The course aims at delivering a novel vision of systems (mainly distributed) and at building a deep, formal, practical, and meditated experience of their operations. We are immersed into those systems, personally, socially, and as part of organizations.
Infrastructures for Cloud Computing and Big Data is about what is behind those systems, and their behavior and impact, both from the user perspective but more important with the point of view of the implementers and designers. In particular we focus on the experience of operations more than in static planning and configuration, and we aim at the entire life cycle operations.
The course covers many topics:
- Advanced models for large distributed & cloud systems
- Replication, group and many-to-many communication, and systems for QoS
- Middleware for development and management of large distributed & cloud systems
- Infrastructures for global data storage and processing
The topics can be organized as follows.
Advanced models for large distributed & cloud systems
- Class Starting: general information and presentation
- Goals, Basics, and Models: classifications, service and cloud models, parallelization models
- Middleware & Cloud Models: definitions, categories, basic organization, and patterns for large distributed and cloud systems
Replication, group and many-to-many communication, and systems for QoS
- Replication: models, strategies and protocols
- Communication and groups: models, protocols and algorithms
- Systems and protocols for QoS
- Multicast and MOM middleware
Middleware for large distributed & cloud systems
- CORBA: middleware and operating environment
- OpenStack: an example of a widely-diffused cloud IaaS
Infrastructures for global data storage and processing
- Global data storage: solutions for long-term and short-term data memorization
- Global data processing: batching and streaming based big data processing in the cloud