Chinese Wall Security Policy

The cloud computing is an Internet-based resource sharing system in which virtualized resources are provided over the Internet. Cloud computing refers to a class of systems and applications that employ distributed resources for use in various applications; these computing resources are utilized over a network to facilitate the execution of tasks. However, cloud computing resources are heterogeneous and dynamic, connecting a broad range of resources. Thus, there are a large numbers of application and data center in the cloud computing environment. Therefore, the security issues of authentication and communication in application services and data center need to be considered in the cloud computing environment.

A Business Cloud is defined to be a collection of company datasets that are stored on the Cloud. For simplicity, we have assumed: Each company only has one dataset. There are information flows among these datasets. Within such an environment Chinese Wall Security Policy (CWSP) is revisited. Based on the physical view of Brewer and Nash, the Chinese Wall policy that regulates Conflict of Interest binary Relation, denoted by CIR among company datasets, is investigated. CIR has found to be anti-reflexive, symmetric and anti-transitive, in other words, the compliment of CIR is an equivalence relation. The main theorem states that, from the owner (a company) view, a dataset under CWSP will neve flow into enemies hands (companies in conflicts). More generally, if CIR is anti-reflexive and symmetric, then there are Aggressive Chinese Wall that surrounds the company datasets of those companies who are in conflict, and a Conservative Chinese Wall that that surrounds the company datasets whose owner are friends; note that the set friends is a subset of not in conflict. The main theorem states that no information of those company datasets that are stored within the Aggressive Chinese Wall may never penetrate the Wall, similar conclusion for Conservative Chinese Wall. However those company datasets that are outside these two walls may penetrate the two walls.

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date: September 30 th 2022

In this paper, we present a management system for distributed software development in cloud computing environments, called DDMan. Cloud computing environments provide more flexibility than conventional computing environments. In particular, the platform as a service (PaaS) in cloud computing environments provides benefits for users in a variety of aspects, such as application design, development testing, software deployment, team collaboration, web service integration, database integration, and scalability. Although the process of distributed software development evolves and changes by a lot in the past decade, there are still some user needs remaining unsatisfied. For managing distributed software development in cloud computing systems, we present the DDMan system, which extends the WebSD model of distributed software development management. In DDMan, the interactions among the roles in the software development are precisely defined. In addition to the conventional web-based interface, the DDMan system also supports the interface for the Amazon cloud environment, which is an approach to a fully automatic service of distributed software development management in cloud computing environments.

Cloud Computing has emerged as a computing paradigm where services are provided through the internet in recent years. Offering on-demand services has transformed the IT companies' working environment, leading to a linearly increasing trend of its usage. The provisioning of the Computing infrastructure is achieved with the help of virtual machines. A great figure of physical devices is required to satisfy the users' resource requirements. To meet the requirements of the submitted workloads that are usually dynamic, the cloud data centers cause the over-provisioning of cloud resources. The result of this over-provisioning is the resource wastage with an increase in the levels of energy consumption, causing a raised operational cost. High CO2 emissions result from this huge energy consumption by data centers, posing a threat to environmental stability. The environmental concern demands for the controlled energy consumption, which can be attained by optimal usage of resources to achieve in the server load, by minimizing the number of active nodes, and by minimizing the frequency of switching between active and de-active server mode in the data center. Motivated by these actualities, we discuss numerous statistical, deterministic, probabilistic, machine learning and optimization based computational solutions for the cloud computing environment. A comparative analysis of the computational methods, on the basis of architecture, consolidation step involved, objectives achieved, simulators involved and resources utilized, has also been presented. A taxonomy for virtual machine (VM) consolidation has also been derived in this research article followed by emerging challenges and research gaps in the field of VM consolidation in cloud computing environment.

In this paper, we conduct research on the library development prospects and challenges under the environment of big data and cloud computing. Increasingly nervous and public li braries are facing funding, resources construction pace slow or stagnant difficult situation, readers to the library cause in the new times challenge. Big data era has quietly come, for the knowledge storage, use and development as own duty, the library, how to improve the ability of handling large amounts of growth literature is urgent. Our methodology solves the issues well which will be meaningful.

In China, with the continuous advance of cloud computing technologies such as virtualization technology and big data processing technology, small and medium-sized enterprises are constantly updating and improving their management information system. In recent years, computer network technology and information technology have been gradually applied to enterprise management. With the development of enterprises, a large number of heterogeneous software services deployed on different servers are accumulated in enterprises. Cloud computing can not only reduce the cost of informatization construction of small and medium-sized enterprises, strengthen the integration of information resources, promote resource sharing, but also promote enterprise management and service innovation. In view of the development of social economy and the advance of the industry, if the enterprise wants to be better based on the market, it must realize the scientific management mode, and constantly improve the efficiency and quality of work. Through the cloud computing model, this paper introduces the system construction of the management information system for small and medium-sized enterprises in the cloud computing environment and the implementation scheme of enterprises in the cloud computing environment.

