Earticle

Intrusion detection acts as a vital function in providing information security, and additionally the key technology is to precisely classify diverse attacks. Intrusion detection system (IDS) is identified as an important security issue within the cloud network environment. In this paper, IDS is given based on an innovative optimized custom RC-NN (Recurrent Convolutional Neural Network) which is proposed for intrusion detection along with the Ant Lion optimization algorithm. By this method, CNN (Convolutional Neural Network) is made hybrid with LSTM (Long Short Term Memory). Thus, all the attacks identified with the network layer of cloud are classified efficiently. The experimental results shown below describe the presentation of the IDS classification model with high accuracy, thus improving the detection rate or error rate. The optimized custom RC-NN-IDS model thus achieved an improved classification accuracy of 94% and also a decreased error rate of 0.0012. Additionally true positive rate, true negative rate and precision are considered as performance metrics. The proposed approach is evaluated using the DARPA IDS evaluation Data Sets and CSE-CIC-IDS2018 dataset and is compared with some existing approaches.

One of the latest emerging technologies is artificial intelligence, which makes the machine mimic human behaviour. The most important component used to detect cyber attacks or malicious activities is the intrusion detection system (IDS). Artificial intelligence plays a vital role in detecting intrusions and widely considered as the better way in adapting and building IDS. In modern days, neural network algorithms are emerging as a new artificial intelligence technique that can be applied to real-time problems. The proposed system is to detect a classification of botnet attack which poses a serious threat to financial sectors and banking services. The proposed system is created by applying artificial intelligence on a realistic cyber defence dataset (CSE-CIC-IDS2018), the latest IDS Dataset in 2018 by Canadian Institute for Cybersecurity (CIC) on AWS (Amazon Web Services). The proposed system of Artificial Neural Networks provides an outstanding performance of Accuracy score is 99.97% and an average area under ROC(Receiver Operator Characteristic) curve is 0.999 and an average False Positive rate is a mere value of 0.03. The proposed system of Artificial Intelligence-based Intrusion detection of botnet attack classification is powerful, more accurate and precise. The novel proposed system can be applied to conventional network traffic analysis, cyber-physical system traffic analysis and also can be applied to the real-time network traffic data analysis.

Our paramount task is to examine and detect network attacks, is one of the daunting tasks because the variety of attacks are day by day existing in colossal number. The program proposed detects botnet attacks using the newest CSE-CIC-IDS2018 cyber dataset published by the Canadian Cybersecurity Establishment (CIC). The cyber dataset can be accessed on AWS (Amazon Web Services). The realistic network dataset consists of all the modern and existing attacks such as Brute-force attacks and password cracking, Heartbleed, Botnet, DoS (Denial of Service), DDoS also known as Distributed Denial of Service, Web attacks i.e. vulnerable web app attacks, and infiltration of the network from inside. The objective of the proposed research is to identify a classification of Botnet attacks. Botnet attack is a Trojan Horse malware attack that poses a serious security threat to the banking and financial sectors. Since a specific classifier could possibly work for such datasets it is crucial to finish a comparative examination of classifiers in order to achieve the most noteworthy execution in such basic detection of network attacks. The proposed framework is to incorporate different classifier methods such as KNearset Neighbor classifier, Naive Bayes, Adaboost with Decision Tree, Support Vector Machine classifier, Random Forest classifier, and Artificial Intelligence to distinguish a portrayal of botnet attacks on the recent and realistic cyber dataset CSE-CIC-IDS2018. The results of the classification are given as precise precision for the specific classifiers. And furthermore, the proposed framework uses the Calibration curve as a standard approach in analytical methods which generates reliability diagrams to check the predicted probabilities of various classifiers are well-calibrated or not. Finally, the displayed graph proves how well the artificial intelligence technique outperforms all other classifiers which generates reliability diagrams to check the predicted probabilities of various classifiers are well-calibrated or not.

