• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.9-16
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• 2017

In this paper, we propose to present a model of cloud security that has emerged as the biggest topic of cloud computing, replacing the traditional IT environment. While cloud computing is an extension of existing IT technology, security issues and threats can be applied to traditional security technologies. However, the biggest difference between a typical computing environment and a cloud computing environment is a virtualized environment with a hypervisor. Currently, there are many weaknesses in the virtualized environment, and there are few related security products. In order for a cloud computing environment to function as a reliable IT environment, we expect more research on hypervisor-based security technologies, and we expect to secure safer cloud services through a secure model over the next generation of new-based networks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.757-760
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• 2012

Cloud storage system that cloud computing providers provides large amounts of data storage and processing of cloud computing is a key component. Large vendors (such as Facebook, YouTube, Google) in the mass sending of data through the network quickly and easily share photos, videos, documents, etc. from heterogeneous devices, such as tablets, smartphones, and the data that is stored in the cloud storage using was approached. At time, growth and development of the globally data, the cloud storage business model emerging is getting. Analysis new network storage cloud storage services concepts and technologies, including data manipulation, storage virtualization, data replication and duplication, security, cloud computing core.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.23-28
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• 2024

The multi-tenancy and high scalability of the cloud have inspired businesses and organizations across various sectors to adopt and deploy cloud computing. Cloud computing provides cost-effective, reliable, and convenient access to pooled resources, including storage, servers, and networking. Cloud service models, SaaS, PaaS, and IaaS, enable organizations, developers, and end users to access resources, develop and deploy applications, and provide access to pooled computing infrastructure. Despite the benefits, cloud service models are vulnerable to multiple security and privacy attacks and threats. The SaaS layer is on top of the PaaS, and the IaaS is the bottom layer of the model. The software is hosted by a platform offered as a service through an infrastructure provided by a cloud computing provider. The Hypertext Transfer Protocol (HTTP) delivers cloud-based apps through a web browser. The stateless nature of HTTP facilitates session hijacking and related attacks. The Open Web Applications Security Project identifies web apps' most critical security risks as SQL injections, cross-site scripting, sensitive data leakage, lack of functional access control, and broken authentication. The systematic literature review reveals that data security, application-level security, and authentication are the primary security threats in the SaaS model. The recommended solutions to enhance security in SaaS include Elliptic-curve cryptography and Identity-based encryption. Integration and security challenges in PaaS and IaaS can be effectively addressed using well-defined APIs, implementing Service Level Agreements (SLAs), and standard syntax for cloud provisioning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.2960-2976
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• 2018

The limited storage and computing capacity hinder the development of cognitive radio ad hoc networks (CRAHNs). To solve the problem, a new paradigm of cloud-based CRAHN has been proposed, in which a CRAHN will make use of the computation and storage resources of the cloud. This paper envisions an integrated CRAHN-cloud network architecture. In this architecture, some cognitive radio users (CUs) who satisfy the required metrics could perform as mobile gateway candidates to connect other ordinary CUs with the cloud. These mobile gateway candidates are dynamically clustered according to different related metrics. Cluster head and time-to-live value are determined in each cluster. In this paper, the gateway advertisement and discovery issues are first addressed to propose a hybrid gateway discovery mechanism. After that, a QoS-based gateway selection algorithm is proposed for each CU to select the optimal gateway. Simulations are carried out to evaluate the performance of the overall scheme, which incorporates the proposed clustering and gateway selection algorithms. The results show that the proposed scheme can achieve about 11% higher average throughput, 10% lower end-to-end delay, and 8% lower packet drop fractions compared with the existing scheme.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.507-510
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• 2013

Cloud computing is an emerging technology, which allows the user to fulfill his needs by outsourcing the resources. With the passage of time, cloud computing has become an essential part of our lives. But it still requires some sort of standardization, specially in terms of user's trust, privacy, and security related things. This study presents different types of cloud computing services and their working domains along with some key virtualization related issues that are encountered by the cloud service provider as well as the user. Those key issues, related with virtual network are discussed in this paper. This study provides a basis to work further on those issues, so that the key concerns are addressed as soon as possible and cloud computing could become standardized and more prevalent.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.158-170
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• 2021

