Nowadays, Cloud Computing infrastructures are being challenged by increasing demand for evolved cloud services characterized by heterogeneous performance requirements including real-time, data- intensive, and highly dynamic workloads. The classical way to deal with dynamism is to scale computing and network resources horizontally.

However, these techniques can be way more effective when coupled with mechanisms ensuring efficient and predictable execution of software components in distributed, shared & multi-tenant infrastructures. Such mechanisms may span across the multitude of layers or planes characterizing a cloud infrastructure: ensuring temporal isolation at the OS kernel and/or hypervisor level intelligent, QoS-aware mechanisms for VM placement and migration preventing unstable networking performance by avoiding cross-talks and resources saturation through appropriate QoS-aware management of the network and applications’ data flows design scalable data stores with guarantees on the end-to-end performance and latency, to support soft real-time workloads in cloud infrastructures. The most relevant investigated topics are the following:

• Real-time cloud computing;
• Real-time access to NoSQL data stores for predictable cloud services;
• Operating systems for massively parallel & distributed systems;
• Adaptive resource management and optimization;
• Open-source real-time operating systems.

Key Recent Projects

• On-going: AI for NFV, Vodafone, Industrial, 2018-2022
• On-going: Real-Time Cloud Computing, Ericsson, Industrial, since 2022
• Concluded: SCHED_DEADLINE for real-time packet processing in NFV, Industrial, 2016-2021
• Concluded: S(o)OS: Service-oriented Operating Systems, FP7, 2010-2013
• Concluded: IRMOS: Interactive Real-time Multimedia Applications on Service Oriented Infrastructures, FP7, 2008-2011