소셜 네트워크는 이용자들의 공통적 관심 분야를 기반으로 서로 연결된 개별적 온라인 서비스 구조이다. 소셜 네트워킹 웹사이트의 사용은 온라인 커뮤니케이션과 데이터 공유 및 상호작용 방법을 혁신적으로 바꿔놓았다. 소셜 네트워킹 사이트의 사용량이 증가함에 따라 막대한 양의 사용자 상호작용 데이터가 매일 축적되고 있어 다양한 분야에서의 적용을 위한 대량의 소셜 네트워크 데이터 마이닝 및 분석 방안이 갈수록 주목받고 있다. OLAP데이터 분석은 효과적으로 소셜 데이터를 분석하기 위하여 사용될 수 있는 유용한 데이터 분석방법이지만 계속적으로 가변하는 대량 데이터와 OLAP쿼리의 복잡성 때문에 작업 시간이 오래걸리는 문제점이 있다. 본 논문은 대량의 소셜 네트워크 데이터 분석에 적합한 클라우드 컴퓨팅의 발전이 이루어지면서 효율적이고 빠른 데이터 분석을 위하여 클라우드 컴퓨팅 플랫폼을 사용하는 OLAP기반 소셜 데이터 분석도구에 대해 설명한다.

A social network is an online structure of individuals which are related to each other based on some common relationship of interest. The usage of social networking websites has literally revolutionized the methods of online communication, data sharing and interaction. As a result of the increased usage of social networking sites a massive amount of user interaction data is been generated daily. The mining and analysis of this large amount of social network data for a variety of applications is getting popularity. OLAP analysis is a famous data analysis method which can be used to effectively analyze social data. However, because of the huge amount of continuously changing data and the complexity of OLAP queries, this data analysis in a time consuming task. The advances in cloud computing motivated us to use the cost effective cloud computing infrastructure for the task of analyzing large amount of social network data. This paper presents an OLAP based social data analysis tool which uses cloud computing platform for efficient and timely analysis of data. The usage of cloud computing infrastructure results in reduction of computational cost, device and location independence, and an increase in peak-load capacity.

국방부는 지난 2010년, ‘2012 정보화사업계획’의 일환으로 2014년까지 클라우드 컴퓨팅 기술이 포함된 국방통합정보관리소(메가센터)를 구축하기로 결정하였고, 현재 추진 중에 있다. 클라우드 컴퓨팅 환경은 데이터 센터에 많은 IT 자원이 집약된 형태로 효율적인 인프라구조를 위한 기술과 정책을 적용하여 시스템을 설계해야 한다. 즉, 사용자의 요구를 만족시키기 위해 클라우드 서비스를 효율적으로 제공하여야 하며, 시스템 구축 시 불필요하게 낭비되는 자원이 없게 설계되어야 한다. 그러나 최적 시스템을 구축하기 위해서는 시스템 설계 최초단계에 서비스 성능과 자원 사용의 가용성을 예측할 수 있어야 한다. 본 논문에서는 국방 클라우드 컴퓨팅 시스템의 서비스 가용성 예측을 위해 CloudAnalyst 시뮬레이터를 이용하여 현재 전 세계에서 가장 많은 사용자를 보유한 소셜 네트워크 서비스 중 하나인 Facebook을 클라우드 모델링 및 분석하였다. 시뮬레이션 결과를 통해 최적의 국방 클라우드 컴퓨팅 설계 모델을 제안한다.

In 2010, the Ministry of National Defense decided to build a MegaCenter including the cloud computing technology by 2014, as part of the '2012 Information Service Plan', which is now underway. The Cloud computing system environment should be designed applying cloud computing technology and policy for an efficient infrastructure that many IT resources are available in the data center as a concentrated form. That is, the system should be designed in such a way that clouding services will be efficiently provided to meet the needs of users and there will not be unnecessary waste of resources. However, in order to build an optimal system, it should be possible to predict the service performance and the resource availability at the initial phase of system design. In this paper, using the CloudAnalyst simulator to predict availability of the K-defence cloud computing system service, conducts cloud modeling and analysis of the 'Facebook', one of the most famous social network service applications with most users in the world. An Optimal K-Defense cloud computing design model is proposed through simulation results.

