Earticle

Recently, artificial intelligence techniques have been widely used in the computer science field, such as the Internet of Things, big data, cloud computing, and mobile computing. In particular, resource management is of utmost importance for maintaining the quality of services, service-level agreements, and the availability of the system. In this paper, we review and analyze various ways to meet the requirements of cloud resource management based on artificial intelligence. We divide cloud resource management techniques based on artificial intelligence into three categories: fog computing systems, edge-cloud systems, and intelligent cloud computing systems. The aim of the paper is to propose an intelligent resource management scheme that manages mobile resources by monitoring devices' statuses and predicting their future stability based on one of the artificial intelligence techniques. We explore how our proposed resource management scheme can be extended to various cloud-based systems.

Cloud computing services are becoming more popular for various reasons which include ‘having no need for capital expenditure’ and ‘the ability to quickly meet business demands’. However, what seems to be an attractive option may become a substantial expenditure as more projects are moved into the cloud. Cloud service companies provide different pricing options to their customers that can potentially lower the customers’ spending on the cloud. Choosing the right combination of pricing options can be formulated as a linear mixed integer programming problem, which can be solved using optimization.

최근의 클라우드서비스 수요가 AWS, Azure, Google Cloud 등 복수 클라우드 플랫폼을 병행 운용하면서, 클라우드 설정 오류와 API 관리, 권한 및 인증 장기 방치, 데이터 주권·규제(GDPR 등) 문제가 보안의 핵심 이슈로 부상하였다. 공격 표면의 확대와 함께 DDoS, API 침해, 공급망 공격 등 새로운 형태의 위협이 빠르게 증가하며, AI 기반 자동화 공격도 현실화되고 있 다. 이에 대한 대응 전략으로는 정책 자동화, 강화된 인증, API Gateway 및 WAF, 데이터 암호화, 그리고 CSPM, SIEM, SOAR, DevSecOps·Zero Trust 아키텍처 도입이 중요하다. 그리고 중앙집중 또는 메쉬 기반 통합 관리로 멀티클라우드 자산 과 트래픽을 단일 플랫폼에서 관리하고, AI·ML 기반 실시간 이상 행위 탐지와 자동화된 정책 시행, 거버넌스 강화가 효과적 이다. 따라서 본 연구에서는 클라우드 컴퓨팅의 확산에 따른 보안 위협을 분석하고, 클라우드 가시성 및 위협대응 통합, 위험 순위 산출 및 보안확장 그리고 클라우드 워크로드 보호를 위해 NIST·CSA·MITRE ATT&CK프레임워크 등 글로벌 표준에 가반한 AI 기반 보안아키텍처와 실시간 탐지 및 자동화된 대응 보안시스템에 대한 연구를 진행하였다.

The recent demand for cloud services involves the parallel operation of multiple platforms such as AWS, Azure, and Google Cloud, and this has elevated key security issues including cloud configuration errors, API management, prolonged neglect of entitlements and access controls, and complex regulatory requirements like GDPR. Together with an expanding attack surface, new forms of threats such as DDoS, API breaches, and supply chain attacks are rapidly increasing, and AI-driven automated attacks are materializing. As a response, major strategies consist of policy automation, strengthened authentication, API gateways and WAFs, encryption, as well as the adoption of CSPM, SIEM, SOAR tools, DevSecOps, and Zero Trust architectures. Centralized or mesh-based integrated management allows single-platform governance and traffic controls across multi-cloud assets, with real-time anomaly detection by AI/ML and automated policy enforcement enhancing effectiveness. Accordingly, this study analyzes security threats emerging from cloud expansion, and explores integrated visibility, threat response, risk ranking, security extension, and cloud workload protection through research on AI-based security architectures and real-time automated detection/response systems built on global standards like NIST, CSA, and MITRE ATT&CK.

Cloud computing integrates a large number of virtual resources and provides flexible service to users by pay-as-you-need. Compared with the traditional software systems, the fault detection is more complex and difficult in cloud computing systems. The fault model is helpful for people to find and distinguish the software fault. However, the current researches mostly focus on software fault model under the traditional environment. Based on the research of software fault model, this paper proposed the fault models in cloud computing systems combining with the characteristics of cloud computing. Then an improved C4.5 decision algorithm is used to distinguish the cloud fault. The experiment results showed that the fault model and improved algorithm is effective.

The most challenging field of concern these days is energy conservation in various applications. Energy crisis led a way to green computing and green computing needs an efficient redesign of algorithms and mechanisms to meet the desired energy efficiency. Green IT is a study and practice which can reduce energy consumption significantly. In this paper various efficient energy saving Green IT methods are discussed. As cloud computing has become a fast emerging technology this days, some energy saving strategy for cloud computing has also been discussed in the paper. Most of the IT sector energy consumption happens in data centers. Virtual Machine migration an emerging energy aware technology used in data centers is also discussed in the paper. Finally some power conserving matrix parameters are given importance.

