Introduction to Cloud Computing

VM3: Measuring, modeling and managing VM shared resources

The initial hype of any development can eclipse its practical applications. The present paper attempts to separate the hype of cloud computing and explores the possibilities of resource sharing in libraries through Cloud Computing. The present study is based on the usage pattern of cloud computing in libraries and explores a model for resource sharing. It also proposes a cloud based resource sharing framework for libraries for moving from ground to the amorphous cloud. Although a lot of work has been done on cloud computing yet none of the works has been dedicated to the cause of libraries. The paper presnts a comparative study on various library resource sharing practices, library cloud adoption framework as well as cloud based library products and cloud services providers.

Organizations are now seriously considering adopting cloud into the existing business context, but migrating data, application and services into cloud doesn't come without substantial risks. These risks are the significant barriers for the wider cloud adoption. There are works that consolidate the existing work on cloud migration and technology. However, there is no secondary study that consolidates the state of the art research and existing practice on risk management in cloud computing. It makes difficult to understand the risks management trend, maturity, and research gaps. This paper investigates the state of the art research and practices relating to risk management in cloud computing and discusses survey results on migration goals and risks. The survey participants are practitioners from both public and private organizations of two different locations, i.e., UK and Malaysia. The authors identify and classify the relevant literature and systematically compare the existing works and survey results. The results show that most of the existing works do not consider the existing organization and business context for the risk assessment. The authors' study results also reveal that risk management in cloud computing research and practice is still not in a mature stage but gradually advancing. Finally, they propose a risk assessment approach and determine the relative importance of the migration goals from two real migration use cases.

With the rapid development of national economy, our demand for power resources has also increased rapidly. The traditional power grid can not be achieved operational status of equipment for effective monitoring and control, we will not be able to ensure the quality and quality of electricity service. Cloudbased build smart grid information platform enables gathering the parameter of grid operation state, greatly increasing the economic efficiency of power enterprises. Firstly, cloud computing and smart grid briefly described, and then pointed out that cloud computing applications in the smart grid information platform, and specialized of cloud-based smart grid information platform, in order for the power grid enterprises to enhance their level of service and provide effective reference. KEYWORD: cloud computing; smart power grids; information platform; Application 4th International Conference on Mechanical Materials and Manufacturing Engineering (MMME 2016) © 2016. The authors Published by Atlantis Press 202 3 THE MAIN PROBLEM IS STILL THE SMART GRID INFORMATION PLATFORM FACING The transition to a cloud computing data center. Our data center because of the different regions and the degree of information presents different characteristics, some data centers already have a basic cloud computing capabilities, but most data centers still remain in the initial level, to ensure the normal operation of the existing data center power under the premise, gradual transition to cloud computing data center in order to meet future requirements of the development of smart grid. Restricting data transmission problem. State Grid Corporation between provincial branches and there are a lot of data needs to be transmitted, will bring massive data transmission efficiency decreases, taking into account the load balancing device between the cloud node, thus building a wide area network channels to resolve data transmission problem. Security of data transmission. Data Center for Cloud Computing power companies to build internal private cloud, capable of self-maintenance and management, system wide area network data transmission services, according to the third security zone defined security partition, and install proprietary equipment production area and external public network isolation . 4 CLOUD COMPUTING APPLICATIONS IN THE SMART GRID INFORMATION POLICY PLATFORM Integrate and optimize the management of heterogeneous resources. Among the smart grid systems, data resources, information systems between various platforms and mutually dispersed, severe heterogeneity, thus leading to the integration of the quality of information sharing to increase difficulty, unable to effectively improve network management efficiency for the power distribution system electrical work quality management and marketing operations system maintenance upgrade will not be able to effectively meet demand (Jie Yang, 2014). Cloud computing technology enables the power system to effectively integrate the various business data and computing resources on the basis of information for business collaboration platform built on effective, able to satisfy the huge demand for smart grid information resource sharing. Cloud computing network virtualization and application virtualization technology, the server virtualization technology and other resources to achieve each service abstraction, and then realize the difference between the system and the software effectively eliminated by the appropriate method to achieve the effective integration of information resources, and then to optimize the management, greatly improving the efficiency of heterogeneous resources management applications. Distributed storage and management of large volume of data. After application of smart grid information platform construction, customer service requests varied, huge data volume information brought it to the storage and management of data provides a great challenge. Cloud computing technology in general face amount of data each smart grid system and unit collected using forms distributed storage of data to achieve the effective storage, greatly to ensure the security and reliability of data storage. GFS effective use of technology with each node, the master server is responsible for the effective management of the whole file system, the file system and complete the work of the metadata stored in the data block is the main server for data storage block sliced, and by saving three or more redundant backup storage. At the same time, client access and complete access to the main block server information. Fast Power System Parallel Computation and Treatment. Smart grids and information platform for the large volume of data to provide stable operation of the power grid data base, but more critical is the analysis and design of each system operating data, planning and decision-making, and even failures and transient stability, state estimation and intelligent a variety of computing and content analysis and decision-making process, its huge computing needs, general computing system can not reach its goal (Wang, Mengxue, 2015). Cloud computing technology through parallel computing technology has greatly enhanced parallel processing capability of the power system, which provides parallel programming model makes more simple and efficient parallel algorithms. MapReduce as Google made an important parallel programming model, and its role in large data sets in parallel computing plays a very prominent. Parallel processing job results by the client to achieve effective user submitted, via decomposition automated master role, the corresponding task scheduling to working nodes and nodes work completed by the specific calculations and perform the appropriate task. 5 SMART GRID INFORMATION PLATFORM CLOUD COMPUTING ANALYSIS Cloud Computing Smart Grid Information Platform Architecture. Smart grid technology information platform architecture cloud computing include the following several levels and aspects (Yan Cui, 2015): First, that the platform infrastructure layer is the integration of the various hardware resources and related management capabilities of virtualized using virtual technology to achieve the corresponding hardware devices such as computers and network storage devices abstraction, and then complete the automation and optimization of resource data and load management; secondly, the main platform for the integration of the various layers of software