본 연구는 클라우드에 기반한 ERP 투자 결정에 있어서 네트워크 효과가 어떠한 영향을 미치는지에 초점을 맞추어 분석하였다. 구체적으로 국내 82개 중소기업에서 수집된 설문데이터를 사용하여 정보기술 담당자들이 클라우드에 기반한 ERP 투자 의사결정과 관련된 위험을 관리하기 위한 수단으로써 실물옵션을 채택하고자 하는 의도가 있는지, 그리고 동시에 실물옵션 채택 의도에 네트워크 효과가 어떻게 영향을 미치는지를 실증분석하였다. 선행연구를 바탕으로 연구모형과 4개의 가설을 설정하였다. 실증분석 결과, 클라우드 ERP 관련 기술적 위험은 연기 옵션의 채택에 양의 영향을 주고 도입 기업의 공급자와의 관계적 위험 및 클라우드 ERP 투자 관련 경제적 위험은 연기, 축소 및 포기 옵션의 채택에 모두 양의 영향을 주는 것으로 분석되었다. 반면 보안 및 정보유출에 관련된 안전성 위험은 어떠한 옵션의 채택에도 유의적인 영향을 미치지 않았다. 클라우드 ERP 도입 기업이 많을수록 증가되는 네트워크 효과의 경우 연기, 축소 및 포기 옵션 채택에 유의적인 양의 영향을 미치는 것으로 분석되었다. 본 연구는 국내 중소기업의 클라우드 ERP 투자와 관련하여 정보기술 담당자들은 기술, 관계, 경제적 위험이 있을 경우 그리고 네트워크 효과가 기대될 때 실물옵션을 채택할 의도가 존재한다는 것을 실증적으로 보여주었다.

We propose network effects upon the investment decision of cloud-based ERP. Using the survey data collected from 82 companies in 2015, we examine whether IT managers have an intention to adopt real options in order to manage the risk of cloud-based ERP investments and how the network effects influence upon the intention to adopt real options. Based on prior literature, we propose a research model with 4 hypotheses. We find partial support of the hypotheses from the empirical analysis: technological risks has a positive impact upon the adoption of real options such as defer, contract, and abandon. In contrast, we find no significant impact of security risks upon real options. We validate positive network effects upon the adoption of real options such as defer, contract, and abandon. This work empirically find that IT managers in Korean middle and small sized firms have an intention to adopt real options when the managers realize economic, technological, and relationship risks and when they expect network effects.

클라우드 컴퓨팅 환경에서 서버나 애플리케이션들이 수 분 사이에 구축되고 장애 발생 시 복구 또한 쉬워졌다. 특히, 잠시 서비스하기 위해서 물리적인 서버를 구축하는 것 보다 클라우드에서 가상 서버를 이용하면 편리함과 동시에 비용도 저렴하다. 하지만 그러한 서버나 애플리케이션들의 기반이 되는 네트워크나 보안시스템은 대부분 하드웨어 기반으로 구성되어 있어 클라우드 가상화 적용에 어려움이 많다. 클라우드 내에서도 네트워크나 보안설비 등에 대한 가상화를 통한 보호가 필요하게 되었다. 본 논문은 네트워크 가상화 기술을 활용하여 클라우드 네트워크의 보안을 강화하는 방법에 대한 연구를 다루고 있 다. 가상 서버 및 가상 네트워크를 생성해 다양한 보안 이점을 제공하는 가상화 기술을 활용해 링크 가상화와 라우터 가 상화를 적용하여 보안이 강화된 네트워크를 구성하였다. 구성된 네트워크에 가상 방화벽 기능을 적용해 네트워크를 격리 할 수 있었으며, 이 결과를 토대로 가상화 환경에서 보안 취약점을 극복하고 안전한 네트워크 구성을 위한 관리 전략을 제안하는데 기여할 것으로 기대된다.

