Cloud Computing

AI-driven workflows for implementing composite serverless applications

The objective of the thesis focus on the design and implementation of algorithmic models regarding machine and deep machine learning (ML / DL) in order to determine the workflow of applications developed in serverless environments. Recommended tools: Apache OpenWhisk (OW composer, OW action conductors), OpenFaas, AWS lambda, Google (commercial product) Cloud functions (commercial product).

Required Skills: Python, Machine Learning, Cloud Platforms

Distributed ML/DL training using FaaS

The objective of the thesis focus on the re-design of the topology and implementation of distributed training regarding existing machine and deep learning machine models, by exploiting the Function-as-a-Service (FaaS) model. Recommended tools: Apache OpenWhisk OpenFaas, AWS lambda, Google Cloud functions. Additionally, it is advisable to perform a comparative analysis among different cloud platform tools to assess the performance of training in terms of accuracy and time.

Required Skills: Python, Machine Learning, Cloud Platforms

Monitoring engine for cloud applications

The goal of the thesis consist of analysing the monitoring requirement of a cloud application, select the most suitable monitoring engine and evaluate the performance of the monitoring in term of data collection amount and freshness.

Required Skills: Python, Java, Docker

Comparative studies for quality of service for microservice

This thesis is about the compative study of different method for evaluating the compliance of the application’w owner expectations and real application performance.

Required Skills: Java, Python, Go, Docker

Automatic distributed microservice life-cycle controller

This thesis consist of building a microservice controller capable of managing the life-cycle of a distributed application. By life-cyle we mean the deployment, scaling and application snapshot capturing.

Required Skills: Python, Kubernetes, Docker

Cloud application performance analysis in a edge environment

This study consist of capturing different performance indicators of an edge Application and establishing correlation between them for discovering different application’s dependency on hardware and the architecture.

Required Skills: Python, Docker, Machine Learning

Automatic application deployment form and hardware accelerator selection

This thesis is about the establishment of a model enabling the automatic selection of the deployment form (docker, serverless, vm) and the most suitable hardware accelerator (GPU, FPGA) of an application in cloud/edge environment.

Required Skills: Python, Java, Docker, Serverless, Machine learning