Virtualization technologies are being adopted and broadly utilized in many fields and at different levels. In cloud computing, achieving load balancing across large distributed virtual machines is considered a complex optimization problem with an essential importance in cloud computing systems and data centers as the overloading or underloading of tasks on VMs may cause multiple issues in the cloud system like longer execution time, machine failure, high power consumption, etc. Therefore, load balancing mechanism is an important aspect in cloud computing that assist in overcoming different performance issues. In this research, we propose a new approach that combines the advantages of different task allocation algorithms like Round robin algorithm, and Random allocation with different threshold techniques like the VM utilization and the number of allocation counts using least connection mechanism. We performed extensive simulations and experiments that augment different scheduling policies to overcome the resource utilization problem without compromising other performance measures like makespan and execution time of the tasks. The proposed system provided better results compared to the original round robin as it takes into consideration the dynamic state of the system.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.59-66
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• 2024

This paper presents the concept and characteristics of cloud computing, and it addresses how cloud computing delivers quality of service (QoS) to the end-user. Next, it discusses how to schedule one's workload in the infrastructure using technologies that have recently emerged such as Machine Learning (ML). That is followed by an overview of how ML can be used for resource management. This paper then looks at the primary goal of this project, which is to outline the benefits of using ML to schedule upcoming demands to achieve QoS and conserve energy. In this survey, we reviewed the research related to ML methods for predicting workloads in cloud computing. It also provides information on the approaches to elasticity, while another section discusses the methods of prediction used in previous studies and those that used in this field. The paper concludes with a summary of the literature on predicting workloads and optimising QoS in the cloud computing.

• Journal of Convergence Society for SMB
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• v.5 no.1
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• pp.31-35
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• 2015

Cloud computing is internet-based computing technology. This is a form for exchanging service focused on the Internet. Because Cost is saved and use is easy there's a tendency that many companies are using. Cloud is in the form of a public cloud and private cloud and hybrid cloud. The service model is SaaS, PaaS, IaaS. Cloud computing use is simple but it has a security vulnerability. In particular, there is a vulnerability in virtualization and centralized information. In order to overcome this new security technology is to be developed. In particular, network security technology and authentication technology should be developed. Another way to overcome security responsibilities must be clearly and policies should be unified.

• Electronics and Telecommunications Trends
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• v.30 no.1
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• pp.133-143
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• 2015

최근 기술발전으로 컴퓨터 수준의 스마트 디바이스를 이용한 무선 인터넷 서비스 이용이 확대되고 있어 이에 따른 트래픽 증가를 효율적으로 수용하기 위한 새로운 이동통신시스템과 네트워크 구조 연구가 활발하게 진행되고 있다. 이러한 과정에 유선 IT 분야에서 선행적으로 진행된 SDN(Software Defined Network) 및 cloud & virtualization 기술들을 이동통신의 액세스 또는 서비스 플랫폼 환경에 적용하는 선도적인 연구개발이 진행되고 있어 SDN 및 cloud & virtualization 분야에 대한 최근 동향을 파악하고 이러한 기술들이 이동통신분야에 적용되는 다양한 사례들을 분석하여 새로운 형태의 네트워크 및 시스템 구조와 방식에 대한 연구개발 방향을 제시한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1482-1484
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• 2019

We present a method for transmitting 3D object information in real time in a telepresence system. Three-dimensional object information consists of a large amount of point cloud data, which requires high performance computing power and ultra-wideband network transmission environment to process and transmit such a large amount of data in real time. In this paper, multiple users can transmit object motion and facial expression information in real time even in small network bands by using GANs (Generative Adversarial Networks), a non-supervised learning machine learning algorithm, for real-time transmission of 3D point cloud data. In particular, we propose the creation of an object similar to the original using only the feature information of 3D objects using conditional GANs.