Cloud Computing with e-Science Applications

Abstract

This chapter discusses the challenges that are imposed by Big Data on the modern and future e-Scientific Data Infrastructure. The chapter discusses a nature and definition of Big Data that include such characteristics as Volume, Velocity, Variety, Value and Veracity. The chapter refers to different scientific communities to define requirements on data management, access control and security. The chapter introduces the Scientific Data Lifecycle Management (SDLM) model that includes all the major stages and reflects specifics in data management in modern e-Science. The chapter proposes the generic SDI architecture model that provides a basis for building interoperable data or project centric SDI using modern technologies and best practices.The chapter discusses how the proposed models SDLM and SDI can be naturally implemented using modern cloud based infrastructure services, analyses security and trust issues in cloud based infrastructure and summaries requirements to access control and access control infrastructure that should allow secure and trusted operation and use of SDI.

BlockFlow: uma arquitetura baseada em Blockchain para confiança em Workflows científicos colaborativos apoiados por uma plataforma de ecossistema de software

“…Para a comunidade científica, o uso do ambiente de computação em nuvem pode ter vantagens e ser atraente de diferentes maneiras. Na literatura, existem vários autores que discutem as vantagens e benefícios do uso da computação em nuvem para executar experimentos científicos KHAN et al, 2019;TERZO;MOSSUCCA, 2017;ZHAO et al, 2011). Com características de alta disponibilidade, mecanismos de tolerância a falhas e principalmente elasticidade (capacidade de provisionamento, de fornecer recursos de hardware e software sob demanda), os ambientes em nuvem tem sido cada vez mais adotados.…”