云计算技术在计算机数据处理中的应用及发展对策

时代的进步推动了科学技术和网络技术的多领域、多层次发展,促使我们进入到云计算时代。云计算是一种基于网络计算机对数据进行处理的技术,但是因为工具和现实生活不配备的问题,导致云计算技术在计算机数据处理的过程中还存在很多问题。对此,需要加强云计算技术的研究和应用,扩大数据处理规模,提高自适应并行计算程度,以对多源信息进行加速处理,优化云计算处理速度,更好地为社会发展服务。     

云计算环境下实时流媒体业务的性能研究

由于虚拟化环境不同于常规的物理集群,流媒体业务迁移至云中后会遇到问题。针对这一现象,搭建私有云环境,部署流媒体系统,进行一系列性能测试和研究。实验结果表明,在引入了虚拟化环境后,由于无法有效进行资源隔离以及共存虚拟机的存在,流媒体业务的性能明显下降,分析结果为进一步深入研究云环境下如何有效提高流媒体业务的性能奠定了很好的基础。     

面向数据密集应用的云计算服务架构

提出了一种具有云计算特点的可伸缩的服务器架构,通过采用模块化的方式有效地分割服务功能,能以对用户透明的方式满足三维网络应用的各种存储和带宽的需要。将该方案在一个三维虚拟现实项目中进行测试,结果表明,该方案有较好的伸缩性和效率。     

Construction of cloud platform for personalized information services in digital library based on cloud computing data processing technology

Digital library has begun to enter a new era of cloud computing after Internet era, Web era, Grid era and Web 2.0 era. Combined with cloud computing technology, this thesis constructed a cloud service platform of digital library and described the service process and operating mechanism of digital library cloud service platform.     

Models of parallel data processing in multiprocessor computing systems

Models of parallel computations are considered for a wide class of data processing programs. Properties of programs are investigated and approaches to parallelizing sequential data processing programs and designing parallel programs are proposed. Computation optimizing problems are formulated.Translated from Kibernetika, No. 4, pp. 1–8, 42, July–August, 1989.     

用于云计算数据访问的多阶段身份认证

为有效解决云计算数据访问服务的隐私性和安全性问题,提出一种基于多阶段身份认证的云计算隐私保护数据访问算法.在注册阶段基于抑制方法将用户的个人信息存贮在云服务器中,在认证阶段采用口令、一次性令牌和条件属性来高效验证客户端的身份,在数据访问阶段使用数据加密/解密以便在云端实现更高的数据安全级别.将仿真结果与其它算法进行比较,验证了所提算法在隐私保护率、计算复杂度和身份认证精度方面均优于其它方法.     

High-performance computing systems and applications for AI

The Journal of Supercomputing -     

Key Derivation Policy for data security and data integrity in cloud computing

Cloud computing is currently emerging as a promising next-generation architecture in the Information Technology (IT) industry and education sector. The encoding process of state information from the data and protection are governed by the organizational access control policies. An encryption technique protects the data confidentiality from the unauthorized access leads to the development of fine-grained access control policies with user attributes. The Attribute-Based Encryption (ABE) verifies the intersection of attributes to the multiple sets. The handling of adding or revoking the users is difficult with respect to changes in policies. The inclusion of multiple encrypted copies for the same key raised the computational cost. This paper proposes an efficient Key Derivation Policy (KDP) for improvement of data security and integrity in the cloud and overcomes the problems in traditional methods. The local key generation process in proposed method includes the data attributes. The secret key is generated from the combination of local keys with the user attribute by a hash function. The original text is recovered from the ciphertext by the decryption process. The key sharing between data owner and user validates the data integrity referred MAC verification process. The proposed efficient KDP with MAC verification analyze the security issues and compared with the Cipher Text–Attribute-Based Encryption (CP-ABE) schemes on the performance parameters of encryption time, computational overhead and the average lifetime of key generation. The major advantage of proposed approach is the updating of public information and easy handling of adding/revoking of users in the cloud.     

云计算中海量数据高效索引方法

针对云计算环境中数据的海量性和分布性特点,以及现有的分布式B树索引方法存在访问效率较低的问题,提出一种云计算环境中海量数据高效索引方法,它在分布式B树的基础之上采用日志来记录节点的分裂历史,并基于节点分裂历史高效并发访问分布式B树.实验结果表明,它有效提高了云计算环境中海量、分布式的数据访问和索引效率.     

