Earticle

Cloud computing is becoming an effective and efficient way of computing resources and computing service integration. Through centralized management of resources and services, cloud computing delivers hosted services over the internet, such that access to shared hardware, software, applications, information, and all resources is elastically provided to the consumer on-demand. The main enabling technology for cloud computing is virtualization. Virtualization software creates a temporarily simulated or extended version of computing and network resources. The objectives of virtualization are as follows: first, to fully utilize the shared resources by applying partitioning and time-sharing; second, to centralize resource management; third, to enhance cloud data center agility and provide the required scalability and elasticity for on-demand capabilities; fourth, to improve testing and running software diagnostics on different operating platforms; and fifth, to improve the portability of applications and workload migration capabilities. One of the key features of cloud computing is elasticity. It enables users to create and remove virtual computing resources dynamically according to the changing demand, but it is not easy to make a decision regarding the right amount of resources. Indeed, proper provisioning of the resources to applications is an important issue in IaaS cloud computing. Most web applications encounter large and fluctuating task requests. In predictable situations, the resources can be provisioned in advance through capacity planning techniques. But in case of unplanned and spike requests, it would be desirable to automatically scale the resources, called auto-scaling, which adjusts the resources allocated to applications based on its need at any given time. This would free the user from the burden of deciding how many resources are necessary each time. In this work, we propose an analytical and efficient VM-level scaling scheme by modeling each VM in a data center as an M/M/1 processor sharing queue. Our proposed VM-level scaling scheme is validated via a numerical experiment.

In the era of Cloud Computing, the Infrastructure-as-a-Service is one of the key issues to provide feasible applications to enhance customer’s services. Cloud Computing Service model aims to provide Everything as a Service (XaaS) over Internet. It is still necessary to integrate infrastructure to have rich resource management for the Cloud Computing requirements. Grid Computing has been a popular solution in providing infrastructure and computational resources as service. Grid computing Infrastructure, despite various progresses, still has an issue of providing dedicated customized and controllable remote execution environment. Virtualization has been provided as one solution for the issue. Virtualization in grid environment can be extended to the cloud computing context to provide the Grid Infrastructure as a Service to the cloud users. In this paper, we propose a new middleware architecture and methodology for providing virtual environment in Grids as a Service for the cloud users. This can be advantageous in Grid computing scenario also, where virtual environment provides coarse grained job execution thereby enhancing Quality of Service (QOS) compared to fine grained execution of jobs and also management of resources. Towards virtualization at the user end, the middleware acts as a bridge between the Grid resource layer and the users, thereby making the middleware light-weight. The Grid Control node chooses the resource node for service based on the resource information and thus the Grid Infrastructure as a service to the users.

지난 몇 년간 IT인프라는 다양한 형태로 변화해왔다. 메인프레임 환경에서 Unix, x86서버로의 전환과 그리드 컴퓨팅, 유틸리티 컴퓨팅에서 클라우드 컴퓨팅에 이르기까지 새로운 IT모델로의 진화 가 이루어져 왔다. 그러면서 IT는 하나의 서비스모델화가 되었고 공급자와 사용자간의 서비스 요구수 준 정량화 등을 통해 서비스 품질을 보장하기 위한 약정인 SLA(Service Level Agreement) 필요하 다. 특히 클라우드 환경에서는 이용자가 서비스 품질이나 보안 등에 불안할 수밖에 없는 여건에 따라 SLA의 중요성이 더욱 강조되고 있는 실정이다. 본 연구에서는 기존 환경에서의 SLA에 대한 측정 지 표의 중요성과 비교 분석을 통해 클라우드 서비스 환경에 따른 SLA작성 시 참조할 수 있는 자료로 활용할 수 있도록 클라우드 그룹과 비 클라우드 그룹 간 중요도 차이에 대한 연구를 수행했다. 그 결 과 클라우드 환경에서의 가용성 SLA 지표와 보안성 SLA지표의 중요도가 기존 IT인프라에 비해 더 높다는 결과를 도출하였다.

Past few years, IT infrastructure altered in various form. New IT model has been evolved from mainframe environment to grid and utility computing to cloud computing. In this process, IT became one service model, and it required stipulation for service quality assurance between supplier and consumer such as SLA(Service Level Agreement) standard service requirement. Importance of SLA is emphasized increasingly over the period because of user’s concern regarding service quality and security in cloud computing environment. Especially this research compared and analyzed importance of previous environment measurement index. Also this research evaluated relative importance between cloud group and non cloud group, therefore it could utilize as reference data when we tabulate SLA. As a result, significance of fusibility and security SLA index in cloud environment is higher than previous IT infrastructure.

