• Cloud Computing
• Concurrency
• Database
• Distributed Database
• MongoDB
• NoSQL
• Priority
• Quality-of-Service
• Real-Time
• Scheduling

The advent of Cloud Computing and Big Data brought several changes and innovations in the landscape of database management systems.
Nowadays, a cloud-friendly storage system is required to reliably support data that is in continuous motion and of previously unthinkable magnitude, while guaranteeing high availability and optimal performance to thousands of customers.
In particular, NoSQL data-base services are taking momentum as a key technology thanks to their relaxed requirements with respect to the relational counterparts, that are not designed to scale massively on distributed systems.
Most research papers on performance of cloud storage systems propose solutions that aim to achieve high read and write throughput, while neglecting the problem of controlling latencies for specific users or queries. The latter research topic is particularly important for real-time applications, where task completion is bounded by precise timing constraints.
In this thesis a set of modifications is proposed to the popular MongoDB NoSQL data-base software, that reduces temporal interference among competing requests on a per-client/request basis.
Extensive experimentation with synthetic stress workloads demonstrates that the proposed solution is able to assure that requests with higher priorities achieve reduced and significantly more stable response times, with respect to lower priorities ones.