Rodrigo da Rosa Righi

By Rodrigo da Rosa Righi and Lucas Graebin

Part of the book: Production Scheduling

By Arthur Tórgo Gómez, Antonio Gabriel Rodrigues and Rodrigo da Rosa Righi

Part of the book: Production Scheduling

By Diórgenes Eugênio da Silveira, Eduardo Souza dos Reis, Rodrigo Simon Bavaresco, Marcio Miguel Gomes, Cristiano André da Costa, Jorge Luis Victoria Barbosa, Rodolfo Stoffel Antunes, Alvaro Machado Júnior, Rodrigo Saad and Rodrigo da Rosa Righi

The growth in data generated by private and public organizations leads to several opportunities to obtain valuable knowledge. In this scenario, data science becomes pertinent to define a structured methodology to extract valuable knowledge from raw data. It encompasses a heterogeneous group of techniques that challenge the implementation of a single platform capable of incorporating all the available resources. Thus, it is necessary to formulate a data science workflow based on different tools to extract knowledge from massive datasets. In this context, high-performance computing (HPC) provides the infrastructure required to optimize the processing time of data science workflows, which become a collection of tasks that must be efficiently scheduled to provide results in acceptable time intervals. While few studies explore the use of HPC for data science tasks, in the best of our knowledge, none conducts an in-depth analysis of scheduling and load balancing on such workflows. In this context, this chapter proposes an analysis of scheduling and load balancing from the perspective of data science scenarios. It presents concepts, environments, and tools to summarize the theoretical background required to define, assign, and execute data science workflows. Furthermore, we are also presenting new trends concerning the intersection of data science, scheduling, and load balance.

Part of the book: Scheduling Problems

By Rodrigo da Rosa Righi, Bárbara Canali Locatelli Bellini, Fernanda Fritsch, Vinicius Facco Rodrigues, Madhusudan Singh and Marcelo Pasin

Infrastructures based on fog computing are gaining popularity as an alternative to provide low-latency communication on executing distributed services. With cloud resources, it is possible to assemble an architectub re with resources close to data providers and those with more processing capacity, achieved through internet links. In this context, this book chapter presents the first insight regarding fog-cloud architecture for the healthcare area. In particular, we address vital sign monitoring in sensor devices and provide intelligent health services that reside both in the fog and the cloud to benefit the end-users and the public government. The preliminary results show the advantages of combining fog and cloud and critical applications and highlight some points of attention to address system scalability and quality of service.

Part of the book: Ubiquitous and Pervasive Computing