数字图书馆云计算安全分析及管理方法研究

云计算技术在近几年快速兴起,并且快速应用到图书馆系统中来,但随之带来的还有安全问题.本文就云时代的数字图书馆所面临的安全问题进行了阐述,并且就如何解决这些安全问题进行了细致的研究.     

A continuous coupled hydrological and water resources management model

Human exploitation of water resources is widespread and its impact on hydrological fluxes is expected to increase in the future. Water use interacts in a complex manner with the hydrological system causing severe alterations of the hydrological fluxes with multifaceted feedbacks. Implementing this coupling within hydrological models is essential when dealing with the impact of human activities on water resources at all relevant scales. We contribute to the effort in developing models coupling natural and human systems with a distributed continuous model, named GEOTRANSF. The model allows to quantify, within the same framework, alterations in the natural regime and constraints and limitations to water resources availability. After presenting GEOTRANSF, an example of application to a medium-size Alpine catchment with streamflow modified by hydropower and distributed uses is discussed, followed by the analysis of the effect of suitable water uses scenarios in the same catchment.     

Prediction of mobile image saliency and quality under cloud computing environment

Recent years have witnessed the explosive growth of multimedia applications over networks and increasingly high requirements of consumers for multimedia signals in terms of quality of experience (QoE). Effective and efficient yet energy-saving saliency detection model and quality prediction method are eagerly desired, since they play critical roles in raising users' QoE and promoting the progress of green multimedia communication. Current studies of saliency detection and quality evaluation are far from ideal yet. In this paper we investigate the influence of complexity on visual saliency and quality. Complexity is an essential concept in human perception to visual stimulus, but it is substantially abstract and hard to be endowed with a clear definition. We suppose that brain systematically combines global and local features during the whole process of human perception. Global features lead a dominant position in seeking salient areas under the condition that image complexity is high, namely without obviously isolated foreground objects, whereas local features play a key role in an opposite situation. With this consideration, this paper establishes a novel framework for detecting visual saliency based on image complexity estimation before complexity-adaptive merging of global and local features. Furthermore, the concept of complexity is deployed for blind photographic image quality assessment (IQA) by means of saliency-based weighting. Features which refer to contrast, artifacts, brightness and natural scene statistics (NSS) are modified and integrated to derive a blind IQA model and predict the quality of photos. Based on the above two technologies, this paper introduces smart phones as mobile terminals, cloud platforms for speed-up and energy-saving, and wireless networks for transmission, and provides a practical mobile multimedia application. Comparative experiments validate that, within this application system, our proposed saliency detection model and blind photographic IQA method implement better than existing relevant competitors in terms of effectiveness and efficiency comparison.     

Application-aware integration of data collection and power management in wireless sensor networks

Sensors are typically deployed to gather data about the physical world and its artifacts for a variety of purposes that range from environment monitoring, control, to data analysis. Since sensors are resource constrained, often sensor data is collected into sensor databases that reside at (more powerful) servers. A natural tradeoff exists between resources (bandwidth, energy) consumed and the quality of data collected at the server. Blindly transmitting sensor updates at a fixed periodicity to the server results in a suboptimal solution due to the differences in stability of sensor values and due to the varying application needs that impose different quality requirements across sensors. In order to adapt to these variations while at the same time optimizing the energy consumption of sensors, this paper proposes three different models and corresponding data collection protocols. We analyze all three models with a Markov state machine formulation, and either derive closed forms for the operation point of the data collection application or suggest algorithms for estimating this operating point to achieve a minimal energy consumption. We observe that the operating point depends on environmental characteristics and application quality requirements, which the proposed algorithms aim to accommodate. Our experimental results show significant energy savings compared to the naive approach to data collection.     

CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end‐users under a usage‐based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter‐networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter‐networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy‐efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns. Copyright © 2010 John Wiley & Sons, Ltd.     

Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers

Cloud computing aims to provide dynamic leasing of server capabilities as scalable virtualized services to end users. However, data centers hosting cloud applications consume vast amounts of electrical energy, thereby contributing to high operational costs and carbon footprints. Green cloud computing solutions that can not only minimize the operational costs but also reduce the environmental impact are necessary. This study focuses on the Infrastructure as a Service model, where custom virtual machines (VMs) are launched in appropriate servers available in a data center. A complete data center resource management scheme is presented in this paper. The scheme can not only ensure user quality of service (through service level agreements) but can also achieve maximum energy saving and green computing goals. Considering that the data center host is usually tens of thousands in size and that using an exact algorithm to solve the resource allocation problem is difficult, the modified shuffled frog leaping algorithm and improved extremal optimization are employed in this study to solve the dynamic allocation problem of VMs. Experimental results demonstrate that the proposed resource management scheme exhibits excellent performance in green cloud computing.     

EFADS: Efficient,flexible and anonymous data sharing protocol for cloud computing with proxy re-encryption

The concept of cloud computing has emerged as the next generation of computing infrastructure to reduce the costs associated with the management of hardware and software resources. It is vital to its success that cloud computing is featured efficient, flexible and secure characteristics. In this paper, we propose an efficient and anonymous data sharing protocol with flexible sharing style, named EFADS, for outsourcing data onto the cloud. Through formal security analysis, we demonstrate that EFADS provides data confidentiality and data sharer's anonymity without requiring any fully-trusted party. From experimental results, we show that EFADS is more efficient than existing competing approaches. Furthermore, the proxy re-encryption scheme we propose in this paper may be independent of interests, i.e., compared to those previously reported proxy re-encryption schemes, the proposed scheme is the first pairing-free, anonymous and unidirectional proxy re-encryption scheme in the standard model.     

A simple, least-time, and energy-efficient routing protocol with one-level data aggregation for wireless sensor networks

The area of wireless sensor networks (WSN) is currently attractive in the research community area due to its applications in diverse fields such as defense security, civilian applications and medical research. Routing is a serious issue in WSN due to the use of computationally-constrained and resource-constrained micro-sensors. These constraints prohibit the deployment of traditional routing protocols designed for other ad hoc wireless networks. Any routing protocol designed for use in WSN should be reliable, energy-efficient and should increase the lifetime of the network. We propose a simple, least-time, energy-efficient routing protocol with one-level data aggregation that ensures increased life time for the network. The proposed protocol was compared with popular ad hoc and sensor network routing protocols, viz., AODV ( [35] and [12]), DSR (Johnson et al., 2001), DSDV (Perkins and Bhagwat, 1994), DD (Intanagonwiwat et al., 2000) and MCF (Ye et al., 2001). It was observed that the proposed protocol outperformed them in throughput, latency, average energy consumption and average network lifetime. The proposed protocol uses absolute time and node energy as the criteria for routing, this ensures reliability and congestion avoidance.     

Towards best practice implementation and application of models for analysis of water resources management scenarios

Water resources management models are widely used to evaluate planning or operational scenarios to support water resource management decision-making. However, the approaches to modelling used in the past have led to problems, such as modellers having difficulty establishing the credibility of their model with stakeholders, and stakeholders having difficulty understanding and trusting model results. A best practice approach to the implementation and application of water resources management models based on a quality assurance procedure is an appropriate means of overcoming these difficulties, and there are a number of guidelines and papers available promoting this approach. However, guidance in these on the use of models to analyse water resource planning scenarios is limited or not provided. This paper therefore provides guidance on the implementation and application of water resources management models with an emphasis on scenario analysis. This guidance is principally intended for practising modellers, and also for peer reviewers and stakeholders such as managers, decision makers, and community-based groups. Adoption strategies and recommendations for future directions are also discussed.     

