10,372 research outputs found

    Big Data Caching for Networking: Moving from Cloud to Edge

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    In order to cope with the relentless data tsunami in 5G5G wireless networks, current approaches such as acquiring new spectrum, deploying more base stations (BSs) and increasing nodes in mobile packet core networks are becoming ineffective in terms of scalability, cost and flexibility. In this regard, context-aware 55G networks with edge/cloud computing and exploitation of \emph{big data} analytics can yield significant gains to mobile operators. In this article, proactive content caching in 55G wireless networks is investigated in which a big data-enabled architecture is proposed. In this practical architecture, vast amount of data is harnessed for content popularity estimation and strategic contents are cached at the BSs to achieve higher users' satisfaction and backhaul offloading. To validate the proposed solution, we consider a real-world case study where several hours of mobile data traffic is collected from a major telecom operator in Turkey and a big data-enabled analysis is carried out leveraging tools from machine learning. Based on the available information and storage capacity, numerical studies show that several gains are achieved both in terms of users' satisfaction and backhaul offloading. For example, in the case of 1616 BSs with 30%30\% of content ratings and 1313 Gbyte of storage size (78%78\% of total library size), proactive caching yields 100%100\% of users' satisfaction and offloads 98%98\% of the backhaul.Comment: accepted for publication in IEEE Communications Magazine, Special Issue on Communications, Caching, and Computing for Content-Centric Mobile Network

    Data Provenance and Management in Radio Astronomy: A Stream Computing Approach

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    New approaches for data provenance and data management (DPDM) are required for mega science projects like the Square Kilometer Array, characterized by extremely large data volume and intense data rates, therefore demanding innovative and highly efficient computational paradigms. In this context, we explore a stream-computing approach with the emphasis on the use of accelerators. In particular, we make use of a new generation of high performance stream-based parallelization middleware known as InfoSphere Streams. Its viability for managing and ensuring interoperability and integrity of signal processing data pipelines is demonstrated in radio astronomy. IBM InfoSphere Streams embraces the stream-computing paradigm. It is a shift from conventional data mining techniques (involving analysis of existing data from databases) towards real-time analytic processing. We discuss using InfoSphere Streams for effective DPDM in radio astronomy and propose a way in which InfoSphere Streams can be utilized for large antennae arrays. We present a case-study: the InfoSphere Streams implementation of an autocorrelating spectrometer, and using this example we discuss the advantages of the stream-computing approach and the utilization of hardware accelerators

    Dual refractive index and viscosity sensing using polymeric nanofibers optical structures

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    Porous materials have demonstrated to be ideal candidates for the creation of optical sensors with very high sensitivities. This is due both to the possibility of infiltrating the target substances into them and to their notable surface-to-volume ratio that provides a larger biosensing area. Among porous structures, polymeric nanofibers (NFs) layers fabricated by electrospinning have emerged as a very promising alternative for the creation of low-cost and easy-to-produce high performance optical sensors, for example, based on Fabry-Perot (FP) interferometers. However, the sensing performance of these polymeric NFs sensors is limited by the low refractive index contrast between the NFs porous structure and the target medium when performing in-liquid sensing experiments, which determines a very low amplitude of the FP interference fringes appearing in the spectrum. This problem has been solved with the deposition of a thin metal layer (∼ 3 nm) over the NFs sensing layer. We have successfully used these metal-coated FP NFs sensors to perform several real-time and in-flow refractive index sensing experiments. From these sensing experiments, we have also determined that the sponge-like structure of the NFs layer suffers an expansion/compression process that is dependent of the viscosity of the analyzed sample, what thus gives the possibility to perform a simultaneous dual sensing of refractive index and viscosity of a fluid

    Towards a big data reference architecture

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