About me

I started my undergraduate studies in 2003 and conciliated daily work with evening classes. In the professional area, I had positions as Process Analyst, Software Developer, Software Architect and IT Manager. In 2009, I took a leading position to work with ERP SAP, infrastructure decisions, SAP integrations, and negotiation with suppliers. Among my responsibilities, I controlled deadlines and generated opportunities for process improvements.

Towards the end of my undergraduate studies, I created a distributed solution for updating batch views in a database system. I turned the sequential execution of a system into a distributed and collaborative solution that exploited the company’s idle computing resources.

Through the experience on distributed systems, I decided to explore this field during my master research further. Along with Dr. Rodrigo Righi, my advisor during my masters, I tackled challenges on mixing P2P environments with Bulk Synchronous Parallel (BSP) models. We published articles, book chapters on the subject, and registered the software in the Brazilian National Institute of Industrial Property.

Currently, I work on elastic big data streams as a Ph.D. Student at ENS-Lyon AVALON.

Research interests

Fields: 1. Big Data Analytics 2. Edge/ FOG Infrastructure 3. Operators Placement/ Scheduling 4. Auto-Parallelization/ Dynamics Graphs

Ph.D. Goal

The increasing availability of sensors and Internet-connected devices has led to an explosion on the volume, variety and velocity of data generated that requires some kind of analysis. Under several application scenarios, such as in smart cities, monitoring information from large infrastructures, and Internet of Things, continuous data streams must be processed in nearly real time. Several frameworks have been proposed for data stream processing, many of which have been deployed in cloud environments, aiming to benefit from characteristics such as elasticity. Elasticity, when properly exploited, refers to the ability of a cloud to allow a service to allocate additional resources or release idle capacity on demand. Although early efforts have been made towards making stream-processing more elastic, many issues are still not addressed. Most stream processing services follow a dataflow approach. They are Directed Acyclic Graphs of so called operators, which perform User Defined Functions, and whose placement on available resources, identification of bottlenecks, and adaption can be difficult; especially when these services are part of a larger infrastructure that comprises other types of execution models.

The research goals are to investigate architectures and resource management algorithms for attaining elastic and distributed data-stream processing. At an architectural level, the goal is to design resource management models that can exploit resources from both the edges of the Internet and traditional cloud infrastructure. From a cloud provider’s perspective, a goal is to investigate algorithms and mechanisms that provide elasticity to distributed stream processing services and other big-data applications that perform periodical analyses. Such algorithms aim to reduce fixed and variable costs such as with electricity whilst respecting QoS metrics.

Approaches

  • Series-Parallel Decomposable Graph
  • Semantics for Stream Behaviors
  • Mixed-Integer Linear Progamming (MILP)

Current Collaborations