Towards building a cloud for scientific applications

The Cloud computing becomes an innovative computing paradigm, which aims to provide reliable, customized and QoS guaranteed computing infrastructures for users. This paper presents our early experience of Cloud computing based on the Cumulus project for compute centers. In this paper, we give the Cloud computing definition and Cloud computing functionalities. This paper also introduces the Cumulus project with its various aspects, such as design pattern, infrastructure, and middleware. This paper delivers the state-of-the-art for Cloud computing with theoretical definition and practical experience.     

A tensor-based distributed discovery of missing association rules on the cloud

An increasing number of data applications such as monitoring weather data, data streaming, data web logs, and cloud data, are going online and are playing vital in our every-day life. The underlying data of such applications change very frequently, especially in the cloud environment. Many interesting events can be detected by discovering such data from different distributed sources and analyzing it for specific purposes (e.g., car accident detection or market analysis). However, several isolated events could be erroneous due to the fact that important data sets are either discarded or improperly analyzed as they contain missing data. Such events therefore need to be monitored globally and be detected jointly in order to understand their patterns and correlated relationships. In the context of current cloud computing infrastructure, no solutions exist for enabling the correlations between multi-source events in the presence of missing data. This paper addresses the problem of capturing the underlying latent structure of the data with missing entries based on association rules. This necessitate to factorize the data set with missing data.The paper proposes a novel model to handle high amount of data in cloud environment. It is a model of aggregated data that are confidences of association rules. We first propose a method to discover the association rules locally on each node of a cloud in the presence of missing rules. Afterward, we provide a tensor based model to perform a global correlation between all the local models of each node of the network.The proposed approach based on tensor decomposition, deals with a multi modal network where missing association rules are detected and their confidences are approximated. The approach is scalable in terms of factorizing multi-way arrays (i.e. tensor) in the presence of missing association rules. It is validated through experimental results which show its significance and viability in terms of detecting missing rules.     

Automatic offloading of mobile applications into the cloud by means of genetic programming

The limited battery life of modern mobile devices is one of the key problems limiting their use. Even if the offloading of computation onto cloud computing platforms can considerably extend battery duration, it is really hard not only to evaluate the cases where offloading guarantees real advantages on the basis of the requirements of the application in terms of data transfer, computing power needed, etc., but also to evaluate whether user requirements (i.e. the costs of using the cloud services, a determined QoS required, etc.) are satisfied. To this aim, this paper presents a framework for generating models to make automatic decisions on the offloading of mobile applications using a genetic programming (GP) approach. The GP system is designed using a taxonomy of the properties useful to the offloading process concerning the user, the network, the data and the application. The fitness function adopted permits different weights to be given to the four categories considered during the process of building the model. Experimental results, conducted on datasets representing different categories of mobile applications, permit the analysis of the behavior of our algorithm in different applicative contexts. Finally, a comparison with the state of the art of the classification algorithm establishes the goodness of the approach in modeling the offloading process.     

On-demand minimum cost benchmarking for intermediate dataset storage in scientific cloud workflow systems

Many scientific workflows are data intensive: large volumes of intermediate datasets are generated during their execution. Some valuable intermediate datasets need to be stored for sharing or reuse. Traditionally, they are selectively stored according to the system storage capacity, determined manually. As doing science on clouds has become popular nowadays, more intermediate datasets in scientific cloud workflows can be stored by different storage strategies based on a pay-as-you-go model. In this paper, we build an intermediate data dependency graph (IDG) from the data provenances in scientific workflows. With the IDG, deleted intermediate datasets can be regenerated, and as such we develop a novel algorithm that can find a minimum cost storage strategy for the intermediate datasets in scientific cloud workflow systems. The strategy achieves the best trade-off of computation cost and storage cost by automatically storing the most appropriate intermediate datasets in the cloud storage. This strategy can be utilised on demand as a minimum cost benchmark for all other intermediate dataset storage strategies in the cloud. We utilise Amazon clouds’ cost model and apply the algorithm to general random as well as specific astrophysics pulsar searching scientific workflows for evaluation. The results show that benchmarking effectively demonstrates the cost effectiveness over other representative storage strategies.     

Copernicus,a hybrid dataflow and peer-to-peer scientific computing platform for efficient large-scale ensemble sampling

Compute-intensive applications have gradually changed focus from massively parallel supercomputers to capacity as a resource obtained on-demand. This is particularly true for the large-scale adoption of cloud computing and MapReduce in industry, while it has been difficult for traditional high-performance computing (HPC) usage in scientific and engineering computing to exploit this type of resources. However, with the strong trend of increasing parallelism rather than faster processors, a growing number of applications target parallelism already on the algorithm level with loosely coupled approaches based on sampling and ensembles. While these cannot trivially be formulated as MapReduce, they are highly amenable to throughput computing. There are many general and powerful frameworks, but in particular for sampling-based algorithms in scientific computing there are some clear advantages from having a platform and scheduler that are highly aware of the underlying physical problem. Here, we present how these challenges are addressed with combinations of dataflow programming, peer-to-peer techniques and peer-to-peer networks in the Copernicus platform. This allows automation of sampling-focused workflows, task generation, dependency tracking, and not least distributing these to a diverse set of compute resources ranging from supercomputers to clouds and distributed computing (across firewalls and fragile networks). Workflows are defined from modules using existing programs, which makes them reusable without programming requirements. The system achieves resiliency by handling node failures transparently with minimal loss of computing time due to checkpointing, and a single server can manage hundreds of thousands of cores e.g. for computational chemistry applications.     

