Bibhudatta Sahoo

Es wurden zahlreiche Technologien und Modelle vorgeschlagen, um Big-Data-Dienste in die Cloud-Umgebung zu bringen. Die Wahl dieser Technologien ist jedoch immer anwendungsspezifisch, und die Bestimmung der Eignung einer L sung ist eine verwirrende Aufgabe, die es f r Architekten und Designer erforderlich macht, die Anforderungen zu berpr fen und eine derzeit auf dem Markt verf gbare L sung auszuw hlen. Dieses Buch konzentriert sich auf die Untersuchung von Servicemodellen f r Big-Data-Dienste in der Cloud und ihre Klassifizierung auf der Grundlage der drei Cloud-Modelle (d. h. IaaS, PaaS & SaaS). Die Herausforderungen, die diese Modelle mit sich bringen, wurden kombiniert und danach kategorisiert, wer sich um die Herausforderungen k mmert. In diesem Text werden "Big Data Service Model for the Cloud", "Storage Space Estimation for Servicing Nodes" und "Replica Server Placement on Multiple Data Center" f r Big Data-Anwendungen vorgestellt.

Foram propostas in meras tecnologias e modelos para levar os servi os de grandes volumes de dados para o ambiente de nuvem. No entanto, a escolha destas tecnologias sempre espec fica da aplica o e determinar a adequa o de uma solu o uma tarefa desconcertante que torna necess rio que os arquitectos e os designers revejam os requisitos e escolham uma solu o atualmente dispon vel no mercado. Este livro centra-se no estudo do modelo de servi o para servi os de Big Data na Nuvem e na sua classifica o com base nos tr s modelos de Nuvem (ou seja, IaaS, PaaS, & SaaS). Os desafios trazidos por estes modelos foram combinados e categorizados em fun o de quem trata dos desafios. Este texto apresenta o "Modelo de servi o de Big Data para a Nuvem", a "Estimativa de espa o de armazenamento para n s de servi o" e a "Coloca o de servidores de r plica em v rios centros de dados" para aplica es de Big Data

Internet of Things (IoT) is a technique to connect each and everything to the Internet, and it provides anything at any time regardless of irrespective of the location of the thing. As we are moving towards the IoT, the number of sensors deployed around the world is also growing at a rapid pace and an accelerated growth is expected in future. An increasing number of IoT middleware solutions are being developed in both research as well as commercial environments. However, sensor search and selection still remains a critical requirement and a challenge owing to its diversity and heterogeneity. This text presents a new architecture for fog computing along with mathematical modeling and logical design to achieve the minimized latency. Various methods have been introduced so as to carefully choose the fog node that helps in reducing the overall delay and generating quick responses. Different methods have been proposed to carry out a low latency and energy efficient sensor selection process for IoT services in the fog environment associated with any application presented in the form of flow based process(FBP).

Today’s advancements in technology have brought about a new era of speed and simplicity for consumers and businesses. Due to these new benefits, the possibilities of universal connectivity, storage and computation are made tangible, thus leading the way to new Internet-of Things solutions.Resource Management and Efficiency in Cloud Computing Environments is an authoritative reference source for the latest scholarly research on the emerging trends of cloud computing and reveals the benefits cloud paths provide to consumers. Featuring coverage across a range of relevant perspectives and topics, such as big data, cloud security, and utility computing, this publication is an essential source for researchers, students and professionals seeking current research on the organization and productivity of cloud computing environments.Topics Covered:Big DataCloud Application Services (SaaS)Cloud SecurityHybrid CloudInternet of Things (IoT)Private CloudPublic CloudService Oriented Architecture (SOA)Utility ComputingVirtualization Technology