In the cloud computing environment, servers and applications can be set up within minutes, and recovery in case of fail ures has also become easier. Particularly, using virtual servers in the cloud is not only convenient but also cost-effective compared to the traditional approach of setting up physical servers just for temporary services. However, most of the und erlying networks and security systems that serve as the foundation for such servers and applications are primarily hardwa re-based, posing challenges when it comes to implementing cloud virtualization. Even within the cloud, there is a growing need for virtualization-based security and protection measures for elements like networks and security infrastructure. This paper discusses research on enhancing the security of cloud networks using network virtualization technology. I configured a secure network by leveraging virtualization technology, creating virtual servers and networks to provide various security benefits. Link virtualization and router virtualization were implemented to enhance security, utilizing the capabilities of virt ualization technology. The application of virtual firewall functionality to the configured network allowed for the isolation of the network. It is expected that based on these results, there will be a contribution towards overcoming security vulnerabil ities in the virtualized environment and proposing a management strategy for establishing a secure network.

네트워크 기술이 발전함에 따라 보안을 위한 암호화나 VPN(Virtual Private LAN)등의 기술 이 일반적인 네트워크 환경과 클라우드 환경에서 사용 사례가 점차 증가하고 있다. 네트워크는 일반 적으로 1500Byte 패킷 단위로 통신하기 때문에 VPN과 같은 서비스를 제공하는 기술은 네트워크 패 킷의 헤더가 추가됨으로써 패킷 헤더의 오버헤드 증가와 Fragmentation 발생으로 인한 네트워크 전 송효율이 감소하게 된다. 본 논문에서는 추가되는 헤더를 압축하여 네트워크 전송효율을 증가시키기 위해 표준 프로토콜로 제안되어있는 ROHC를 기반으로 ROHC Engine을 설계하고, 범용 OS인 리 눅스에서 구현 후 헤더 압축 과정의 기능 검정 방법을 제시한다.

As network evolves, network techniques such as network security or VPN (Virtual Private LAN) have been used increasingly in cloud computing as well as general network environment. Because the default Ethernet MTU is 1500 bytes, adding a packet header in VPN increases communication overhead and fragmentation, reducing the network transmission efficiency. This paper proposes a ROHC Engine for increasing the network transmission efficiency by compressing the additional headers. The ROHC Engine is designed based on the ROHC standard protocol and implemented in Linux for its function verification. This paper presents how detailed functions of the proposed ROHC engine are verified in Linux.

최근 사물인터넷 기기들이 다양한 분야에 적용되어 우리의 일상생활 속까지 다가오며 보편화되고 있다. 우리의 일상에서의 모든 사물들이 네트워크로 연결되어 서로 상호작용을 하며 우리들에게 좀 더 유용한 효과를 주곤 한다. 본 논문 에서는 사용자 인증 한번 만으로 인증장치와 연결된 네트워크 환경에서 LED조명이 그 상호 연결되어 인증한 사람의 장소 까지 조명이 효율적으로 켜질 수 있는 알고리즘을 구현하였다. 한 건물 내에서 사용자 인증 한번을 통해 사용자 인증을 통해 인증된 정보를 이용하여 데이터베이스에 저장된 그 사람의 연구실위치를 탐색하여 최적의 알고리즘을 이용하여 그 사람이 일하는 연구실까지 조명이 켜지는 LED 에너지 효율화 알고리즘을 제안한다. 이를 통해 사람이 자주 다니지 않아 불필요한 에너지가 발생하는데 이를 효율적으로 이용하기 본 방법을 제안한다.

Recently, Internet of things have been applied to various fields and are becoming common in our everyday life. Everything in our daily lives is networked, interacting with each other and giving us more useful effects. In this paper, we implemented an algorithm that can illuminate the LED lighting to the authorized person 's place in the network environment connected with the authentication device only once with the user authentication. User authentication in one building User authenticated through a single user search for the location of the user's lab stored in the database using the certified information and LED lights up to the lab where the user works. Through this, unnecessary energy is generated because people do not pass frequently.