云计算中的入侵检测模型

本文提出了一种云环境下的网络安全处理模型,模型中的每台云服务器都拥有自己的入侵检测系统,并且所有的服务器共享一个异常管理平台,该平台负责报警信息的接收、处理和日志管理.模型采用报警级别动态调整技术和攻击信息共享方法,最大限度地降低了漏报率和服务器遭受同种攻击的可能性,有效提高了检测效率和系统安全水平.     

智能电网中虚拟化云计算的应用和安全技术分析

智能电网符合当前需求,意义重大。首先简单介绍了云计算和智能电网,并对云计算在智能电网中的应用做了阐述,然后主要对云计算的安全技术进行了分析。     

Bounds on the multi-clients incremental computing for homogeneous decreasing computation sequences

This paper studies the performance of network-based incremental computing under various message sequences. We show the bounds on the time needed to compute the tasks requested by multiple clients. Our simulation result shows that the expected performance of random message sequences is close to the optimal performance.     

Empirical prediction models for adaptive resource provisioning in the cloud

Cloud computing allows dynamic resource scaling for enterprise online transaction systems, one of the key characteristics that differentiates the cloud from the traditional computing paradigm. However, initializing a new virtual instance in a cloud is not instantaneous; cloud hosting platforms introduce several minutes delay in the hardware resource allocation. In this paper, we develop prediction-based resource measurement and provisioning strategies using Neural Network and Linear Regression to satisfy upcoming resource demands.Experimental results demonstrate that the proposed technique offers more adaptive resource management for applications hosted in the cloud environment, an important mechanism to achieve on-demand resource allocation in the cloud.     

A collaborative and integrated platform to support distributed manufacturing system using a service-oriented approach based on cloud computing paradigm

Today's manufacturing enterprises struggle to adopt cost-effective manufacturing systems. Overview of the recent manufacturing enterprises shows that successful global manufacturing enterprises have distributed their manufacturing capabilities over the globe. The successes of global manufacturing enterprises depend upon the entire worldwide integration of their product development processes and manufacturing operations that are distributed over the globe. Distributed manufacturing agents' collaboration and manufacturing data integrity play a major role in global manufacturing enterprises' success. There are number of works, conducted to enable the distributed manufacturing agents to collaborate with each other. To achieve the manufacturing data integrity through manufacturing processes, numbers of solutions have been proposed which one of the successful solutions is to use ISO 10303 (STEP) standard. However, adopting this standard one can recognize antonym effects of integration and collaboration approaches that weaken both integration and collaboration capabilities of manufacturing agents. In our latest work, we had developed an integrated and collaborative manufacturing platform named LAYMOD. Albeit the platform in question was through enough to be applied in various collaborative and integrated CAx systems, its embedded structure hampers its application for collaboration in distributed manufacturing systems. To achieve an integrated and collaborative platform for distributed manufacturing agents, this paper proposes a service-oriented approach. This approach is originated from cloud computing paradigm known as one of the technologies which enables a major transformation in manufacturing industry. Also, to maintain the product data integration based on the STEP standard, a new service-oriented approach is proposed. This approach is in parallel to the new capability of the STEP standard for supporting XML data structures. The result is a new platform named XMLAYMOD. XMLAYMOD is able to support distributed manufacturing collaboration and data integration based on the STEP standard. The different aspects of this platform to fulfill the requirements of distributed collaboration and also to overcome the lacks of the STEP standard are discussed through a brief case study.     

An efficient and secure approach for a cloud collaborative editing

The demand for cloud-based collaborative editing service is rising along with the tremendously increased popularity in cloud computing. In the cloud-based collaborative editing environment, the data are stored in the cloud and able to be accessed from everywhere through every compatible device with the Internet. The information is shared with every accredited user in a group. In other words, multiple authorized users of the group are able to work on the same document and edit the document collaboratively and synchronously online. Meanwhile, during the whole collaborative editing process, the encryption technique is eventually applied to protect and secure the data. The encryption for the collaborative editing, however, could require much time to operate. To elevate the efficiency of the encryption, this study first analyzes the text editing in the collaborative service and presents a framework of the Red–Black tree, named as rbTree-Doc. The rbTree-Doc can reduce the amount of data to be encrypted. Although the trade-off for creating the Red–Black tree introduces extra cost, the experimental results of using rbTree-Doc in text editing operations, such as insertion and removal, show improved efficiency compared with other whole-document encryption strategy. Using rbTree-Doc, the efficiency is improved by 31.04% compared to that 3DES encryption is applied and by 23.94% compared to that AES encryption is applied.     

CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling

Since the appearance of cloud computing, computing capacity has been charged as a service through the network. The optimal scheduling of computing resources (OSCR) over the network is a core part for a cloud service center. With the coming of virtualization, the OSCR problem has become more complex than ever. Previous work, either on model building or scheduling algorithms, can no longer offer us a satisfactory resolution. In this paper, a more comprehensive and accurate model for OSCR is formulated. In this model, the cloud computing environment is considered to be highly heterogeneous with processors of uncertain loading information. Along with makespan, the energy consumption is considered as one of the optimization objectives from both economic and ecological perspectives. To provide more attentive services, the model seeks to find Pareto solutions for this bi-objective optimization problem. On the basis of classic multi-objective genetic algorithm, a case library and Pareto solution based hybrid Genetic Algorithm (CLPS-GA) is proposed to solve the model. The major components of CLPS-GA include a multi-parent crossover operator (MPCO), a two-stage algorithm structure, and a case library. Experimental results have verified the effectiveness of CLPS-GA in terms of convergence, stability, and solution diversity.     

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.     

Hierarchical attribute-based encryption and scalable user revocation for sharing data in cloud servers

With rapid development of cloud computing, more and more enterprises will outsource their sensitive data for sharing in a cloud. To keep the shared data confidential against untrusted cloud service providers (CSPs), a natural way is to store only the encrypted data in a cloud. The key problems of this approach include establishing access control for the encrypted data, and revoking the access rights from users when they are no longer authorized to access the encrypted data. This paper aims to solve both problems. First, we propose a hierarchical attribute-based encryption scheme (HABE) by combining a hierarchical identity-based encryption (HIBE) system and a ciphertext-policy attribute-based encryption (CP-ABE) system, so as to provide not only fine-grained access control, but also full delegation and high performance. Then, we propose a scalable revocation scheme by applying proxy re-encryption (PRE) and lazy re-encryption (LRE) to the HABE scheme, so as to efficiently revoke access rights from users.     

Uncertainty in cloud service relationships: Uncovering the differential effect of three social influence processes on potential and current users

Consumer cloud services are characterized by uncertainty before usage but also for individuals who are already using the service. Our cloud service relationship model posits that individuals facing continuous uncertainty during adoption and continuance decisions rely on their social environment to make evaluations and decisions. Drawing on a representative dataset of 2011 Internet users, we distinguish three social influence processes from social influence theory (identification, internalization, and compliance) and uncover their differential effect on potential and current users’ uncertainty evaluations and on usage intentions. Our results can help cloud providers to successfully manage their relationships with potential and current users.     

Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors

Many factors influence the adoption of cloud computing. Organizations must systematically evaluate these factors before deciding to adopt cloud-based solutions. To assess the determinants that influence the adoption of cloud computing, we develop a research model based on the innovation characteristics from the diffusion of innovation (DOI) theory and the technology-organization-environment (TOE) framework. Data collected from 369 firms in Portugal are used to test the related hypotheses. The study also investigates the determinants of cloud-computing adoption in the manufacturing and services sectors.     

A vector C and Fortran compiler for the FPS T-series: Experiences with compiling to occam I

We describe our implementation of C and Fortran preprocessors for the FPS T-series hypercube. The target of these preprocessors is the occam I language. We provide a brief overview of the INMOS transputer and the Weitek vector processing unit (VPU). These two units comprise one node of the T-series. Some depth of understanding of the VPU is required to fully appreciate the problems encountered in generating vector code. These complexities were not fully appreciated at the outset. The occam I language is briefly described. We focus on only those aspects of occam I which differ radically from C. The transformations used to preprocess C into occam I are discussed in detail. The special problems with the VPU both in terms of its (non)interface with occam I and in dealing with numerical programs is discussed separately. A lengthy discussion on the special techniques required for compilation is provided. C and Fortran are simply incompatible with the occam I model. We provide a catalogue of problems encountered. We emphasize that these problems are not so much with occam I but with preprocessing to occam I. We feel the CSP and occam I models are quite good for distributed processing. The ultimate message from this work should be seen in a larger context. Several languages—such as Ada and Modula-2—are being touted as the standards for the 1990s. These languages severely restrict parallel programming style; this may make saving dusty decks by preprocessing an impossibility.     

Perceived potential for value creation from cloud computing: a study of the Australian regional government sector

ABSTRACT

Cloud computing is gaining increasing interest as an enabling technology for improved productivity, efficiency and cost reduction. It also has the potential to add value to reliability and scalability of organisation’s systems, which would allow organisations to better focus on their core business and strategies. This research paper aimed at exploring the perceived potential for value creation from cloud computing in Australian Regional Governments. Using the Technology-Organization-Environment (TOE) framework, the Diffusion of Innovation (DOI) theory and Desires Framework (DF), a conceptual research model is devised. The key identified factors that are likely to influence adoption of cloud computing are identified along with the implications to current policies, anticipated benefits and challenges of adoption of cloud computing. The research employed a qualitative approach involving 21 in-depth interviews with Australian local councils’ IT managers. The findings of this research are mapped to the research model factors, thereby presenting and validating a number of value-adding concepts that are expected to contribute to investment decisions on cloud computing adoption.