A Comparative Analysis on Handling Big Data Using Cloud Services

In this era of technology, a lot of advancements have been done in almost every field such as medical, science, aerospace and other fields. With the increasing advancements in technology, a lot of data is being produced at the same time. For instances in the field of medicine there is a huge amount of data that is being generated as there are hundreds and thousands of patients who came for their checkup. So now the question arises where this huge amount of data is being stored. This huge amount of data is called as Big Data. And the major problem faced is how to manage and organize this huge amount of data along with its security and not being lost. Big data is used for extracting a lot of useful information but it is not easy to organize it. If the data is being lost than there are a lot of problems that can occur on a huge level as a lot of data being stored in big data is very confidential. This data can be stored on cloud which is the new advancement in the field of technology as it is highly reliable for huge amount of information. So, in this survey paper we will discuss about the solutions of organizing and handling big data proposed by different authors

Big Data in Smart-Cities: Current Research and Challenges

Smart-cities are an emerging paradigm containing heterogeneous network infrastructure, ubiquitous sensing devices, big-data processing and intelligent control systems. Their primary aim is to improve the quality of life of the citizens by providing intelligent services in a wide variety of aspects like transportation, healthcare, entertainment, environment, and energy. In order to provide such services, the role of big-data and its analysis is extremely important as it enables to obtain valuable insights into the large data generated by the smart-cities.  In this article, we investigate the state-of-art research efforts directed towards big-data analytics in a smart-city context. Specifically, first we present a big-data centric taxonomy for the smart-cities to bring forth a generic overview of the importance of big-data paradigm in a smart-city environment. This is followed by the presentation of a top-level snapshot of the commonly used big-data analytical platforms. Due to the heterogeneity of data being collected by the smart-cities, often with conflicting processing requirements, suitable analytical techniques depending upon the data type are also suggested. In addition to this, a generic four-tier big-data framework comprising of the sensing hub, storage hub, processing hub and application hub is also proposed that can be applied in any smart-city context. This is complemented by providing the common big-data applications in a smart-city and presentation of ten selected case studies of smart-cities across the globe. Finally, the open challenges are highlighted in order to give future research directions

Master of Science

thesisCloud infrastructures have massively increased access to latent compute resources al- lowing for computations that were previously out of reach to be performed efficiently and cheaply. Due to the multi-user nature of clouds, this wealth of resources has been "siloed" into discrete isolated segments to ensure privacy and control over the resources by their current owner. Modern clouds have evolved beyond basic resource sharing, and have become platforms of modern development. Clouds are now home to rich ecosystems of services provided by third parties, or the cloud itself. However, clouds employ a rigid access control model that limits how cloud users can access these third-party services. With XNet, we aim to make cloud access control systems more flexible and dynamic by model- ing cloud access control as an object-based capability system. In this model, cloud users create and exchange "capabilities" to resources that permit them to use those resources as long as they continue to possess a capability to them. This model has collaborative policy definition at its core, allowing cloud users to more safely provide services to other users, and use services provided to them. We have implemented our model, and have integrated it into the popular OpenStack cloud system. Further, we have modified the existing Galaxy scientific workflow system to support our model, greatly enhancing the security guaranteed to users of the Galaxy system

Foundations and Technological Landscape of Cloud Computing

The cloud computing paradigm has brought the benefits of utility computing to a global scale. It has gained paramount attention in recent years. Companies are seriously considering to adopt this new paradigm and expecting to receive significant benefits. In fact, the concept of cloud computing is not a revolution in terms of technology; it has been established based on the solid ground of virtualization, distributed system, and web services. To comprehend cloud computing, its foundations and technological landscape need to be adequately understood. This paper provides a comprehensive review on the building blocks of cloud computing and relevant technological aspects. It focuses on four key areas including architecture, virtualization, data management, and security issues

Computational Methods for Medical and Cyber Security

Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields