OPERA: A Low Power Approach to the Next Generation Cloud Infrastructures
The continuous evolution of information and communication technology has led to a change in the adopted computing paradigms over time. Cloud computing is an emerging paradigm in which users, depending on their specific requirements, access to a shared pool of computing resources dynamically allocated. Cloud computing represents, with respect to Grid computing, the evolutionary step towards the implementation of a ubiquitous computing service. Such paradigm leverages on the infrastructural capabilities (compute, storage, and network) of modern data centers to provide an adequate level of computational power able to satisfy users' requests. However, trying to continuously increase such capabilities comes at the cost of an increased energy consumption. Energy efficiency is, therefore, one of the major challenges that cloud providers must address. The OPERA project aims at bringing innovative solutions to increase the energy efficiency of cloud infrastructures, by leveraging on modular, high-density, heterogeneous and low power computing systems, which are able to cover the whole computing continuum. To this end, the project will design a high-density server solution in which low power processors and FPGA devices will be used to accelerate cloud workloads. High-speed optical interconnections will be used to connect the proposed server with high-performance nodes, such as OpenPOWER-based machines. Cyber-Physical Systems (CPS) represents a natural extension of cloud infrastructures since they can collect and process data locally, more specifically where they were generated. OPERA aims at researching energy efficiency of such cloud end-nodes by designing an ultra-low power computing system with reconfigurable radio frequency capabilities. The effectiveness of the whole platform will be demonstrated with key scenarios, specifically a road traffic monitoring application, the deployment of a virtual desktop infrastructure, and the deployment of a small data center on a truck.