537 research outputs found

    Scalable architectures for platform-as-a-service clouds: performance and cost analysis

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    Scalability is a significant feature of cloud computing, which ad-dresses to increase or decrease the capacities of allocated virtual resources at application, platform, database and infrastructure level on demand. We investigate scalable architecture solutions for cloud PaaS that allow services to utilize the resources dynamically and effectively without directly affecting users. We have implemented scalable architectures with different session state management solutions, deploying an online shopping cart application in a PaaS solution, and measuring the performance and cost under three server-side session state providers: Caching, SQL database and NoSQL database. A commercial solution with its supporting state management components has been used. Particularly when re-architecting software for the cloud, the trade-off between performance, scalability and cost implications needs to be discussed

    Design and evaluation of a hierarchical multi-tenant data management framework for cloud applications

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    Cloud computing is a technology that enables elastic, on-demand resource provisioning. Migrating applications to the cloud can increase their elasticity, allowing them to adapt to workload changes by dynamically allocating resources. In a multi-tenant application multiple client organizations, each referred to as tenants, make use of one or more shared application instances. These shared instances must however behave like a private instance by guaranteeing both data separation and performance isolation for every tenant. In order to achieve high scalability, a multi-tenant application running on the elastic cloud requires a flexible and scalable architecture for both the computational resources and the storage resources. In this paper we present and evaluate the design of a data management framework which can be used to extend existing multi-tenant cloud applications in order to achieve high scalability of the storage resources. We describe the most important components, and discuss important design choices. The framework invokes data allocation algorithms in order to find a feasible allocation of tenant data resulting in a minimal operating cost and a maximal performance, while taking no more than 10 ms to execute

    Computing on Masked Data to improve the Security of Big Data

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    Organizations that make use of large quantities of information require the ability to store and process data from central locations so that the product can be shared or distributed across a heterogeneous group of users. However, recent events underscore the need for improving the security of data stored in such untrusted servers or databases. Advances in cryptographic techniques and database technologies provide the necessary security functionality but rely on a computational model in which the cloud is used solely for storage and retrieval. Much of big data computation and analytics make use of signal processing fundamentals for computation. As the trend of moving data storage and computation to the cloud increases, homeland security missions should understand the impact of security on key signal processing kernels such as correlation or thresholding. In this article, we propose a tool called Computing on Masked Data (CMD), which combines advances in database technologies and cryptographic tools to provide a low overhead mechanism to offload certain mathematical operations securely to the cloud. This article describes the design and development of the CMD tool.Comment: 6 pages, Accepted to IEEE HST Conferenc

    Cloud migration of legacy applications

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    Security Implications of Adopting a New Data Storage and Access Model in Big Data and Cloud Computing

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    This article examines the security implications of using cloud computing and Big Data. It employs a mixed methodology of qualitative and quantitative research and takes a critical realist epistemological approach. The objective is to identify the components of a theory for predicting and explaining [1, 4] the security implications associated with adopting the services provided by cloud computing and Big Data. The integration of various information sources and the widespread use of computing across diverse fields have resulted in a significant increase in data volume, scale, quantity, and diversity. Consequently, data management, storage, retrieval, and access have undergone significant changes. The latest developments in IT have brought forth novel technologies such as Cloud Computing and Big Data. Big Data comprises of technologies that rely on NoSQL (Not only SQL) databases, which enable the growth of data volumes, numbers, and types on a large scale. The new NoSQL systems are seen as solutions for meeting scalability requirements of large IT firms. Multiple open-source and pay-as-you-go NoSQL models are available for purchase

    Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

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    Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed
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