네트워크를 통한 정보의 공유는 오늘날 클라우드 서비스 환경으로 발전하여 그 이용자수를 빠르게 증가시키고 있지 만 네트워크를 기반으로 하는 불법적인 공격자들의 주요 표적이 되고 있다. 아울러 공격자들의 다양한 공격 기법 중 IP 스푸핑은 그 공격 특성상 일반적으로 자원고갈 공격을 수반하기 때문에 이에 대한 빠른 탐지와 대응 기법이 요구 된다. IP 스푸핑 공격에 대한 기존의 탐지 방식은 연결 요청을 시도한 클라이언트의 트레이스 백 정보 분석과 그 일치 여부에 따라 최종적인 인증과정을 수행 한다. 그렇지만 트레이스 백 정보의 단순 비교 방식은 서비스 투명성을 요구하는 환경 에서 빈번한 False Positive로 인하여 과도한 OTP 발생을 요구할 수 있다. 본 논문에서는 이러한 문제를 개선하기 위해 트레이스 백 정보 기반의 대칭키 암호화 기법을 적용하여 상호 인증 정보로 사용하고 있다. 즉, 트레이스 백 기반의 암 호화 키를 생성한 후 정상적인 복호화 과정의 수행 여부로 상호 인증이 가능하도록 하였다. 아울러 이러한 과정을 통하 여 False Positive에 의한 오버헤드도 개선할 수 있었다.

Network-based sharing of information has evolved into a cloud service environment today, increasing its number of users rapidly, but has become a major target for network-based illegal attackers.. In addition, IP spoofing among attackers' various attack techniques generally involves resource exhaustion attacks. Therefore, fast detection and response techniques are required. The existing detection method for IP spoofing attack performs the final authentication process according to the analysis and matching of traceback information of the client who attempted the connection request. However, the simple comparison method of traceback information may require excessive OTP due to frequent false positives in an environment requiring service transparency. In this paper, symmetric key cryptography based on traceback information is used as mutual authentication information to improve this problem. That is, after generating a traceback-based encryption key, mutual authentication is possible by performing a normal decryption process. In addition, this process could improve the overhead caused by false positives.

다양한 모바일 단말을 활용한 스마트 워크에 대한 수요 증가 및 클라우드, 웨어러블 컴퓨팅, 차세대 유·무 선 통신망 등과 같은 융합형 인프라 기반의 서비스 증가에 따라 네트워크의 신뢰성이 매우 중요한 문제로 대두되고 있다. 그러나, 아직까지 이러한 융합형 인프라 기반의 서비스 제공 시 네트워크의 신뢰성을 제공하는 방안에 대한 연구는 거의 이루어지지 않고 있다. 따라서, 본 논문에서는 이러한 문제를 해결하고 클라우드 기반 사무환경 구축 시 적용할 수 있는 ISNTC (Intelligent Safe Network Technology based on Cloud)를 제안한다. 제안하는 ISNTC는 SDN (Software Defined Network) 기반의 세이프 네트워크 기술을 활용하여 사용자 데이터를 전송하는 방식이며, 동 기화된 전송 경로 및 전송 타이밍을 통해 안전한 데이터 전송을 보장한다. 제안한 ISNTC의 성능 평가는 OPnet 기반 의 시뮬레이션을 통해 이루어졌으며, 이를 통해 제안한 방안의 우수성을 검증하였다.

According to the necessity of smart working with various mobile devices, and the increasing services based on the converged infrastructures such as Cloud, Wearable Computing, Next Generation Wired/Wireless Mobile Networks, the network reliability has been one of the most important things. However, the research related to the network reliability is still insufficient. To solve these problems, we propose the ISNTC (Intelligent Safe Network Technology based on Cloud), which uses the safe network technique based on SDN, to be adopted to the smart working environments. The proposed ISNTC guarantees secure data forwarding through the synchronized transmission path and timing. We have verified the throughput which outperformed the existing techniques through the computer simulations using OPnet.