As more and more Cloud systems are designed and deployed in business and science engineering areas, how to evaluate and monitor the system’s runtime performance is becoming an important issue. However, few efforts are taken to address such an issue because the resources in Cloud systems often owned by different institutes. In this paper, we present an integrated performance evaluation framework, which is aiming to provide cloud users an easy-to-use toolkit to evaluate their system’s runtime performance, or compare the performance under different resource management policies. The design and implementation of our framework is highly extensible and re-useable in most existing cloud systems. Currently, the prototype of our implementation is examined by a series of experiments, and the results indicate that its configurable feature is very useful when users are conducting performance comparing under different contexts.

Nowadays cloud computing has turned into a key innovation and has become a great solution for indulging a flexible utility oriented , online allocation and storage of computing resources and client’s information in lower expense, on- interest and dynamically scalable framework on pay per use premise. This technology is a new pattern emerging in IT environment with immense necessities of framework and resources. Job Scheduling Problem is an essential issue. For efficient usage and managing resources, administrations, scheduling plays a critical role. This paper apportion the performance enhancement of Hyper- Heuristic Scheduling Approach to schedule cloudlets and resources, by taking account of both , computation time and transmission cost with two detection operators. Load Balancing and RASA concept is applied for efficient Load Scheduling, resource utilization and thereby enhancing the overall performance of cloud computing environment. The numerical investigations of HHSA were performed on CloudSim. Experimental results generated via simulation shows that enhanced heuristic scheduling approach is much better than individual heuristic approach in terms of minimizing makespan time.

Cloud computing can improve utilization of powerful computer systems. However, security problems become an important challenge in the cloud systems. Implementation of antivirus software can cost a lot of resource. This paper advanced an access-control based architecture of operation systems which ensure the high availability of the whole system. System resource cost on security can be dropped by the implementation of this architecture.

With the fast development of cloud computing and computer science technology, the combination of the IOT and clod computing in the medical-assisted environment is urgently needed. The prior research focus more on individual development of the single technique, quite a less research on the field of medical monitoring and managing service application have been conducted. Therefore, in this paper, we study and analyze the application of cloud computing and the Internet of Things on the field of medical environment. We are trying to make the combination of the two kinds of technology monitoring and management information system in hospital. Remote monitoring cloud platform architecture model (RMCPHI) set up medical information in the first place. Then the RMCPHI architecture was analyzed. Eventually, the last effective PSOSAA algorithm proposed the hospital medical information service cloud system monitoring and management application. Experimental simulation illustrates that the proposed algorithm outperforms the other state-of-the-art algorithms. Further potential research areas are discussed.

Many security professionals know about Image Metadata, but they do not really know how it can be used to protect from the threats and prove the authenticity of these images and the most significant aspect is the anonymity of perpetrator. The novelty of this paper lies on how to use a new algorithm of Image Metadata to detect internal intrusion and how to protect it in real time inside the cloud providers. In this paper, we propose a novel algorithm to use features and characteristics technologies of Image Metadata, namely, a novel family of Intrusion Detection Systems (IDS) built on Exchangeable Image File Format (EXIF) of Image Metadata. It can assist in eliminating the anonymity advantage of perpetrator which is considered extremely important for the Digital Investigation to detect the attacker in a real time and to prove the authenticity of the images Metadata against attackers.

Presently the vendors of ERP software are facing threats as well as opportunities by new technological challenges. In particular, business intelligence and cloud computing are, besides others, important trends in the ERP market. It is crucial for the ERP vendors to gain strong positions in both fields to remain competitive. Since the software market is often influenced by network effect we analyzed four big ERP vendors, namely Microsoft, Oracle, Sage, and SAP with respect to these effects on business intelligence and cloud computing. We enriched our analysis by IBM which is not an ERP vendor but is a leading IT company in both fields: business intelligence as well as cloud computing. The four ERP vendors have notably different histories. While SAP started as an ERP vendor, Oracl began as a vendor of data base systems. Microsoft’s most important products are still the operating system Windows and its office suite. Sage, like SAP, is an ERP vendor but primarily addresses SME while SAP’s main strength is still in the field of EPR systems for large co-operations.