Cloud Computing has revolutionized the way Information Technology (IT) operates. Resource sharing in the cloud enables multiple users or providers to share resources, which improves the utilization of their resources. In the cloud, aggregated resources create a resource pool. The resource pools handle the dynamic demand of IT. Resource sharing enables cloud providers and customers to reduce their capital expenditure, operational expenditure, and total cost of ownership. Multiple cloud service providers connect to form a federation to share their resources among themselves to meet the dynamic demand of their customers. This chapter discusses the resource sharing in Intercloud, Social cloud, Mobile cloud, Manufacturing cloud, Vehicular cloud, and Green cloud. In each cloud, the various resource sharing approaches, strengths, and challenges are discussed.

The word “sharing” is not unfamiliar to everyone now, but many people do not understand the sharing of educational resources (ER), and the distribution of ER does not seem to be the topic that people are most concerned about. This paper is aimed at conducting research on the construction of educational big data information intelligence based on resource sharing. A parallel association rule mining approach for educational big data is proposed in this research. Now, the educational big data is classified, and then, the data is operated. At the same time, a CcG-based educational information resource sharing platform has also been built for educational resource sharing, which can be used for cloud download and upload functions of cloud computing (CcG) for teaching and educational resource sharing. The experimental results of this paper show that the educational information resource sharing platform in this paper processes about 150 information per second, and its service performance must be better than that of traditional educational platforms.

Service integration and supply on-demand coming from cloud computing can significantly improve the utilization of computing resources and reduce power consumption of per service, and effectively avoid the error of computing resources. However, cloud computing is still facing the problem of intrusion tolerance of the cloud computing platform and sensitive data of new enterprise data center. In order to address the problem of intrusion tolerance of cloud computing platform and sensitive data in new enterprise data center, this paper constructs a virtualization intrusion tolerance system based on cloud computing by researching on the existing virtualization technology, and then presents a method of intrusion tolerance to protect sensitive data in cloud data center based on virtual adversary structure by utilizing secret sharing. This system adopts the method of hybrid fault model, active and passive replicas, state update and transfer, proactive recovery and diversity, and initially implements to tolerate F faulty replicas in N=2F+1 replicas and ensure that only F+1 active replicas to execute during the intrusion-free stage. The remaining replicas are all put into passive mode, which significantly reduces the resource consuming in cloud platform. At last we prove the reconstruction and confidentiality property of sensitive data by utilizing secret sharing.