Arc4nix: A cross-platform geospatial analytical library for cluster and cloud computing

In order to improve the task scheduling strategy, a method based on genetic algorithm in cloud computing environment was proposed. First, the independent task scheduling algorithm and associated task scheduling algorithm commonly used in cloud computing are studied and compared, respectively, and their application characteristics, advantages, and disadvantages are analyzed in detail. Second, an independent task scheduling strategy based on multipopulation genetic algorithm is proposed for independent task scheduling in cloud environment, considering the scheduling time, scheduling cost, and system resource utilization of task set. The implementation steps of the algorithm are given in detail. Finally, the simulation experiment is carried out on Cloud Sim platform. The experimental results show that computing resource M is 10, subtask N is 2000, population size S is 80, and ETC matrix and RCU array are randomly generated by the system. As the number of iterations increases, the scheduling scheme formed by MCGA and CGA is more obvious and close to the subtask execution cost optimization. Finally, the optimized scheme is basically formed. However, the scheduling scheme formed by TGA has no obvious optimization effect on the subtask execution cost. It is proved that the algorithm proposed in this paper can effectively optimize the task scheduling efficiency and improve the utilization of cloud computing resources at the same time, providing a feasible idea and method for task scheduling in the cloud computing environment.

Interest in cloud computing has grown considerably over recent years, primarily due to scalable virtualized resources. So, cloud computing has contributed to the advancement of real-time applications such as signal processing, environment surveillance and weather forecast where time and energy considerations to perform the tasks are critical. In real-time applications, missing the deadlines for the tasks will cause catastrophic consequences; thus, real-time task scheduling in cloud computing environment is an important and essential issue. Furthermore, energy-saving in cloud data center, regarding the benefits such as reduction of system operating costs and environmental protection is an important concern that is considered during recent years and is reducible with appropriate task scheduling. In this paper, we present an energy-aware task scheduling approach, namely EaRTs for real-time applications. We employ the virtualization and consolidation technique subject to minimizing the energy consumptions, improve resource utilization and meeting the deadlines of tasks. In the consolidation technique, scale up and scale down of virtualized resources could improve the performance of task execution. The proposed approach comprises four algorithms, namely Energy-aware Task Scheduling in Cloud Computing(ETC), Vertical VM Scale Up(V2S), Horizontal VM Scale up(HVS) and Physical Machine Scale Down(PSD). We present the formal model of the proposed approach using Timed Automata to prove precisely the schedulability feature and correctness of EaRTs. We show that our proposed approach is more efficient in terms of deadline hit ratio, resource utilization and energy consumption compared to other energy-aware real-time tasks scheduling algorithms.

Multi-tenancy of the cloud maximizes the utility of computation and storage resources by multiplexing the underlying hardware infrastructure amongst cloud customers; however, it also introduces significant security issues such as information leakage between two virtual machines (VMs) even if certain access control policy (e.g., Chinese Wall security policy) has been deployed in the cloud. Physical resource isolation between VMs is an effective mechanism to remove the covert channels in the cloud and prevent information leakage; however, due to economic concerns or negligence, some cheap-and-lazy cloud providers are not motivated to enforce the physical resource isolation as they promised. In this paper, we first develop a mechanism to check the co-residency of two files on local hard disk(s) by measuring the file access time, and then extend our mechanism to check data storage co-residency on Amazon S3 cloud storage.

Cloud computing technology and services has become an important issue in the recent years. Cloud Computing needs lot of attention, time and innovative concepts for the technology to mature over a period of time. Many organizations show interest in adopting cloud computing technology and services because of its socio economic and time factor. Cloud computing is nothing but virtual centralization of different computers where the software and memory space is provided by the vendor and the data is managed by them. This leaves the client/customer unaware of where the process is running or where the data is stored. Security of the data is highly dependent on the vendor who has to provide an assurance to the customer on security issues by making service level agreements. This paper focus on security issues, requirements for providing a secured data in cloud computing environment by giving a standard service oriented cloud computing Architecture and management tools that can be used by the organizations for authentication, confidentiality and integrity. Thus providing secured data access and transfer.

This paper first states the relationship between big data and cloud computing, as well as their advantages, then analyzes the importance of guaranteeing the network information security and the network information security problems under the environment of big data cloud computing, and finally makes exploration into the guaranteed measures for network information security under the environment of big data cloud computing, which lays a solid foundation for guaranteeing the network information security.

Resources are provided mainly in the form of services in cloud computing. In the distribute environment of cloud computing, how to find the needed services efficiently and accurately is the most urgent problem in cloud computing. In cloud computing, services are the intermediary of cloud platform, services are connected by lots of service providers and requesters and construct the complex heterogeneous network. The traditional recommendation systems only consider the functional and non-functional requirements of services but ignore the links between providers and requesters of service, which result to the service position is not accurate. Focus on the problems, this study intends to model the relationship of the cloud services participants with the format of heterogeneous information network, which intend to mine the hidden relationships between services participants in the cloud computing environment. In theoretical research, we proposed a cloud service heterogeneous network extraction and automatic maintenance model, proposed a new service recommendation system based on heterogeneous service network ranking and clustering.

Virtualization is very important factor to reduce cost of data warehouse deployment. It can reduce cost as well as tremendous pressure of managing devices, storage servers, application models and man power. In current era, data warehouse is very effective concept that can make huge impact in organizational decision support system. Unlike database systems, data warehouse systems take large amount of time and cost to deploy and develop in-house system for an organization. This is the reason that people now think about cloud computing as a solution of the problem instead of implementing their own data warehouse system. This paper is about, how cloud computing environment can be established as an alternative of data warehouse system. It will also throw some light about better environment choice for the organizational need. Organizational data warehouse and EC2 (Elastic Cloud Computing) are discussed with different parameters like ROI, security, scalability, robustness of data, maintenance of system etc. Paper will help in comparative analysis of both the systems.