A survey of mathematical models,simulation approaches and testbeds used for research in cloud computing

The first hurdle for carrying out research on cloud computing is the development of a suitable research platform. While cloud computing is primarily commercially-driven and commercial clouds are naturally realistic as research platforms, they do not provide to the scientist enough control for dependable experiments. On the other hand, research carried out using simulation, mathematical modelling or small prototypes may not necessarily be applicable in real clouds of larger scale. Previous surveys on cloud performance and energy-efficiency have focused on the technical mechanisms proposed to address these issues. Researchers of various disciplines and expertise can use them to identify areas where they can contribute with innovative technical solutions. This paper is meant to be complementary to these surveys. By providing the landscape of research platforms for cloud systems, our aim is to help researchers identify a suitable approach for modelling, simulation or prototype implementation on which they can develop and evaluate their technical solutions.     

A QoS-driven approach for cloud computing addressing attributes of performance and security

In recent years, cloud computing has been one of the most widely discussed topics in the field of Information Technology. Owing to the popularity of services offered by cloud environments, several critical aspects of security have aroused interest in the academic and industrial world, where there is a concern to provide efficient mechanisms to combat a wide range of threats. As is well known, the application of security techniques and methodologies has a direct influence on the performance of the system, since security and performance are two quantities that are inversely proportional. This means that if the service providers fail to manage their computing infrastructure efficiently, the demand for services may not be met with the quality required by clients, including security and performance requirements, and the computational resources may be used inefficiently. The aim of this paper was to define QoS-driven approaches for cloud environments on the basis of the results of a performance evaluation of a service in which different security mechanisms are employed. These mechanisms impose additional overhead on the performance of the service, and to counter this, an attempt was made to change computational resources dynamically and on-the-fly. On the basis of the results, it could be shown that in a cloud environment, it is possible to maintain the performance of the service even with the overhead imposed by the security mechanisms, through an alteration in the virtualized computational resources. However, this change in the amount of resources had a direct effect on the response variables.     

A cloud resource management model for the creation and orchestration of social communities

Managing resources, context and data in mobile clouds is a challenging task. Specific aspects of spontaneity, large interaction space and dynamic interaction share a metaphorical resemblance to chemistry, chemical reactions and solutions. In this paper, it is argued that by adopting a nature-inspired chemical computing model, a mobile cloud resource management model can be evolved to serve as the basis for novel service modelling and social computing in mobile clouds. To support the argument, a chemistry inspired computation model, Chemistry for Context Awareness (C₂A), is extended with Higher Order Chemical Language (HOCL) and High Level Petri-net Graph (HLPNG) formalisms. A scenario and simulation-based evaluation of the proposed model, focusing on two applications dynamic service composition and social communities identification, is also presented in this paper. The formal encoding of C₂A validates its assumptions, enabling formal execution and analysis of context-based interactions that are derived using C₂A principles.     

Realizing modelling outcomes: A synthesis of success factors and their use in a retrospective analysis of 15 Australian water resource projects

We review several papers that have afforded insights into determinants of positive outcomes (e.g. the adoption of tools, improved learning and/or collaboration) from modelling projects. From a subsequent internet search in the environmental domain we identified 33 such factors that are then invoked in a transferable survey-based method to facilitate structured reflections by model developers on 15 projects. Four factors were considered most necessary to realize overall success for any modelling project. Three factors related to aspects of stakeholder engagement in the modelling process; the other to critical thinking around problem framing and the role(s) of models. The latter factor was considered reasonably well-achieved across the projects. Harder to control were the stakeholder engagement factors which, along with project management considerations, can constrain or enable achievement of other factors. The paper provides further evidence of the critical need to consider non-technical aspects in the design and implementation of modelling projects.     

基于云计算的桥梁安全与健康监测数据中心基础架构及其数据管理技术

桥梁安全监测数据信息以几何级数速度增长,对数据中心海量数据存储、管理提出了智能化、高效率化的要求.分析了桥梁安全与健康监测数据中心的数据管理技术及发展趋势;阐述了基于云计算（Cloud Computing）的数据中心基础架构;深入探讨了关键设备和软件的选型;对基于云计算的桥梁安全监测数据中心的应用前景进行了展望.     

