue is NP-hard. Due to the complexity of this problem, an efficient heuristic algorithm OMS (Online Multi-tenancy Scheduling) is proposed to solve the online multi-tenancy scheduling problem. We further explore the virtual cluster placement problem based on the OMS and propose a novel algorithm during the virtual machine placement. We evaluate our algorithms through a series of simulations, and the simulations results demonstrate that OMS can significantly increase the efficiency and total revenue for the data centers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권7호
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• pp.3026-3049
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• 2016

Cloud computing promises high performance and cost-efficiency. However, most cloud infrastructures operate at a low utilization, which greatly adheres cost effectiveness. Previous works focus on seeking efficient virtual machine (VM) consolidation strategies to increase the utilization of virtual resources in production environment, but overlook the under-utilization of backup virtual resources. We propose a heuristic time sharing policy of backup VMs derived from the restless multi-armed bandit problem. The proposed policy achieves increasing backup virtual resources utilization and providing high availability. Both the results in simulation and prototype system experiments show that the traditional 1:1 backup provision can be extended to 1:M (M≫1) between the backup VMs and the service VMs, and the utilization of backup VMs can be enhanced significantly.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.126-133
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• 2023

Recent advancements in cloud service virtualization technologies have witnessed a shift from a Virtual Machine-centric approach to a container-centric paradigm, offering advantages such as faster deployment and enhanced portability. Container orchestration has emerged as a key technology for efficient management and scheduling of these containers. However, with the increasing complexity and diversity of heterogeneous workloads and service types, resource scheduling has become a challenging task. Various research endeavors are underway to address the challenges posed by diverse workloads and services. Yet, a systematic approach to container orchestration for effective cloud management has not been clearly defined. This paper proposes the DRA-Engine (Dynamic Resource Allocation Engine) for resource scheduling in container orchestration. The proposed engine comprises the Request Load Procedure, Required Resource Measurement Procedure, and Resource Provision Decision Procedure. Through these components, the DRA-Engine dynamically allocates resources according to the application's requirements, presenting a solution to the challenges of resource scheduling in container orchestration.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.677-684
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• 2009

다양한 센서 네트워크와 유비쿼터스 기술이 제공되는 지능형 협업 환경은 사용자를 위해 확장된 인터랙션을 제공할 수 있다. 기존의 인터랙션이 사용자와 컴퓨터 머신과의 직접적인 인터랙션이 주를 이룬 반면 새로 확장된 인터랙션은 사용자와 공간과의 인터랙션을, 실질적으로 공간을 구성하는, 관리와 제어가 가능한 구성요소와의 인터랙션을 나타낸다. 본 논문은 이러한 공간 인터랙션을 효과적으로 제공할 수 있도록 하기 위해서 공간 오브젝트를 등록, 인식하고, 특히 사용자의 의도에 맞는 태스크를 지원하기 위해 과거의 인터랙션 정보를 이용한 템플릿 기반 맵핑 알고리즘을 설계한다. 제안된 알고리즘을 이용하는 경우, 공간 오브젝트가 증가함에 따라 템플릿을 검색하여 처리하는데 드는 시스템의 비용이 어느 정도 향상되는지 실험을 통해 분석하도록 하며, 진행되는 모든 공간 인터랙션을 시각적으로 보여주기 위한 그래픽 기반의 도시 방법을 소개하고 결론을 맺는다.

• 방송공학회논문지
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• 제19권1호
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• pp.14-23
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• 2014

본 논문은 최근 이동통신 네트워크에서 폭발적으로 증가하고 있는 비디오 트래픽이 야기한 문제와 요구사항의 해결책으로써, SDN(Software Defined Network)을 기반으로 하는 다중 무선 접속 기술(Multiple Radio Access Technology)의 제어 기법을 제안하고 그 성능을 자체 구축한 테스트베드를 통하여 평가한다. 이를 위하여, 먼저 사업자 입장에서 3rd-party의 비디오 트래픽을 사업자망으로부터 우회(off-loading)시키는 방안의 필요성과, 사용자에게 저비용으로 고속의 대용량 비디오 콘텐츠 서비스를 제공하는 방안에 대하여 논의한다. 또한 성능평가를 위한 테스트베드는 OpenStack 클라우드 및 SDN 기반으로 구축 하였다. 이를 통해, OpenFlow와 Open Switch를 이용하여 2개의 2.4GHz 무선 랜 링크와 3개의 5GHz 무선 랜 링크가 동시에 하나의 서비스를 지원하도록 함으로서 820 Mbps 급의 초고속 클라우드 비디오 서비스를 위한 전송 속도를 실현하였다.