스마트폰과 모바일 환경이 발전하면서 개인의 업무 수행을 위한 시간과 공간의 제약이 사라지고 있다. 기업은 클라 우드 컴퓨팅을 통하여 비용을 절감하고 사업의 범위를 빠르게 확대할 수 있게 되었다. 다양한 클라우드의 사용이 확대되면 서 사용자, 데이터, 어플리케이션의 경계가 사라지고 있다. 경계(Perimeter)을 기준으로 하는 전통적인 보안 접근은 클라우 드 환경에서 효용을 잃어가고 있다. 이에, 본 논문에서는 클라우드 환경에서 기존 Network Access Control(NAC)의 한계를 기술하고 이를 보완한 네트워크 보안 기술을 제안한다. 관련연구로 SDP에 대해서 설명하고, NAC의 한계를 극복하기 위해 SDP(Software Defined Perimeter)를 융합하고 동시에 클라우드 환경의 지원을 위한 새로운 프레임워크로의 역할을 설명한 다. 본 논문에서 제안한 새로운 프레임워크는 물리적인 부분과 소프트웨어적인 부분에 SDP 기술을 적용하여 IP 기반이 아 닌 신원 중심 접근제어 제공, 암호화된 세그먼트 관리, 동적정책관리 등을 지원하는 소프트웨어 기반의 네트워크 보안 솔루 션을 제안한다.

As the smartphone and mobile environment develop, the time and space constraints for individual work performance are disappearing. Companies can reduce costs and expand their business quickly through cloud computing. As the use of various cloud expands, the boundaries of users, data, and applications are disappearing. Traditional security approaches based on boundaries (Perimeter) are losing their utility in the cloud environment. This paper describes the limitations of existing network access control (NAC) in a cloud environment and suggests network security technology that complements it. The study explains the SDP and combines SDP(Software Defined Perimeter) to overcome the limitations of NAC, while at the same time explaining its role as a new framework for supporting the cloud environment. The new framework proposed in this paper suggests a software-based network security solution that supports physical and software parts, providing identity-based access control, encrypted segment management, and dynamic policy management, not IP-based.

본 논문에서는 셀 간 정보의 공유를 통한 협력이 가능한 연결된 네트워크 구조를 갖는 5G Cloud RAN 네트워크에 적용이 가능한 새로운 자원 관리 알고리즘을 제안한다. 제안 알고리즘은 네트워크 협력의 이점을 활 용하여 공정성과 처리량을 동시에 증가시킨다. 이러한 네트워크 성능 최적화는 허용이 가능한 양의 신호 처리 오버헤드 및 계산 복잡도 내에서 달성될 수 있으며 시뮬레이션 결과는 셀 외곽 사용자에 대한 네트워크 용량이 기존의 방법 대비 40% 가량 향상되었으며 사용자간 공정성 또한 Jain의 공평성 지수를 기준으로 약 23% 향상되 었음을 확인하였다.

A new resource management algorithm is proposed for 5G networks which have a coordinated network architecture. By sharing the contol information among multiple neighbor cells and managing in centralized structure, the propsed algorithm fully utilizes the benefits of network coordination to increase fairness and throughput at the same time. This optimization of network performance is achieved while operating within a tolerable amount of signaling overhead and computational complexity. Simulation results confirm that the proposed scheme improve the network capacity up to 40% for cell edge users and provide network-wise fairness as much as 23% in terms of the well-knwon Jain’s Fainess Index.

This paper presents the novel observation model, called Modified Spherical Signature Descriptor(MSSD), capable of representing 2D image generated from 3D point cloud data. The Modified Spherical Signature Descriptor has a uniform mesh grid to accumulate the occupancy evidence caused by neighbor point cloud data. According to a kind of area such as wall, road, tree, car, and so on, the evidence pattern of 2D image looks so different each other. For the parameter learning of Convolutional Neural Network(CNN) layers, these 2D images were applied as the input layer. The Convolutional Neural Network, one of the deep learning methods and familiar with the image analysis, was utilized for the urban structure classification. The case study on CNN practice was introduced in detail in this paper. The simulation results shows that the classification accuracy of CNN with 2D images of the proposed MSSD was improved more than the traditional methods' one.

