Distributed infrastructure based on both cloud computing and Software-Defined Networking (SDN) technologies can provide on-demand computation, storage and connectivity resources to provision advanced cloud services. The pervasive diffusion of edge devices and components (sensors, personal handheld and wearable devices) is leading to a fundamental shift from the traditionally centralized cloud model to a more decentralized one. This will be realized through the fog computing paradigm, which executes remote workloads closer to the user, where data is ultimately produced, elaborated and consumed. Fog computing is considered a key enabler for innovative scenarios such as smart cities and smart agriculture, Industry 4.0 and the fifth generation of mobile networks (5G). However, in the early evolution of such novel technologies their robustness and security are seldom addressed adequately.
In this context, the RiSING research unit aims to study intrinsic properties of fog computing systems and propose, design and implement methods and algorithms that guarantee their security and resiliency, while taking into due account the overall system efficiency (in terms of e.g. resource utilisation or power consumption). RiSING R&D has a dual perspective and concerns both:
· Decentralised Infrastructure, where concepts of anti-fragility achieved through autonomic failure-tolerance, robustness to misconfigurations, protection from and response to attacks are introduced into both the edge and the core fog nodes. Advanced network security mechanisms relying on data plane monitoring, multi-layer encryption and unhackable communications based on quantum technologies are all being investigated.
· Application and Services, where advanced pricing models connect infrastructure to services and a tailored orchestration of security network functions can secure each service while catering to multiple application’s needs.
RiSING researchers have expertise in modelling complex processes and systems by means of instruments such as graph theory, game theory, learning algorithms, discrete and continuous optimization, etc. Research ideas are advanced to innovation by means of research assets based on widely-adopted open-source frameworks such as OpenStack, Kubernetes, ONOS and OpenDaylight.