Single-instruction-set architecture (Single-ISA) heterogeneous multi-core processors (HMP) are superior to Symmetric Multi-core processors in performance per watt. They are popular in many aspects of the Internet of Things, including mobile multimedia cloud computing platforms. One Single-ISA HMP integrates both fast out-of-order cores and slow simpler cores, while all cores are sharing the same ISA. The quality of service (QoS) is most important for virtual machine (VM) resource management in multimedia mobile computing, particularly in Single-ISA heterogeneous multi-core cloud computing platforms. Therefore, in this paper, we propose a dynamic cloud resource management (DCRM) policy to improve the QoS in multimedia mobile computing. DCRM dynamically and optimally partitions shared resources according to service or application requirements. Moreover, DCRM combines resource-aware VM allocation to maximize the effectiveness of the heterogeneous multi-core cloud platform. The basic idea for this performance improvement is to balance the shared resource allocations with these resources requirements. The experimental results show that DCRM behaves better in both response time and QoS, thus proving that DCRM is good at shared resource management in mobile media cloud computing.

Cloud computing (CC) refers to an Internet-based computing technology in which shared resources, such as storage, software, information, and platform, are offered to users on demand. CC is a technology through which virtualized and dynamically scalable resources are presented to users on the Internet. Security is highly significant in this on-demand CC. Therefore, this paper presents improved metaheuristics with a fuzzy logic-based intrusion detection system for the cloud security (IMFL-IDSCS) technique. The IMFL-IDSCS technique can identify intrusions in the distributed CC platform and secure it from probable threats. An individual sample of IDS is deployed for every client, and it utilizes an individual controller for data management. In addition, the IMFL-IDSCS technique uses an enhanced chimp optimization algorithm-based feature selection (ECOA-FS) method for choosing optimal features, followed by an adaptive neuro-fuzzy inference system (ANFIS) model enforced to recognize intrusions. Finally, the hybrid jaya shark smell optimization (JSSO) algorithm is used to optimize the membership functions (MFs). A widespread simulation analysis is performed to examine the enhanced outcomes of the IMFL-IDSCS technique. The extensive comparison study reported the enhanced outcomes of the IMFL-IDSCS model with maximum detection efficiency with accuracy of 99.31%, precision of 92.03%, recall of 78.25%, and F-score of 81.80%.

This position paper summarizes the development and the present status of Department of Defense (DoD) and other government policies and guidances regarding cloud computing services. Due to the heterogeneous and growing biomedical big datasets, cloud computing services offer an opportunity to mitigate the associated storage and analysis requirements. Having on-demand network access to a shared pool of flexible computing resources creates a consolidated system that should reduce potential duplications of effort in military biomedical research. Interactive, online literature searches were performed with Google, at the Defense Technical Information Center, and at two National Institutes of Health research portfolio information sites. References cited within some of the collected documents also served as literature resources. We gathered, selected, and reviewed DoD and other government cloud computing policies and guidances published from 2009 to 2017. These policies were intended to consolidate computer resources within the government and reduce costs by decreasing the number of federal data centers and by migrating electronic data to cloud systems. Initial White House Office of Management and Budget information technology guidelines were developed for cloud usage, followed by policies and other documents from the DoD, the Defense Health Agency, and the Armed Services. Security standards from the National Institute of Standards and Technology, the Government Services Administration, the DoD, and the Army were also developed. Government Services Administration and DoD Inspectors General monitored cloud usage by the DoD. A 2016 Government Accountability Office report characterized cloud computing as being economical, flexible and fast. A congressionally mandated independent study reported that the DoD was active in offering a wide selection of commercial cloud services in addition to its milCloud system. Our findings from the Department of Health and Human Services indicated that the security infrastructure in cloud services may be more compliant with the Health Insurance Portability and Accountability Act of 1996 regulations than traditional methods. To gauge the DoD's adoption of cloud technologies proposed metrics included cost factors, ease of use, automation, availability, accessibility, security, and policy compliance. Since 2009, plans and policies were developed for the use of cloud technology to help consolidate and reduce the number of data centers which were expected to reduce costs, improve environmental factors, enhance information technology security, and maintain mission support for service members. Cloud technologies were also expected to improve employee efficiency and productivity. Federal cloud computing policies within the last decade also offered increased opportunities to advance military healthcare. It was assumed that these opportunities would benefit consumers of healthcare and health science data by allowing more access to centralized cloud computer facilities to store, analyze, search and share relevant data, to enhance standardization, and to reduce potential duplications of effort. We recommend that cloud computing be considered by DoD biomedical researchers for increasing connectivity, presumably by facilitating communications and data sharing, among the various intra- and extramural laboratories. We also recommend that policies and other guidances be updated to include developing additional metrics that will help stakeholders evaluate the above mentioned assumptions and expectations.