大数据和云计算技术的应用策略

目前,大数据和云计算在各行业得到了广泛运用,提升了企业的经济效益和社会效益,也为人们的生活创造了便利条件。文章主要分析了大数据和云计算技术,并对其应用策略进行了探讨,以供相关人员参考。     

Secure multi-server-aided data deduplication in cloud computing

Cloud computing enables on-demand and ubiquitous access to a centralized pool of configurable resources such as networks, applications, and services. This makes that huge of enterprises and individual users outsource their data into the cloud server. As a result, the data volume in the cloud server is growing extremely fast. How to efficiently manage the ever-increasing datum is a new security challenge in cloud computing. Recently, secure deduplication techniques have attracted considerable interests in the both academic and industrial communities. It can not only provide the optimal usage of the storage and network bandwidth resources of cloud storage providers, but also reduce the storage cost of users. Although convergent encryption has been extensively adopted for secure deduplication, it inevitably suffers from the off-line brute-force dictionary attacks since the message usually can be predictable in practice. In order to address the above weakness, the notion of DupLESS was proposed in which the user can generate the convergent key with the help of a key server. We argue that the DupLESS does not work when the key server is corrupted by the cloud server. In this paper, we propose a new multi-server-aided deduplication scheme based on the threshold blind signature, which can effectively resist the collusion attack between the cloud server and multiple key servers. Furthermore, we prove that our construction can achieve the desired security properties.     

Robustness improvement of component-based cloud computing systems

The Journal of Supercomputing - With the increasing popularity of Cloud computing systems, the demand for highly dependable Cloud applications has increased significantly. For this, reliability and...     

Extended missing data imputation via GANs for ranking applications

Data Mining and Knowledge Discovery - We propose Conditional Imputation GAN, an extended missing data imputation method based on Generative Adversarial Networks (GANs). The motivating use case is...     

MEACC: an energy-efficient framework for smart devices using cloud computing systems

Frontiers of Information Technology & Electronic Engineering - Rapidly increasing capacities, decreasing costs, and improvements in computational power, storage, and communication technologies...     

Fog computing: from architecture to edge computing and big data processing

The Journal of Supercomputing - Cloud computing plays a vital role in processing a large amount of data. However, with the arrival of the Internet of Things, huge data are generated from these...     

Privacy-preserving public auditing for educational multimedia data in cloud computing

Nowadays, as distance learning is being widly used, multimedia data becomes an effective way for delivering educational contents in online educational systems. To handle the educational multimedia data efficiently, many distance learning systems adopt a cloud storage service. Cloud computing and storage services provide a secure and reliable access to the outsourced educational multimedia contents for users. However, it brings challenging security issues in terms of data confidentiality and integrity. The straightforward way for the integrity check is to make the user download the entire data for verifying them. But, it is inefficient due to the large size of educational multimedia data in the cloud. Recently many integrity auditing protocols have been proposed, but most of them do not consider the data privacy for the cloud service provider. Additionally, the previous schemes suffer from dynamic management of outsourced data. In this paper, we propose a public auditing protocol for educational multimedia data outsourced in the cloud storage. By using random values and a homomorphic hash function, our proposed protocol ensures data privacy for the cloud and the third party auditor (TPA). Also, it is secure against lose attack and temper attack. Moreover, our protocol is able to support fully dynamic auditing. Security and performance analysis results show that the proposed scheme is secure while guaranteeing minimum extra computation costs.     

Resource virtualization methodology for on-demand allocation in cloud computing systems

The resources’ heterogeneity and unbalanced capability, together with the diversity of resource requirements in cloud computing systems, have produced great contradictions between resources’ tight coupling characteristics and user’s multi-granularities requirements. We propose a resource virtualization model and its on-demand allocation oriented infrastructure mainly providing computing services to solve that problem. A loosely coupled resource environment centered on resource users is created to complete a mapping from physical view of resources to logic view of resources. Heuristic resource combination algorithm (HRCA) is proposed to transform physical resources to logic resources, which meets two requirements: randomness in combination and fluctuation control to the size of resources granularities. On the basis of the appraisal indexes presented for the on-demand allocation, resource matching algorithm (RMA), targeting at resource satisfaction with the highest resource utilization, is designed to reuse resources. RMA can satisfy users’ requirement in limited time and keep resource satisfaction in the highest level in the condition of logic resources granularities being less than their required size. Resource reconfiguration algorithm (RRA) is presented to implement resource matching in the condition that virtual computing resource pool cannot match granularities of resource requirements. RRA assures the lowest resource refusal rate and the greatest resource satisfaction. We verify the effectiveness, performance and accuracy of algorithms in implementing the goal of resource virtualization centered on resource users and on-demand allocation.