클라우드컴퓨팅서비스는 4차산업혁명을 대표하는 혁신적인 서비스 중 하나로, 많은 기업이 클 라우드컴퓨팅서비스를 이용하고 있다. 우리나라는 클라우드컴퓨팅서비스를 안전하게 이용할 수 있는 환경을 조성하고 클라우드컴퓨팅 산업 발전을 위하여 2015년 「클라우드컴퓨팅 발전 및 이용자 보호 에 관한 법률」 을 제정하였다. 그러나 정부의 노력에도 불구하고 행정기관과 공공기관은 클라우드컴 퓨팅서비스의 보안에 대한 의문, G-클라우드 이용 정책, 예산 절감에 대한 의문, 구체적인 도입 절차 의 불비로 민간 클라우드 이용은 아직 저조하다. 이 논문에서는 현행 법제하에서 행정·공공기관이 민 간 클라우드컴퓨팅서비스를 이용하기 위한 절차를 제시하고, 그 과정에서 문제점과 개선 방안을 제시 하였다.

Many companies offers and uses cloud computing services. The government of Republic of Korea enacted "The Act on the Development of Cloud Computing and Protection of Users" in 2015 to use a cloud computing by creating an environment for safe use of cloud computing services, to develop of the national economy by promoting a cloud computing industry. In spite of government's efforts, the administrative and public institutions have a difficulty in using cloud computing services due to concern about Information Security, policy which is administrative institutions using a G-Cloud of National Information Resources Service, concern about saving costs, a lack of procedure. We have presented a procedure of administrative and public institutions using a cloud computing services under the current laws and regulations. Also we have presented problems and improvements in this article.

Infrastructure as a Service (IaaS) is one of the three important fundamental service models provided by cloud computing. It provides users with computing resource and storage resource in terms of virtual machine instances. Because of the rapid development of cloud computing, more and more application systems have been deployed on the IaaS cloud computing platforms. Therefore, once anomalies incur in the IaaS cloud computing platforms, all the application systems cannot work normally. In order to enhance the dependability of IaaS cloud computing platform, a virtual machine instance anomaly detection system is proposed for IaaS cloud computing platform to detect virtual machine instances that exhibit abnormal behaviors. The proposed virtual machine instance system consists of four modules that are the data collection, the data transmission, the data storage, and the anomaly detection. In order to reduce the computing complexity and improve the detection precision, the anomaly detection module introduces the principal components analysis to reprocess the collected data and then adopts the Bayesian decision theory to detect the abnormal data. Experimental results show that the proposed virtual machine instance anomaly detection system is effective.

In order to monitor the running status of IaaS cloud computing platforms, performance metric data are collected to perform anomaly detection for IaaS cloud computing platforms and determine whether the IaaS cloud computing platforms fail to run normally. However, it is challenging to effectively detect performance anomalies from a large amount of noisy and high dimensional performance metric data. In this paper, an efficient anomaly detection scheme is proposed for IaaS cloud computing platforms. The proposed scheme first designs a global locality preserving projection algorithm to perform feature extraction on performance metric data, and then introduces a local outlier factor algorithm to detect anomalies. A series of experiments are conducted on a private cloud computing platform. Experimental results show that our proposed global locality preserving projection algorithm outperforms the principal components analysis algorithm and the locality preserving projection algorithm and our proposed anomaly detection scheme is better than the state-of-the-art schemes for IaaS cloud computing platforms.

In this paper, a data transmission model is proposed to simulate the way of transmitting resource status information in IaaS cloud computing platforms. An adaptive periodic push strategy is also proposed to improve the performance of the data transmission model. The time interval in the adaptive periodic push strategy is dynamically updated by considering the change degree of resource status information. A series of experiments are conducted on a cloud computing testbed and experimental results show that the adaptive periodic push strategy performs better than previous data transmission strategies in terms of the number of data transmissions and data coherence.

Because the existing BIM platform is based on user services, the focus is on the development of SaaS (Software as a Service), which provides business services online. However, since a harbor is a security facility, the harbor BIM platform is preferably provided in a private form, rather than relying on the infrastructure environment provided by external cloud providers. Therefore, this study analyzes and reviews the main functions to be provided as SaaS services of the harbor BIM platform. The goal is to build a cloud-based harbor BIM platform that can provide this service to users. To this end, we built IaaS (Infrastructure as a Service) environment of the harbor BIM platform based on the open source Open Stack and integrate and develop PaaS environment with Open Shift applied with IaaS. We applied the GPU to the harbor BIM platform to verify the performance of the harbor BIM platform, and found that the rendering and loading times are improved. In particular, it is expected to reduce the cost of introduction and provide it as the basic cloud environment of similar BIM platform for infrastructure facilities.