Integrated sensor and management system for urban waste water networks and prevention of critical situations

This work describes the design and implementation of improvements to the monitoring system of an urban waste water network, resulting in more efficient management of the system. To achieve this objective, the latest communications technology has been incorporated into heterogeneous networks and sensor systems. This technology includes mobile systems, which take measurements and transmit images in real time, an intelligent platform for processing and management of variables, and the implementation of wireless sensor networks (WSNs) designed with specific protocols and tools that allow the rapid deployment of the network and allow measurements to be taken in emergency situations. The sensors in this type of installation are extremely important for the management of the system as they allow us to collect information and make decisions with sufficient time to deal effectively with critical situations such as flooding or overloading of the waste water system, or environmental problems such as dumping of possible pollutants, as well as to make the best use of the water cycle. The solution presented here automates large portions of the processes, minimizing the possibility of human error, and increasing the frequency and accuracy of the measurements taken, ensuring a robust communication system covering all the elements involved to provide ubiquity of information, and finally gives an application layer to manage the system and receive alerts.     

A geospatially-enabled web tool for urban water demand forecasting and assessment of alternative urban water management strategies

This study develops and demonstrates the Integrated Urban Water Model (IUWM) for forecasting urban water demand with options to assess effects of water conservation and reuse. While water and energy balance drive hydrologic, storage and recycling simulations on a daily timestep, social and infrastructural processes are resolved by spatially distributed parameters. IUWM is deployed as an online tool with geographical information system (GIS) interfaces, enhancing its ease of use and applicability at building to municipal scales. The performance of the model at varying spatial scales was evaluated with extensive water metering data for the City of Fort Collins, Colorado. The calibrated model provided very good estimates of demands at individual block group as well as the municipal service area. The capacity of IUWM for the assessment of the spatiotemporal variability of water consumption and effects of water demand management strategies under climate and urban growth scenarios is discussed.     

An integrative modelling approach for linking environmental flow management,ecosystem service provision and inter-stakeholder conflict

Fifteen water flow-dependent ecosystem services (ES) are modelled for the case of the Ter (Catalonia, Spain), a river with persisting intra- and inter-basin conflicts on water flows. The aim is to analyse ES response (and ensuing social reaction) to changes in water flow management, in a context of several tradeoffs and synergies driven by access to water use.We argue for a new modelling approach to integrate diverse values and perspectives through engaging with stakeholders' concerns and claims. This is done under different conditions, including droughts, wet years, and different options for managing flows. Our approach involves two stages, namely water allocation modelling, including scenario development, and ES provision modelling, including participatory design of service suitability curves. The method presented allows analysing spatial/temporal patterns and ES performance. The paper explains methodological innovation and its application to highlight the role of recurrent socio-environmental conflicts in water management decisions.     

Wavelet-based multiresolution analysis for data cleaning and its application to water quality management systems

Data cleaning techniques are useful for extracting desirable knowledge or interesting patterns from existing databases in engineering applications. The major problems of conventional techniques (e.g., Fourier Transformation Technique) are that they are (1) more appropriate in linear systems than nonlinear systems, and (2) stringently depend on state space functions. In this study a wavelet-based multiresolution analysis technique (WMAT) is proposed for reducing noises induced by complex uncertainty. The approach is applied to a river water quality simulation system for showing its practicability in data cleaning and parameter estimation. Clean data are prepared through running a Thomas’ river water quality model and polluted data are synthesized by mixing clean data with white Gaussian noises. The results show that WMAT will not distort the clean data, and can effectively reduce the noise in the polluted data. The data denoised by WMAT are furthermore used for estimating the modeling parameters. It is also indicated that the parameters estimated with the denoised data through WMAT are much closer to real values than those (1) with polluted data through WMAT and (2) with data through Fourier analysis technique. It is thus recommended that the prepared data be used for estimating the modeling parameters until being cleaned with WMAT.