전세계 수많은 사용자들이 특정 실시간 가상 환경을 공유하는 MMOG(Massive Multi-player On-line Game) 를 지원하고자 본 논문에서 P2P(Peer-to-Peer)와 클라우드 컴퓨팅의 기술적 장점을 결합하는 기법을 제시한다. 본 논 문에서 제시된 클라우드 컴퓨팅 기반의 P2P 시스템은 사용자 수가 급증할 경우에도 사용자 자원을 기반 구조에 추가함 으로써 준수한 수준의 확장성을 제공할 수 있다. 또한 이 시스템은 사용자들의 처리 능력을 활용함으로써 클라우드에 있는 서버의 부하 즉 상당한 컴퓨팅 능력과 통신량을 절감할 수 있다. 본 논문에서 MMOG의 확장성을 위한 클라우드 컴퓨팅 기반의 P2P 시스템의 개념과 기본적 구조가 기술된다. 대규모 사용자 집단에 대한 경제적 비용과 서비스 품질의 규모를 고려하는 이 구조가 실현되기 위해서는 효율적이고 효과적인 자원의 공급과 부하의 배분이 반드시 필요하다. 시 뮬레이션 결과 제시된 P2P 시스템은 동시에 실행되는 사용자들의 수가 증가할 때 클라우드와 사용자 제공 자원의 양을 제어함으로써 사용자들의 충분한 대역폭을 활용하는 반면 서버의 대역폭을 감소시킬 수 있는 것으로 나타났다.

In this paper, we propose an approach that combines the technological advantages of P2P and cloud computing to support MMOGs that allowing a huge amount of users worldwide to share a real-time virtual environment. The proposed P2P system based on cloud computing can provide a greater level of scalability because their more resources are added to the infrastructure even when the amount of users grows rapidly. This system also relieves a lot of computational power and network traffic, the load on the servers in the cloud by exploiting the capacity of the peers. In this paper, we describe the concept and basic architecture of cloud computing-based P2P Systems for scalability of MMOGs. An efficient and effective provisioning of resources and mapping of load are mandatory to realize this architecture that scales in economical cost and quality of service to large communities of users. Simulation results show that by controlling the amount of cloud and user-provided resource, the proposed P2P system can reduce the bandwidth at the server while utilizing their enough bandwidth when the number of simultaneous users keeps growing.

2006년 법령이 개정된 후 공공기관에 전자기록관리시스템이 도입되어 대부분의 기관이 기록관에 디지털 저장소를 구축했다. 이 시스템은 도입과 유지에 많은 비용과 인력이 소요되고, 저장소의 확장성이 떨어지며, 상호운용성의 확보가 어려운 단점이 있다. 이에 본 연구에서는 클라우드 컴퓨팅 기술을 전자기록관리시스템에 도입하여 기존 시스템이 지닌 문제를 개선해 더 나은 시스템을 구축할 모델을 제시하였다. 특히 클라우드 컴퓨팅의 장점인 저비용․고효율, 빠른 확장성, 다양한 시스템을 하나로 포용할 수 있는 상호운용성이 공공기관의 전자기록관리시스템 운용에 적용될 때 얻게 될 기대효과를 제시해 향후 클라우드 기술의 도입의 당위성을 제기한다.

After making amendments to archive-related legislations in 2006, the Electronic Records Management Systems (ERMS) were introduced into public institutions. With this, most institutions constructed digital repositories in their records center. In this system, there are several weaknesses that wasted costs and manpower for its introduction and maintenance. The extensibility of a repository is debased, and securing the interoperability is difficult. The study proposed a cloud computing-based model to solve such problems that the previous system had. In particular, this study expected effects through the proposed model. The expected effects are low-cost, highly efficient, extensible, and interoperable in embracing various systems. Thus, the appropriateness of introducing cloud computing into ERMS was analyzed in this study.

최근 IT산업의 화두인 클라우드 컴퓨팅에서, HDFS는 널리 사용되고 있는 분산 파일 시스템이다. HDFS는 분산된 데이터의 저장과 검색의 장점이 있는 반면, 대용량 파일처리를 목적으로 설계되었기 때문에 실시간 파일처리와 저용량 데이터 처리에 비효율적이다. 본 논문에서는 이러한 문제를 해결하기 위해 HDFS의 파일 처리 과정을 개선하여 저용량 파일 처리를 향상시키는 방법을 제안한다. 본 기법은 데이터 블록에 저용량 파일들을 병합함으로써 데이터 처리의 효율성을 높이는 결과를 보였다.

최근 IT산업의 화두인 클라우드 컴퓨팅에서, HDFS는 널리 사용되고 있는 분산 파일 시스템이다. HDFS는 분산된 데이터의 저장과 검색의 장점이 있는 반면, 대용량 파일처리를 목적으로 설계되었기 때문에 실시간 파일처리와 저용량 데이터 처리에 비효율적이다. 본 논문에서는 이러한 문제를 해결하기 위해 HDFS의 파일 처리 과정을 개선하여 저용량 파일 처리를 향상시키는 방법을 제안한다. 본 기법은 데이터 블록에 저용량 파일들을 병합함으로써 데이터 처리의 효율성을 높이는 결과를 보였다.