Accelerating Big Data Analytics using Heterogeneous Resources

January 13, 2022 – /,

Motivation

Internet of Things describes a new world of heterogeneous objects such as sensors, smartphones, actuators etc, which intelligently interacts and communicate with each other and offer a better quality of life. With the increase of these objects, the data volume is growing as well. Processing this volume of data can be helpful to various application domains to reveal complex patterns, gain fast insights or react to observed situations, e.g., fraud detection, smart factories, telecommunication, etc. However, it becomes extremely challenging to continuously process this high volume of data in a timely manner and meet these applications' demands. One of the possible ways can be to use heterogeneous resources' capability such as GPUs, FPGAs, etc. to accelerate the big data analytics instead of relying on homogeneous resources, e.g., CPU.

Task

This thesis is open for both bachelor's and master's student and scope can be adapted accordingly.

  • Analyze existing related literature.
  • Formalizing the problem and modeling the solution and
  • Setting up a development environment and implementing the proof of concept or prototype.
  • Evaluation of the proposed solution in comparison to the existing approaches.

Prerequisites

There are no hard guidelines for this topic. However, it is preferable if you have:

  • Good programming skills in Java and/or Scala.
  • Understanding of the concept of communication networks and big data processing engines, e.g., Apache Flink, Kafka.

Supervisor

Pratyush Agnihotri, Boris Koldehofe

Contact

I am looking for motivated students interested in working on cutting-edge technologies and IoT applications area. If you are interested in writing a Bachelor's or Master's Thesis or gaining experience in MAKI project as HiWi, please feel free to contact me at Pratyush Agnihotri (pratyush.agnihotri(at)kom.tu-darmstadt.de). You can email me the following information:

  • Your CV or small text about your courses and prior experiences in this area.
  • The short note on your motivation about this topic and
  • Transcript or TUCaN grade list.

Keywords:

Research Area(s):

Tutor: Agnihotri,

Open Theses