The influence of the changes about the number of the nodes with different states by the external factors is not considered in the traditional model. So, traditional models are not suitable for the cloud network which is dynamic. A virus propagation model of computer, HSIR propagation model, whish is suitable for the cloud environment is proposed in this paper. The model considers the dynamic changes of the cloud environment, the implementation of the immunization strategy and the time difference between the virus spread and the implementation of the immunization strategy. Additionally, the balance point of the HSIR model is found and the stability of the balance point is proved by the mathematical theory. The HSIR virus propagation model can describe the process of the virus propagation in the cloud environment more realistic.

Social network has become the important platform for the current social individual to exchange information and access various media. Traditional searching and locating method is faced with the characteristics of the high order correlation and implicit correlation for large-scale users, and user-association multidimensional and heterogeneity. In order to effectively deal with large-scale social network data, and improve the efficiency of user’s locating, this article introduces the peer-to-peer distributed searching mechanism with the help of the cloud computing platform. This searching mechanism assigns user a logical identifier, and matches the underlying physical address and the upper users’ logical address, so as to build the cloud logical topology structure of social network. This paper designs a K neighbor discovery algorithm. It is used to cluster the nodes according to the features of the user, so as to realize the quickly locating of social network. The performance of the algorithm is analyzed according to the user’s searching logic path length of social network, and information amount of routing state. The simulation of the algorithm is evaluated by maintenance costs of average network aggregation coverage and query time. The performance analysis and simulation results demonstrate that social network cloud has good performance and searching efficiency.

As one of the driving forces changing the way research and industry uses virtualization, distributed computing, internet, software and web services today, cloud computing stands tall. A cloud is an ecosystem of data centers, distributed servers, and clients all interconnected to each other. The unique selling point of a cloud is its reduced cost of ownership in comparison to traditional models. This primary advantage is complemented by fault tolerance, high availability opportunity, scalable and flexible structure, reduced infrastructure overheads for users, and services that can be accessed as and when required. One of the challenges that face cloud computing is load balancing. Load balancing assures optimum use of available resources, thereby, enabling consistency and performance of the overall system. An imbalance of load causes a single node or nodes to operate beyond its optimum levels. As a result, there could be either a gradual or a rapid loss in overall efficiency of the system leading to increase in emission rates and inefficient use of energy. In this paper, we have focused on resourceful load balancing coupled with a technique which reduces flooding. We have discussed how a combination of these is able to ensure efficient routing with reduced carbon emission.

This paper designed the learning resource storage model mainly based on Hadoop platform, and the system structure was optimized on the basis of the original system against the problems of inefficient storage of massive small files, high memory overhead on the Hadoop platform. The PBL teaching resources are classified to improve the utilization ratio of resources and access efficiency of resources according to the file size and file correlation. When processing the small files, small files are merged according to the knowledge point, and then storage processing is conducted with the method of handling large files, giving full play to the advantages of processing files in the Hadoop. Simulation experiment is conducted in the laboratory environment, to compare the integrated storage space with the time required for resources storage. The result is that the model can reduce the storage space of the file, and improve the integrated storage efficiency on the basis of not affecting file storage.

There are so many security risks for the users of cloud computing, but still the organizations are switching towards the cloud. The cloud provides data protection and a huge amount of memory usage remotely or virtually. The organization has not adopted the cloud computing completely due to some security issues. The research in cloud computing has more focus on privacy and security in the new categorization attack surface. User authentication is the additional overhead for the companies besides the management of availability of cloud services. This paper is based on the proposed model to provide central authentication technique so that secured access of resources can be provided to users instead of adopting some unordered user authentication techniques. The model is also implemented as a prototype.

Cloud Computing is evolving as a key computing platform for sharing infrastructures and applications with flexible scalable computing processing power. Virtualization is a core technology for enabling cloud sharing ability. The management of cloud core infrastructure is essential to provide cloud computing environments. And the management, monitoring, and configuring of network from the cloud is another key topic in this paper. This paper presents a new management model architecture named MulCNeT(Multi-tenant, Cloud-based, Network Property trusteeship ). A system, which has been developing based on this architecture, is introduced. The architecture is based on cloud model, focusing on monitoring, configuring, and managing network with property management business model.