Antonio Pescape

Bio: Antonio Pescape is an academic researcher from University of Naples Federico II. The author has contributed to research in topics: The Internet & Traffic classification. The author has an hindex of 44, co-authored 225 publications receiving 9261 citations. Previous affiliations of Antonio Pescape include University of Missouri & Information Technology University.

On the Integration of Cloud Computing and Internet of Things

Abstract: Cloud computing and Internet of Things (IoT), two very different technologies, are both already part of our life. Their massive adoption and use is expected to increase further, making them important components of the Future Internet. A novel paradigm where Cloud and IoT are merged together is foreseen as disruptive and an enabler of a large number of application scenarios. In this paper we focus our attention on the integration of Cloud and IoT, which we call the CloudIoT paradigm. Many works in literature have surveyed Cloud and IoT separately: their main properties, features, underlying technologies, and open issues. However, to the best of our knowledge, these works lack a detailed analysis of the CloudIoT paradigm. To bridge this gap, in this paper we review the literature about the integration of Cloud and IoT. We start analyzing and discussing the need for integrating them, the challenges deriving from such integration, and how these issues have been tackled in literature. We then describe application scenarios that have been presented in literature, as well as platforms--both commercial and open source--and projects implementing the CloudIoT paradigm. Finally, we identify open issues, main challenges and future directions in this promising field.

Issues and future directions in traffic classification

Abstract: Traffic classification technology has increased in relevance this decade, as it is now used in the definition and implementation of mechanisms for service differentiation, network design and engineering, security, accounting, advertising, and research. Over the past 10 years the research community and the networking industry have investigated, proposed and developed several classification approaches. While traffic classification techniques are improving in accuracy and efficiency, the continued proliferation of different Internet application behaviors, in addition to growing incentives to disguise some applications to avoid filtering or blocking, are among the reasons that traffic classification remains one of many open problems in Internet research. In this article we review recent achievements and discuss future directions in traffic classification, along with their trade-offs in applicability, reliability, and privacy. We outline the persistently unsolved challenges in the field over the last decade, and suggest several strategies for tackling these challenges to promote progress in the science of Internet traffic classification.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Table Of Integrals Series And Products

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