In today's world, digital businesses increasingly adopt cloud computing, and cybersecurity in the day-to-operations. Cloud computing helps the businesses to save money on equipment, training and infrastructure. The cyber security plays a crucial role in protecting data, systems and networks from cyber threats and unauthorized access. This research paper explores the complex relationship between cloud computing and cybersecurity. It probes the vulnerabilities inherent in shared resources, multi-tenancy, and external management, which expose businesses to risks like data breaches and DDoS attacks. Highlighting the role of AI and ML in real-time threat detection and response, the study also emphasizes encryption. This research adopts a qualitative research methodology whereby explorative and thematic analysis has been carried out. The findings underscore the need for adaptive, robust security strategies to protect data, ensure continuity, and maintain trust, suggesting future exploration of AI advancements and quantum computing in enhancing cloud security.

The term "cloud computing" refers to the delivery of various computer services to users via the Internet. These services include servers, storage, databases, networking, software, and analytics. The ability for businesses to swiftly and easily access computing resources as needed is one of the primary benefits of cloud computing, along with scalability, flexibility, and cost savings. To protect themselves from data breaches, distributed denial of service attacks, and insider threats, cloud providers and consumers alike need to deploy adequate security measures. Shared resources and timing inconsistencies within the hypervisor can make it possible for attackers to deduce sensitive information from other Virtual Machines (VMs). In this research, a software-based solution to the problem of VM timing side-channel assaults (SCAs) in CC (Cloud Computing) is proposed. Following an analysis of the process's empirical complexity, the solution uses a randomized timing method, which is compatible with all of the AES–128 sub-steps.

Abstract—Cloud computing is an Internet-based computing pattern through which shared resources are provided to devices on-demand. It is an emerging but promising paradigm to integrating mobile devices into cloud computing, and the integration performs in the cloud based hierarchical multi-user data-shared environment. With integrating into cloud computing, security issues such as data confidentiality and user authority may arise in the mobile cloud computing system, and it is concerned as the main constraints to the developments of mobile cloud computing. In order to provide safe and secure operation, a hierarchical access control method using modified hierarchical attribute-based encryption (M-HABE) and a modified three-layer structure is proposed in this paper. In a specific mobile cloud computing model, enormous data which may be from all kinds of mobile devices, such as smart phones, functioned phones and PDAs and so on can be controlled and monitored by the system, and the data can be sensitive to unauthorized third party and constraint to legal users as well. The novel scheme mainly focuses on the data processing, storing and accessing, which is designed to ensure the users with legal authorities to get corresponding classified data and to restrict illegal users and unauthorized legal users get access to the data, which makes it extremely suitable for the mobile cloud computing paradigms.

ABSTRACTCloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences. However, only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers: 1) selecting an appropriate cloud platform for a specific application could be challenging, as various cloud services are available and 2) existing general cloud platforms are not designed to support geoscience applications, algorithms and models. To tackle such barriers, this research aims to design a hybrid cloud computing (HCC) platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing platform. This platform can manage different types of underlying cloud infrastructure (e.g., private or public clouds), and enables geoscientists to test and leverage the cloud capabilities through a web interface. Additionally, the platform also provides different geospatial cloud services, such as workflow as a service, on the top of common cloud services (e.g., infrastructure as a service) provided by general cloud platforms. Therefore, geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly. A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources.

With the rapid development of the Internet and cloud computing applications, the construction of a digital library in the cloud computing environment has been put on the agenda. Construction and management of digital libraries using cloud computing technology, can effectively utilize resources, reduce costs, achieve better information sharing, and improve the service function of the library. In this paper, studies digital library based on cloud computing. Firstly, introduces the characteristics and establish principles of digital library based on cloud computing; secondly, elaborated the concept and work principle of cloud computing; then, describes the key technology of cloud computing, and a detailed introduction of virtual storage technology in digital library; and finally introduced cloud computing digital library service model, including cloud software as a service layer (SaaS), cloud platform as a service layer (PaaS), cloud infrastructure as a service layer (IaaS).